# What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
Migrate Trainer from `Repository` to `upload_folder`. # What does this PR do?

This PR migrates the internal of the push to Hub strategy inside the Trainer to the upload methods instead of the old git approach. The goal is to benefit from the improvements in terms of speed (for instance `upload_folder` is faster than `Repository` on Colab) and avoid some weird sync errors we have seen happen in the past.

This migration breaks a few things. I'm listing them for the sake of completeness but I don't think those are important breaking changes.
1. the return of `Trainer.push_to_hub` will change: in the blocking case it's the url of the repo instead of the URL of the commit. In the non-blocking case, it's the Future object returned by the `huggingface_hub` libreary (instead of a tuple commit address, job in progress)
2. before the migration, it was instant to cancel the push in progress when at the end of the training, to then push the final version of the Hub (but we also had some weird bugs). After the migration we will have to wait for the last push to be completed before pusing the final version of the model.
3. the `output_dir` of the `Trainer` won't be a git repository which is an exact clone of the model repo anymore.

Note that I chose to push checkpoint jobs in two `upload_folder`: one for the model and one for the optimizer state (roughly). This way I can cancel the second one if it hasn't started when at the end of training.
# coding=utf-8
# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Callable, Optional, Tuple

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from ...modeling_flax_outputs import (
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxBaseModelOutputWithPooling,
    FlaxBaseModelOutputWithPoolingAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_call_sample_docstring,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_big_bird import BigBirdConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "google/bigbird-roberta-base"
_CONFIG_FOR_DOC = "BigBirdConfig"

remat = nn_partitioning.remat


@flax.struct.dataclass
class FlaxBigBirdForPreTrainingOutput(ModelOutput):
    """
    Output type of [`BigBirdForPreTraining`].

    Args:
        prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        seq_relationship_logits (`jnp.ndarray` of shape `(batch_size, 2)`):
            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
            before SoftMax).
        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    prediction_logits: jnp.ndarray = None
    seq_relationship_logits: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


@flax.struct.dataclass
class FlaxBigBirdForQuestionAnsweringModelOutput(ModelOutput):
    """
    Base class for outputs of question answering models.

    Args:
        start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Span-start scores (before SoftMax).
        end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Span-end scores (before SoftMax).
        pooled_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`):
            pooled_output returned by FlaxBigBirdModel.
        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    start_logits: jnp.ndarray = None
    end_logits: jnp.ndarray = None
    pooled_output: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


BIG_BIRD_START_DOCSTRING = r"""

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        config ([`BigBirdConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).

            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.

            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.**

            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""

BIG_BIRD_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        head_mask (`numpy.ndarray` of shape `({0})`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

"""


class FlaxBigBirdEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.setup
    def setup(self):
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.position_embeddings = nn.Embed(
            self.config.max_position_embeddings,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.token_type_embeddings = nn.Embed(
            self.config.type_vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
        # Embed
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        position_embeds = self.position_embeddings(position_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))

        if self.config.rescale_embeddings:
            inputs_embeds *= self.config.hidden_size**0.5

        # Sum all embeddings
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds

        # Layer Norm
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->BigBird
class FlaxBigBirdSelfAttention(nn.Module):
    config: BigBirdConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
        if self.config.hidden_size % self.config.num_attention_heads != 0:
            raise ValueError(
                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
                "                   : {self.config.num_attention_heads}"
            )

        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
            )

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))

    @nn.compact
    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states: Optional[jnp.array] = None,
        init_cache: bool = False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        # get query proj
        query_states = self.query(hidden_states)
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self.key(key_value_states)
            value_states = self.value(key_value_states)
        else:
            # self_attention
            key_states = self.key(hidden_states)
            value_states = self.value(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # handle cache prepare causal attention mask
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        # combine masks if needed
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_probs_dropout_prob,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        # Mask heads if we want to
        if layer_head_mask is not None:
            attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs


class FlaxBigBirdBlockSparseAttention(nn.Module):
    config: BigBirdConfig
    block_sparse_seed: int = None
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            use_bias=self.config.use_bias,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            use_bias=self.config.use_bias,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            use_bias=self.config.use_bias,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

    @staticmethod
    def transpose_for_scores(x, n_heads, head_size):
        new_x_shape = x.shape[:-1] + (n_heads, head_size)
        x = x.reshape(*new_x_shape)
        return jnp.transpose(x, axes=(0, 2, 1, 3))

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic=True,
        output_attentions=False,
    ):
        n_heads = self.config.num_attention_heads
        head_size = self.config.hidden_size // n_heads

        blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
            attention_mask, self.config.block_size
        )

        query_layer = self.transpose_for_scores(self.query(hidden_states), n_heads, head_size)
        key_layer = self.transpose_for_scores(self.key(hidden_states), n_heads, head_size)
        value_layer = self.transpose_for_scores(self.value(hidden_states), n_heads, head_size)

        indices_prng_key = None
        if not deterministic:
            indices_prng_key = self.make_rng("indices")

        attn_output, attn_weights = self.bigbird_block_sparse_attention(
            query_layer,
            key_layer,
            value_layer,
            band_mask,
            from_mask,
            to_mask,
            blocked_encoder_mask,
            blocked_encoder_mask,
            n_heads,
            head_size,
            indices_prng_key=indices_prng_key,
            deterministic=deterministic,
            plan_from_length=None,
            plan_num_rand_blocks=None,
            output_attentions=output_attentions,
        )

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs

    @staticmethod
    def create_masks_for_block_sparse_attn(attention_mask, block_size: int):
        batch_size, seq_length = attention_mask.shape
        if seq_length % block_size != 0:
            raise ValueError(
                f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block"
                f" size is {block_size}."
            )

        def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
            """
            Create 3D attention mask from a 2D tensor mask.

            Args:
                from_blocked_mask: 2D Tensor of shape [batch_size,
                from_seq_length//from_block_size, from_block_size].
                to_blocked_mask: int32 Tensor of shape [batch_size,
                to_seq_length//to_block_size, to_block_size].

            Returns:
                float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
                3*to_block_size].
            """
            exp_blocked_to_pad = jnp.concatenate(
                [to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], axis=2
            )
            band_mask = jnp.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
            band_mask = jnp.expand_dims(band_mask, 1)
            return band_mask

        blocked_encoder_mask = attention_mask.reshape(batch_size, seq_length // block_size, block_size)
        band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)

        from_mask = attention_mask.reshape(batch_size, 1, seq_length, 1)
        to_mask = attention_mask.reshape(batch_size, 1, 1, seq_length)

        return blocked_encoder_mask, band_mask, from_mask, to_mask

    def bigbird_block_sparse_attention(
        self,
        query_layer,
        key_layer,
        value_layer,
        band_mask,
        from_mask,
        to_mask,
        from_blocked_mask,
        to_blocked_mask,
        n_heads,
        head_size,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        deterministic: Optional[bool] = True,
        plan_from_length=None,
        plan_num_rand_blocks=None,
        output_attentions=None,
    ):
        # BigBird block-sparse attention as suggested in paper

        # ITC:
        #     global tokens: 2 x block_size
        #     window tokens: 3 x block_size
        #     random tokens: num_rand_tokens x block_size

        # ETC:
        #     global tokens: extra_globals_tokens + 2 x block_size
        #     window tokens: 3 x block_size
        #     random tokens: num_rand_tokens x block_size

        # Note:
        #     1) Currently, ETC is not supported.
        #     2) Window size is fixed to 3 blocks & it can be changed only by
        #     changing `block_size`.
        #     3) Number of global blocks are fixed (2 blocks here) & global tokens can be
        #     controlled only by `block_size`.

        # attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of
        # shifting tokens (for calculating sliding attention). hence following code can be divided into 5 parts.

        bsz, _, from_seq_len, _ = query_layer.shape
        to_seq_len = key_layer.shape[2]
        from_block_size = to_block_size = self.config.block_size

        if from_seq_len % from_block_size != 0:
            raise ValueError("Query sided sequence length must be multiple of block size")

        if to_seq_len % to_block_size != 0:
            raise ValueError("Key/Value sided sequence length must be multiple of block size")

        if from_seq_len // from_block_size != to_seq_len // to_block_size:
            raise ValueError("Error the number of blocks needs to be same!")

        n_rand_blocks = self.config.num_random_blocks
        rsqrt_d = 1 / jnp.sqrt(head_size)
        attn_mask_penalty = -10000.0

        if from_seq_len in [1024, 3072, 4096]:  # old plans used in paper
            max_seqlen = self.config.max_position_embeddings
            rand_attn = [
                self._bigbird_block_rand_mask(
                    max_seqlen,
                    max_seqlen,
                    from_block_size,
                    to_block_size,
                    n_rand_blocks,
                    indices_prng_key=indices_prng_key,
                    deterministic=deterministic,
                    last_idx=1024,
                )[: (from_seq_len // from_block_size - 2)]
                for _ in range(n_heads)
            ]
        else:
            if plan_from_length is None:
                plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
                    from_seq_len, from_block_size, n_rand_blocks
                )
            rand_attn = self._bigbird_block_rand_mask_with_head(
                from_seq_length=from_seq_len,
                to_seq_length=to_seq_len,
                from_block_size=from_block_size,
                to_block_size=to_block_size,
                num_heads=n_heads,
                plan_from_length=plan_from_length,
                plan_num_rand_blocks=plan_num_rand_blocks,
                indices_prng_key=indices_prng_key,
            )

        rand_attn = jnp.stack(rand_attn, axis=0)
        rand_attn = jnp.broadcast_to(rand_attn, (bsz,) + rand_attn.shape)

        rand_mask = self._create_rand_mask_from_inputs(
            from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size
        )

        blocked_query_matrix = query_layer.reshape(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
        blocked_key_matrix = key_layer.reshape(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
        blocked_value_matrix = value_layer.reshape(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)

        shape = (bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1)
        gathered_key = self.jax_gather(blocked_key_matrix, rand_attn, batch_dims=2).reshape(*shape)
        gathered_value = self.jax_gather(blocked_value_matrix, rand_attn, batch_dims=2).reshape(*shape)

        # 1st PART
        # 1st block (global block) attention scores
        # q[0] x (k[0], k[1], k[2], k[3], k[4] .... )

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
        first_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, 0], key_layer)

        first_product = first_product * rsqrt_d
        first_product += (1.0 - to_mask) * attn_mask_penalty
        first_attn_weights = jax.nn.softmax(first_product, axis=-1)  # [bsz, n_heads, from_block_size, to_seq_len]

        # [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
        first_context_layer = jnp.einsum("bhqk,bhkd->bhqd", first_attn_weights, value_layer)
        first_context_layer = jnp.expand_dims(first_context_layer, 2)

        # 2nd PART
        # 2nd block attention scores
        # q[1] x (sliding_keys, random_keys, global_keys)
        # sliding key blocks -> 2nd, 3rd blocks
        # global key blocks -> 1st block

        second_key_mat = jnp.concatenate(
            [
                blocked_key_matrix[:, :, 0],
                blocked_key_matrix[:, :, 1],
                blocked_key_matrix[:, :, 2],
                blocked_key_matrix[:, :, -1],
                gathered_key[:, :, 0],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        second_value_mat = jnp.concatenate(
            [
                blocked_value_matrix[:, :, 0],
                blocked_value_matrix[:, :, 1],
                blocked_value_matrix[:, :, 2],
                blocked_value_matrix[:, :, -1],
                gathered_value[:, :, 0],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        # ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
        second_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, 1], second_key_mat)
        second_seq_pad = jnp.concatenate(
            [
                to_mask[:, :, :, : 3 * to_block_size],
                to_mask[:, :, :, -to_block_size:],
                jnp.ones([bsz, 1, 1, n_rand_blocks * to_block_size], dtype=to_mask.dtype),
            ],
            axis=3,
        )
        second_rand_pad = jnp.concatenate(
            [
                jnp.ones([bsz, n_heads, from_block_size, 4 * to_block_size], dtype=rand_mask.dtype),
                rand_mask[:, :, 0],
            ],
            axis=3,
        )
        second_product = second_product * rsqrt_d
        second_product += (1.0 - jnp.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
        second_attn_weights = jax.nn.softmax(
            second_product, axis=-1
        )  # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]

        # [bsz, n_heads, from_block_size, (4+r)*to_block_size] x [bsz, n_heads, (4+r)*to_block_size, -1]
        #  ==> [bsz, n_heads, from_block_size, -1]
        second_context_layer = jnp.einsum("bhqk,bhkd->bhqd", second_attn_weights, second_value_mat)
        second_context_layer = jnp.expand_dims(second_context_layer, 2)

        # 3rd PART
        # Middle blocks attention scores
        # q[-2:2] x (sliding_keys, random_keys, global_keys)
        # sliding attn is calculated using special trick of shifting tokens as discussed in paper
        # random keys are generated by taking random indices as per `rand_attn`
        # global keys -> 1st & last block

        exp_blocked_key_matrix = jnp.concatenate(
            [blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], axis=3
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        exp_blocked_value_matrix = jnp.concatenate(
            [blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]],
            axis=3,
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        middle_query_matrix = blocked_query_matrix[:, :, 2:-2]

        # sliding attention scores for q[-2:2]
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        inner_band_product = jnp.einsum("bhlqd,bhlkd->bhlqk", middle_query_matrix, exp_blocked_key_matrix)
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size]
        inner_band_product = inner_band_product * rsqrt_d

        # randn attention scores for q[-2:2]
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
        # x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
        rand_band_product = jnp.einsum("bhlqd,bhlkd->bhlqk", middle_query_matrix, gathered_key[:, :, 1:-1])
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
        rand_band_product = rand_band_product * rsqrt_d

        # Including 1st block (since it's global)
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1]
        #  ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
        first_band_product = jnp.einsum("bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0])
        first_band_product = first_band_product * rsqrt_d

        # Including last block (since it's global)
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1]
        #  ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
        last_band_product = jnp.einsum("bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1])
        last_band_product = last_band_product * rsqrt_d

        # masking padded tokens
        inner_band_product += (1.0 - band_mask) * attn_mask_penalty
        first_band_product += (1.0 - jnp.expand_dims(to_mask[:, :, :, :to_block_size], 3)) * attn_mask_penalty
        last_band_product += (1.0 - jnp.expand_dims(to_mask[:, :, :, -to_block_size:], 3)) * attn_mask_penalty
        rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty

        # completing attention scores matrix for all q[-2:2]
        band_product = jnp.concatenate(
            [first_band_product, inner_band_product, rand_band_product, last_band_product], axis=-1
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]

        # safely doing softmax since attention matrix is completed
        attn_weights = jax.nn.softmax(
            band_product, axis=-1
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]

        # contribution of sliding keys
        # [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size]
        # x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        context_layer = jnp.einsum(
            "bhlqk,bhlkd->bhlqd", attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix
        )
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]

        # adding contribution of random keys
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
        # x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
        context_layer += jnp.einsum(
            "bhlqk,bhlkd->bhlqd",
            attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size],
            gathered_value[:, :, 1:-1],
        )
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]

        # adding contribution of global keys
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1]
        #  ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
        context_layer += jnp.einsum(
            "bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0]
        )
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1]
        # ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
        context_layer += jnp.einsum(
            "bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1]
        )

        # 4th PART
        # last 2nd token attention scores
        # q[-2] x (sliding_keys, random_keys, global_keys)
        # sliding key blocks -> last 3 blocks
        # global key block -> 1st block
        # random key block -> based on indices stored in `randn_attn`

        second_last_key_mat = jnp.concatenate(
            [
                blocked_key_matrix[:, :, 0],
                blocked_key_matrix[:, :, -3],
                blocked_key_matrix[:, :, -2],
                blocked_key_matrix[:, :, -1],
                gathered_key[:, :, -1],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1]
        second_last_value_mat = jnp.concatenate(
            [
                blocked_value_matrix[:, :, 0],
                blocked_value_matrix[:, :, -3],
                blocked_value_matrix[:, :, -2],
                blocked_value_matrix[:, :, -1],
                gathered_value[:, :, -1],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+r)*to_block_size, -1]

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        # ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
        second_last_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, -2], second_last_key_mat)
        second_last_seq_pad = jnp.concatenate(
            [
                to_mask[:, :, :, :to_block_size],
                to_mask[:, :, :, -3 * to_block_size :],
                jnp.ones([bsz, 1, 1, n_rand_blocks * to_block_size], dtype=to_mask.dtype),
            ],
            axis=3,
        )
        second_last_rand_pad = jnp.concatenate(
            [
                jnp.ones([bsz, n_heads, from_block_size, 4 * to_block_size], dtype=rand_mask.dtype),
                rand_mask[:, :, -1],
            ],
            axis=3,
        )
        second_last_product = second_last_product * rsqrt_d
        second_last_product += (1.0 - jnp.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
        second_last_attn_weights = jax.nn.softmax(
            second_last_product, axis=-1
        )  # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]

        # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        # ==> [bsz, n_heads, from_block_size, -1]
        second_last_context_layer = jnp.einsum("bhqk,bhkd->bhqd", second_last_attn_weights, second_last_value_mat)
        second_last_context_layer = jnp.expand_dims(second_last_context_layer, 2)

        # 5th PART
        # last block (global) attention scores
        # q[-1] x (k[0], k[1], k[2], k[3], .... )

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
        last_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, -1], key_layer)
        last_product = last_product * rsqrt_d
        last_product += (1.0 - to_mask) * attn_mask_penalty
        last_attn_weights = jax.nn.softmax(last_product, axis=-1)  # [bsz, n_heads, from_block_size, n]

        # [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
        last_context_layer = jnp.einsum("bhqk,bhkd->bhqd", last_attn_weights, value_layer)
        last_context_layer = jnp.expand_dims(last_context_layer, 2)

        # combining representations of all tokens
        context_layer = jnp.concatenate(
            [first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer],
            axis=2,
        )
        context_layer = context_layer.reshape(bsz, n_heads, from_seq_len, -1) * from_mask
        context_layer = jnp.transpose(context_layer, axes=(0, 2, 1, 3)).reshape(bsz, from_seq_len, -1)

        attention_probs = None

        return context_layer, attention_probs

    @staticmethod
    def jax_gather(params, indices, batch_dims=2):
        """
        Gather the indices from params correctly (equivalent to tf.gather but with modifications)

        Args:
            params: (bsz, n_heads, num_blocks, block_size, head_dim)
            indices: (<num_blocks, 1)
        """

        def _jax_gather(params, indices):
            return params[indices]

        for _ in range(batch_dims):
            _jax_gather = jax.vmap(_jax_gather, in_axes=(0, 0))

        return _jax_gather(params, indices)  # params.shape[:batch_dims] + indices.shape + params.shape[batch_dims+1:]

    def _create_rand_mask_from_inputs(
        self,
        from_blocked_mask,
        to_blocked_mask,
        broadcasted_rand_attn,
        num_attention_heads,
        num_random_blocks,
        batch_size,
        from_seq_length,
        from_block_size,
    ):
        """
        Create 3D attention mask from a 2D tensor mask.

        Args:
            from_blocked_mask: 2D Tensor of shape [batch_size, from_seq_length//from_block_size, from_block_size].
            to_blocked_mask: int32 Tensor of shape [batch_size, to_seq_length//to_block_size, to_block_size].
            broadcasted_rand_attn:
                [batch_size, num_attention_heads, from_seq_length//from_block_size-2, num_rand_blocks]
            num_attention_heads: int. Number of attention heads.
            num_random_blocks: int. Number of random chunks per row.
            batch_size: int. Batch size for computation.
            from_seq_length: int. length of from sequence.
            from_block_size: int. size of block in from sequence.

        Returns:
            float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2,
            from_block_size, num_rand_blocks*to_block_size].
        """
        num_windows = from_seq_length // from_block_size - 2
        rand_mask = self.jax_gather(to_blocked_mask, broadcasted_rand_attn, batch_dims=1)
        rand_mask = rand_mask.reshape(
            batch_size, num_attention_heads, num_windows, num_random_blocks * from_block_size
        )
        rand_mask = jnp.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
        return rand_mask

    @staticmethod
    def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
        """
        Gives the plan of where to put random attention.

        Args:
            from_seq_length: int. length of from sequence.
            from_block_size: int. size of block in from sequence.
            num_rand_blocks: int. Number of random chunks per row.

        Returns:
            plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for
            each block
        """

        plan_from_length = []
        plan_num_rand_blocks = []
        if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
            plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
            plan_num_rand_blocks.append(num_rand_blocks)
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(0)
        elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
            plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
            plan_num_rand_blocks.append(num_rand_blocks // 2)
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
        else:
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(num_rand_blocks)

        return plan_from_length, plan_num_rand_blocks

    @staticmethod
    def _bigbird_block_rand_mask(
        from_seq_length,
        to_seq_length,
        from_block_size,
        to_block_size,
        num_rand_blocks,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        deterministic: Optional[bool] = True,
        last_idx: Optional[int] = -1,
    ):
        """
        Create adjacency list of random attention.

        Args:
            from_seq_length: int. length of from sequence.
            to_seq_length: int. length of to sequence.
            from_block_size: int. size of block in from sequence.
            to_block_size: int. size of block in to sequence.
            num_rand_blocks: int. Number of random chunks per row.
            indices_prng_key: jax.random.PRNGKey. PRNG key that is used to perform random jax operations.
            deterministic: bool. When False random attention will be used.
            last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
            if positive then num_rand_blocks blocks chosen only up to last_idx.

        Returns:
            adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
        """
        # using this method when from_seq_length in [1024, 3072, 4096]

        if from_seq_length // from_block_size != to_seq_length // to_block_size:
            raise ValueError("Error the number of blocks needs to be same!")
        rand_attn = jnp.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=jnp.int32)
        # deterministic nor randomness
        if deterministic:
            return rand_attn

        middle_seq = jnp.arange(1, to_seq_length // to_block_size - 1, dtype=jnp.int32)
        last = to_seq_length // to_block_size - 1
        if last_idx > (2 * to_block_size):
            last = (last_idx // to_block_size) - 1

        r = num_rand_blocks  # shorthand
        for i in range(1, from_seq_length // from_block_size - 1):
            start = i - 2
            end = i
            if i == 1:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[2:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            elif i == 2:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[3:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            elif i == from_seq_length // from_block_size - 3:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            # Missing -3: should have been sliced till last-3
            elif i == from_seq_length // from_block_size - 2:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            # Missing -4: should have been sliced till last-4
            else:
                if start > last:
                    start = last
                    seq_values = jax.random.permutation(indices_prng_key, middle_seq[:start])[:r]
                    rand_attn = rand_attn.at[i - 1].set(seq_values)
                elif (end + 1) == last:
                    seq_values = jax.random.permutation(indices_prng_key, middle_seq[:start])[:r]
                    rand_attn = rand_attn.at[i - 1].set(seq_values)
                else:
                    concat_values = jnp.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
                    seq_values = jax.random.permutation(indices_prng_key, concat_values)[:r]
                    rand_attn = rand_attn.at[i - 1].set(seq_values)
        return rand_attn

    def _bigbird_block_rand_mask_with_head(
        self,
        from_seq_length,
        to_seq_length,
        from_block_size,
        to_block_size,
        num_heads,
        plan_from_length,
        plan_num_rand_blocks,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        deterministic: Optional[bool] = True,
        window_block_left=1,
        window_block_right=1,
        global_block_top=1,
        global_block_bottom=1,
        global_block_left=1,
        global_block_right=1,
    ):
        """
        Create adjacency list of random attention.

        Args:
            from_seq_length: int. length of from sequence.
            to_seq_length: int. length of to sequence.
            from_block_size: int. size of block in from sequence.
            to_block_size: int. size of block in to sequence.
            num_heads: int. total number of heads.
            plan_from_length: list. plan from length where num_random_blocks are choosen from.
            plan_num_rand_blocks: list. number of rand blocks within the plan.
            indices_prng_key: jax.random.PRNGKey. PRNG key that is used to perform random jax operations.
            deterministic: bool. When False random attention will be used.
            window_block_left: int. number of blocks of window to left of a block.
            window_block_right: int. number of blocks of window to right of a block.
            global_block_top: int. number of blocks at the top.
            global_block_bottom: int. number of blocks at the bottom.
            global_block_left: int. Number of blocks globally used to the left.
            global_block_right: int. Number of blocks globally used to the right.

        Returns:
            adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by
            num_rand_blocks
        """
        # using this method when from_seq_length not in [1024, 3072, 4096]

        if from_seq_length // from_block_size != to_seq_length // to_block_size:
            raise ValueError("Error the number of blocks needs to be same!")

        if from_seq_length not in plan_from_length:
            raise ValueError("Error from sequence length not in plan!")

        # Total number of blocks in the mmask
        num_blocks = from_seq_length // from_block_size
        # Number of blocks per plan
        plan_block_length = jnp.array(plan_from_length) // from_block_size
        # till when to follow plan
        max_plan_idx = plan_from_length.index(from_seq_length)

        # Random Attention adjacency list
        rand_attn = [
            jnp.zeros((num_blocks, sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=jnp.int32)
            for i in range(num_heads)
        ]

        # deterministic
        if deterministic:
            for nh in range(num_heads):
                rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
            return rand_attn

        # We will go iteratively over the plan blocks and pick random number of
        # Attention blocks from the legally allowed blocks
        for plan_idx in range(max_plan_idx + 1):
            rnd_r_cnt = 0
            if plan_idx > 0:
                # set the row for all from_blocks starting from 0 to
                # plan_block_length[plan_idx-1]
                # column indx start fromm plan_block_length[plan_idx-1] and ends at
                # plan_block_length[plan_idx]
                if plan_num_rand_blocks[plan_idx] > 0:
                    rnd_r_cnt = int(sum(plan_num_rand_blocks[:plan_idx]))
                    curr_r_cnt = int(sum(plan_num_rand_blocks[: plan_idx + 1]))
                    for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
                        for h in range(num_heads):
                            single_block_row_attention = self._get_single_block_row_attention(
                                block_id=blk_rw_idx,
                                to_start_block_id=plan_block_length[plan_idx - 1],
                                to_end_block_id=plan_block_length[plan_idx],
                                num_rand_blocks=plan_num_rand_blocks[plan_idx],
                                window_block_left=window_block_left,
                                window_block_right=window_block_right,
                                global_block_left=global_block_left,
                                global_block_right=global_block_right,
                                indices_prng_key=indices_prng_key,
                            )
                            rand_attn[h] = (
                                rand_attn[h].at[blk_rw_idx, rnd_r_cnt:curr_r_cnt].set(single_block_row_attention)
                            )

                for pl_id in range(plan_idx):
                    if plan_num_rand_blocks[pl_id] == 0:
                        continue
                    for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
                        rnd_r_cnt = 0
                        to_start_block_id = 0
                        if pl_id > 0:
                            rnd_r_cnt = int(sum(plan_num_rand_blocks[:pl_id]))
                            to_start_block_id = plan_block_length[pl_id - 1]
                        curr_r_cnt = int(sum(plan_num_rand_blocks[: pl_id + 1]))
                        for h in range(num_heads):
                            single_block_row_attention = self._get_single_block_row_attention(
                                block_id=blk_rw_idx,
                                to_start_block_id=to_start_block_id,
                                to_end_block_id=plan_block_length[pl_id],
                                num_rand_blocks=plan_num_rand_blocks[pl_id],
                                window_block_left=window_block_left,
                                window_block_right=window_block_right,
                                global_block_left=global_block_left,
                                global_block_right=global_block_right,
                                indices_prng_key=indices_prng_key,
                            )
                            rand_attn[h] = (
                                rand_attn[h].at[blk_rw_idx, rnd_r_cnt:curr_r_cnt].set(single_block_row_attention)
                            )

            if plan_num_rand_blocks[plan_idx] == 0:
                continue
            curr_r_cnt = int(sum(plan_num_rand_blocks[: plan_idx + 1]))
            from_start_block_id = global_block_top
            to_start_block_id = 0
            if plan_idx > 0:
                rnd_r_cnt = int(sum(plan_num_rand_blocks[:plan_idx]))
                from_start_block_id = plan_block_length[plan_idx - 1]
                to_start_block_id = plan_block_length[plan_idx - 1]
            for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
                for h in range(num_heads):
                    single_block_row_attention = self._get_single_block_row_attention(
                        block_id=blk_rw_idx,
                        to_start_block_id=to_start_block_id,
                        to_end_block_id=plan_block_length[plan_idx],
                        num_rand_blocks=plan_num_rand_blocks[plan_idx],
                        window_block_left=window_block_left,
                        window_block_right=window_block_right,
                        global_block_left=global_block_left,
                        global_block_right=global_block_right,
                        indices_prng_key=indices_prng_key,
                    )
                    rand_attn[h] = rand_attn[h].at[blk_rw_idx, rnd_r_cnt:curr_r_cnt].set(single_block_row_attention)

        for nh in range(num_heads):
            rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
        return rand_attn

    @staticmethod
    def _get_single_block_row_attention(
        block_id,
        to_start_block_id,
        to_end_block_id,
        num_rand_blocks,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        window_block_left=1,
        window_block_right=1,
        global_block_left=1,
        global_block_right=1,
    ):
        """
        For a single row block get random row attention.

        Args:
            block_id: int. block id of row.
            to_start_block_id: int. random attention column start id.
            to_end_block_id: int. random attention column end id.
            num_rand_blocks: int. number of random blocks to be selected.
            indices_prng_key: jax.random.PRNGKey. PRNG key that is used to perform random jax operations
            window_block_left: int. number of blocks of window to left of a block.
            window_block_right: int. number of blocks of window to right of a block.
            global_block_left: int. Number of blocks globally used to the left.
            global_block_right: int. Number of blocks globally used to the right.

        Returns:
            row containing the random attention vector of size num_rand_blocks.
        """
        # list of to_blocks from which to choose random attention
        to_block_list = jnp.arange(to_start_block_id, to_end_block_id, dtype=jnp.int32)
        # permute the blocks
        perm_block = jax.random.permutation(indices_prng_key, to_block_list)

        # illegal blocks for the current block id, using window
        illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))

        # Add blocks at the start and at the end
        illegal_blocks.extend(list(range(global_block_left)))
        illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))

        # The second from_block cannot choose random attention on second last to_block
        if block_id == 1:
            illegal_blocks.append(to_end_block_id - 2)

        # The second last from_block cannot choose random attention on second to_block
        if block_id == to_end_block_id - 2:
            illegal_blocks.append(1)

        selected_random_blocks = []

        for i in range(to_end_block_id - to_start_block_id):
            if perm_block[i] not in illegal_blocks:
                selected_random_blocks.append(perm_block[i])
            if len(selected_random_blocks) == num_rand_blocks:
                break
        return jnp.array(selected_random_blocks, dtype=jnp.int32)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->BigBird
class FlaxBigBirdSelfOutput(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class FlaxBigBirdAttention(nn.Module):
    config: BigBirdConfig
    layer_id: int = None
    causal: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        if self.config.attention_type == "original_full":
            self.self = FlaxBigBirdSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        elif self.config.attention_type == "block_sparse":
            self.self = FlaxBigBirdBlockSparseAttention(self.config, block_sparse_seed=self.layer_id, dtype=self.dtype)
        else:
            raise ValueError(
                f"Your `config.attention_type` is {self.config.attention_type} but it can either be `original_full` or"
                " `block_sparse`"
            )

        self.output = FlaxBigBirdSelfOutput(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states=None,
        init_cache=False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
        if self.config.attention_type == "original_full":
            attn_outputs = self.self(
                hidden_states,
                attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=key_value_states,
                init_cache=init_cache,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
        else:
            attn_outputs = self.self(
                hidden_states,
                attention_mask,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_outputs[1],)

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->BigBird
class FlaxBigBirdIntermediate(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.activation = ACT2FN[self.config.hidden_act]

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->BigBird
class FlaxBigBirdOutput(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + attention_output)
        return hidden_states


class FlaxBigBirdLayer(nn.Module):
    config: BigBirdConfig
    layer_id: int = None
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = FlaxBigBirdAttention(
            self.config, layer_id=self.layer_id, causal=self.config.is_decoder, dtype=self.dtype
        )
        self.intermediate = FlaxBigBirdIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxBigBirdOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention:
            self.crossattention = FlaxBigBirdAttention(self.config, causal=False, dtype=self.dtype)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer.__call__ with Bert->BigBird
    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
    ):
        # Self Attention
        attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attention_output = attention_outputs[0]

        # Cross-Attention Block
        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask=encoder_attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=encoder_hidden_states,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]

        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attention_outputs[1],)
            if encoder_hidden_states is not None:
                outputs += (cross_attention_outputs[1],)
        return outputs


class FlaxBigBirdLayerCollection(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxBigBirdCheckpointLayer = remat(FlaxBigBirdLayer, static_argnums=(5, 6, 7))
            self.layers = [
                FlaxBigBirdCheckpointLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]
        else:
            self.layers = [
                FlaxBigBirdLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection.__call__ with Bert->BigBird
    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        # Check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.shape[0] != (len(self.layers)):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for                  "
                    f"       {head_mask.shape[0]}."
                )

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = layer(
                hidden_states,
                attention_mask,
                head_mask[i] if head_mask is not None else None,
                encoder_hidden_states,
                encoder_attention_mask,
                init_cache,
                deterministic,
                output_attentions,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->BigBird
class FlaxBigBirdEncoder(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.layer = FlaxBigBirdLayerCollection(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        return self.layer(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPredictionHeadTransform with Bert->BigBird
class FlaxBigBirdPredictionHeadTransform(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
        self.activation = ACT2FN[self.config.hidden_act]
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return self.LayerNorm(hidden_states)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLMPredictionHead with Bert->BigBird, np.ndarray->jnp.ndarray
class FlaxBigBirdLMPredictionHead(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.transform = FlaxBigBirdPredictionHeadTransform(self.config, dtype=self.dtype)
        self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.transform(hidden_states)

        if shared_embedding is not None:
            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            hidden_states = self.decoder(hidden_states)

        bias = jnp.asarray(self.bias, self.dtype)
        hidden_states += bias
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyMLMHead with Bert->BigBird
class FlaxBigBirdOnlyMLMHead(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.predictions = FlaxBigBirdLMPredictionHead(self.config, dtype=self.dtype)

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding)
        return hidden_states


class FlaxBigBirdPreTrainingHeads(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.predictions = FlaxBigBirdLMPredictionHead(self.config, dtype=self.dtype)
        self.seq_relationship = nn.Dense(2, dtype=self.dtype)

    def __call__(self, hidden_states, pooled_output, shared_embedding=None):
        prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score


class FlaxBigBirdPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BigBirdConfig
    base_model_prefix = "bert"
    module_class: nn.Module = None

    def __init__(
        self,
        config: BigBirdConfig,
        input_shape: Optional[tuple] = None,
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        if config.attention_type == "block_sparse" and input_shape is None:
            input_shape = (1, 12 * config.block_size)
        elif input_shape is None:
            input_shape = (1, 1)

        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.zeros_like(input_ids)
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        params_rng, dropout_rng, indices_rng = jax.random.split(rng, num=3)
        rngs = {"params": params_rng, "dropout": dropout_rng, "indices": indices_rng}

        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                return_dict=False,
            )
        else:
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                return_dict=False,
            )

        random_params = module_init_outputs["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
    def init_cache(self, batch_size, max_length):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        params: dict = None,
        dropout_rng: Optional[jax.random.PRNGKey] = None,
        indices_rng: Optional[jax.random.PRNGKey] = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        past_key_values: dict = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        # Handle any PRNG if needed
        rngs = {}
        if indices_rng is not None:
            rngs["indices"] = indices_rng

        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        if self.config.add_cross_attention:
            # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
            # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
            # changed by FlaxBigBirdAttention module
            if past_key_values:
                inputs["cache"] = past_key_values
                mutable = ["cache"]
            else:
                mutable = False

            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
                mutable=mutable,
            )

            # add updated cache to model output
            if past_key_values is not None and return_dict:
                outputs, past_key_values = outputs
                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
                return outputs
            elif past_key_values is not None and not return_dict:
                outputs, past_key_values = outputs
                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]

        else:
            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
            )

        return outputs


class FlaxBigBirdModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    add_pooling_layer: bool = True
    gradient_checkpointing: bool = False

    def setup(self):
        self.embeddings = FlaxBigBirdEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxBigBirdEncoder(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.pooler = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        hidden_states = self.embeddings(
            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
        )
        outputs = self.encoder(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            deterministic=deterministic,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]

        pooled = nn.tanh(self.pooler(hidden_states[:, 0, :])) if self.add_pooling_layer else None

        if not return_dict:
            # if pooled is None, don't return it
            if pooled is None:
                return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]

        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=hidden_states,
            pooler_output=pooled,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    "The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.",
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModel with Bert->BigBird
class FlaxBigBirdModel(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdModule


append_call_sample_docstring(FlaxBigBirdModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingModule with Bert->BigBird
class FlaxBigBirdForPreTrainingModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.cls = FlaxBigBirdPreTrainingHeads(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.tie_word_embeddings:
            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        hidden_states = outputs[0]
        pooled_output = outputs[1]

        prediction_scores, seq_relationship_score = self.cls(
            hidden_states, pooled_output, shared_embedding=shared_embedding
        )

        if not return_dict:
            return (prediction_scores, seq_relationship_score) + outputs[2:]

        return FlaxBigBirdForPreTrainingOutput(
            prediction_logits=prediction_scores,
            seq_relationship_logits=seq_relationship_score,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
    sentence prediction (classification)` head.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForPreTraining with Bert->BigBird
class FlaxBigBirdForPreTraining(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForPreTrainingModule


FLAX_BIG_BIRD_FOR_PRETRAINING_DOCSTRING = """
    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, FlaxBigBirdForPreTraining

    >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-roberta-base")
    >>> model = FlaxBigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
    >>> outputs = model(**inputs)

    >>> prediction_logits = outputs.prediction_logits
    >>> seq_relationship_logits = outputs.seq_relationship_logits
    ```
"""

overwrite_call_docstring(
    FlaxBigBirdForPreTraining,
    BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BIG_BIRD_FOR_PRETRAINING_DOCSTRING,
)
append_replace_return_docstrings(
    FlaxBigBirdForPreTraining, output_type=FlaxBigBirdForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMaskedLMModule with Bert->BigBird
class FlaxBigBirdForMaskedLMModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.cls = FlaxBigBirdOnlyMLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.cls(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxMaskedLMOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings("""BigBird Model with a `language modeling` head on top.""", BIG_BIRD_START_DOCSTRING)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMaskedLM with Bert->BigBird
class FlaxBigBirdForMaskedLM(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForMaskedLMModule


append_call_sample_docstring(FlaxBigBirdForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)


class FlaxBigBirdClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.out_proj = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(self, features, deterministic=True):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x, deterministic=deterministic)
        x = self.dense(x)
        x = ACT2FN[self.config.hidden_act](x)
        x = self.dropout(x, deterministic=deterministic)
        x = self.out_proj(x)
        return x


class FlaxBigBirdForSequenceClassificationModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.classifier = FlaxBigBirdClassificationHead(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, deterministic=deterministic)

        if not return_dict:
            return (logits,) + outputs[2:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForSequenceClassification with Bert->BigBird
class FlaxBigBirdForSequenceClassification(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForSequenceClassificationModule


append_call_sample_docstring(
    FlaxBigBirdForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->BigBird
class FlaxBigBirdForMultipleChoiceModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None

        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

        if not return_dict:
            return (reshaped_logits,) + outputs[2:]

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
class FlaxBigBirdForMultipleChoice(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForMultipleChoiceModule

    def __init__(
        self,
        config: BigBirdConfig,
        input_shape: Optional[tuple] = None,
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        **kwargs,
    ):
        if config.attention_type == "block_sparse" and input_shape is None:
            input_shape = (1, 1, 12 * config.block_size)
        elif input_shape is None:
            input_shape = (1, 1)
        super().__init__(config, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)


overwrite_call_docstring(
    FlaxBigBirdForMultipleChoice, BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxBigBirdForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->BigBird
class FlaxBigBirdForTokenClassificationModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxTokenClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassification with Bert->BigBird
class FlaxBigBirdForTokenClassification(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForTokenClassificationModule


append_call_sample_docstring(
    FlaxBigBirdForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


class FlaxBigBirdForQuestionAnsweringHead(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.intermediate = FlaxBigBirdIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxBigBirdOutput(self.config, dtype=self.dtype)
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(self, encoder_output, deterministic=True):
        hidden_states = self.dropout(encoder_output, deterministic=deterministic)
        hidden_states = self.intermediate(hidden_states)
        hidden_states = self.output(hidden_states, encoder_output)
        hidden_states = self.qa_outputs(hidden_states)
        return hidden_states


class FlaxBigBirdForQuestionAnsweringModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    add_pooling_layer: bool = False
    gradient_checkpointing: bool = False

    def setup(self):
        self.config.num_labels = 2
        self.bert = FlaxBigBirdModule(
            self.config,
            dtype=self.dtype,
            add_pooling_layer=self.add_pooling_layer,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.qa_classifier = FlaxBigBirdForQuestionAnsweringHead(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        logits_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        pooled_output = outputs[1] if self.add_pooling_layer else None
        logits = self.qa_classifier(hidden_states, deterministic=deterministic)

        if logits_mask is not None:
            # removing question tokens from the competition
            logits = logits - logits_mask * 1e6

        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        if not return_dict:
            return (start_logits, end_logits) + outputs[1:]

        return FlaxBigBirdForQuestionAnsweringModelOutput(
            start_logits=start_logits,
            end_logits=end_logits,
            pooled_output=pooled_output,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    BIG_BIRD_START_DOCSTRING,
)
class FlaxBigBirdForQuestionAnswering(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForQuestionAnsweringModule

    @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        question_lengths=None,
        params: dict = None,
        dropout_rng: Optional[jax.random.PRNGKey] = None,
        indices_rng: Optional[jax.random.PRNGKey] = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        if question_lengths is None and input_ids is not None:
            # assuming input_ids format: <cls> <question> <sep> context <sep>
            question_lengths = jnp.argmax((input_ids == self.config.sep_token_id).astype("i4"), axis=-1) + 1
            question_lengths = jnp.expand_dims(question_lengths, axis=1)

        seqlen = input_ids.shape[1]

        logits_mask = None
        if question_lengths is not None:
            # setting lengths logits to `-inf`
            logits_mask = self.prepare_question_mask(question_lengths, seqlen)
            if token_type_ids is None:
                token_type_ids = (~logits_mask).astype("i4")
            logits_mask = jnp.expand_dims(logits_mask, axis=2)
            logits_mask = logits_mask.at[:, 0].set(False)

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        if indices_rng is not None:
            rngs["indices"] = indices_rng

        return self.module.apply(
            {"params": params or self.params},
            jnp.array(input_ids, dtype="i4"),
            jnp.array(attention_mask, dtype="i4"),
            token_type_ids,
            jnp.array(position_ids, dtype="i4"),
            jnp.array(head_mask, dtype="i4"),
            logits_mask,
            not train,
            output_attentions,
            output_hidden_states,
            return_dict,
            rngs=rngs,
        )

    @staticmethod
    def prepare_question_mask(q_lengths, maxlen: int):
        # q_lengths -> (bz, 1)
        mask = jnp.arange(0, maxlen)
        mask = jnp.expand_dims(mask, axis=0) < q_lengths
        return mask


append_call_sample_docstring(
    FlaxBigBirdForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxBigBirdForQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)


class FlaxBigBirdForCausalLMModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.cls = FlaxBigBirdOnlyMLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        token_type_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.cls(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxCausalLMOutputWithCrossAttentions(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
    autoregressive tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForCausalLM with Bert->BigBird
class FlaxBigBirdForCausalLM(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyway.
        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs


append_call_sample_docstring(
    FlaxBigBirdForCausalLM,
    _CHECKPOINT_FOR_DOC,
    FlaxCausalLMOutputWithCrossAttentions,
    _CONFIG_FOR_DOC,
)

# coding=utf-8
# Copyright 2021 The Google Flax Team Authors and The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import Callable, Optional, Tuple

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen import partitioning as nn_partitioning
from flax.linen.attention import dot_product_attention_weights
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax

from ...modeling_flax_outputs import (
    FlaxBaseModelOutputWithPastAndCrossAttentions,
    FlaxBaseModelOutputWithPooling,
    FlaxBaseModelOutputWithPoolingAndCrossAttentions,
    FlaxCausalLMOutputWithCrossAttentions,
    FlaxMaskedLMOutput,
    FlaxMultipleChoiceModelOutput,
    FlaxSequenceClassifierOutput,
    FlaxTokenClassifierOutput,
)
from ...modeling_flax_utils import (
    ACT2FN,
    FlaxPreTrainedModel,
    append_call_sample_docstring,
    append_replace_return_docstrings,
    overwrite_call_docstring,
)
from ...utils import ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_big_bird import BigBirdConfig


logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "google/bigbird-roberta-base"
_CONFIG_FOR_DOC = "BigBirdConfig"

remat = nn_partitioning.remat


@flax.struct.dataclass
class FlaxBigBirdForPreTrainingOutput(ModelOutput):
    """
    Output type of [`BigBirdForPreTraining`].

    Args:
        prediction_logits (`jnp.ndarray` of shape `(batch_size, sequence_length, config.vocab_size)`):
            Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
        seq_relationship_logits (`jnp.ndarray` of shape `(batch_size, 2)`):
            Prediction scores of the next sequence prediction (classification) head (scores of True/False continuation
            before SoftMax).
        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    prediction_logits: jnp.ndarray = None
    seq_relationship_logits: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


@flax.struct.dataclass
class FlaxBigBirdForQuestionAnsweringModelOutput(ModelOutput):
    """
    Base class for outputs of question answering models.

    Args:
        start_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Span-start scores (before SoftMax).
        end_logits (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Span-end scores (before SoftMax).
        pooled_output (`jnp.ndarray` of shape `(batch_size, hidden_size)`):
            pooled_output returned by FlaxBigBirdModel.
        hidden_states (`tuple(jnp.ndarray)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `jnp.ndarray` (one for the output of the embeddings + one for the output of each layer) of shape
            `(batch_size, sequence_length, hidden_size)`.

            Hidden-states of the model at the output of each layer plus the initial embedding outputs.
        attentions (`tuple(jnp.ndarray)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `jnp.ndarray` (one for each layer) of shape `(batch_size, num_heads, sequence_length,
            sequence_length)`.

            Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
            heads.
    """

    start_logits: jnp.ndarray = None
    end_logits: jnp.ndarray = None
    pooled_output: jnp.ndarray = None
    hidden_states: Optional[Tuple[jnp.ndarray]] = None
    attentions: Optional[Tuple[jnp.ndarray]] = None


BIG_BIRD_START_DOCSTRING = r"""

    This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading, saving and converting weights from PyTorch models)

    This model is also a Flax Linen [flax.linen.Module](https://flax.readthedocs.io/en/latest/flax.linen.html#module)
    subclass. Use it as a regular Flax linen Module and refer to the Flax documentation for all matter related to
    general usage and behavior.

    Finally, this model supports inherent JAX features such as:

    - [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
    - [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
    - [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
    - [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)

    Parameters:
        config ([`BigBirdConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
        dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
            The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
            `jax.numpy.bfloat16` (on TPUs).

            This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
            specified all the computation will be performed with the given `dtype`.

            **Note that this only specifies the dtype of the computation and does not influence the dtype of model
            parameters.**

            If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`] and
            [`~FlaxPreTrainedModel.to_bf16`].
"""

BIG_BIRD_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`numpy.ndarray` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`numpy.ndarray` of shape `({0})`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:

            - 1 for tokens that are **not masked**,
            - 0 for tokens that are **masked**.

            [What are attention masks?](../glossary#attention-mask)
        token_type_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`numpy.ndarray` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
        head_mask (`numpy.ndarray` of shape `({0})`, `optional):
            Mask to nullify selected heads of the attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.

"""


class FlaxBigBirdEmbeddings(nn.Module):
    """Construct the embeddings from word, position and token_type embeddings."""

    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEmbeddings.setup
    def setup(self):
        self.word_embeddings = nn.Embed(
            self.config.vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.position_embeddings = nn.Embed(
            self.config.max_position_embeddings,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.token_type_embeddings = nn.Embed(
            self.config.type_vocab_size,
            self.config.hidden_size,
            embedding_init=jax.nn.initializers.normal(stddev=self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, input_ids, token_type_ids, position_ids, attention_mask, deterministic: bool = True):
        # Embed
        inputs_embeds = self.word_embeddings(input_ids.astype("i4"))
        position_embeds = self.position_embeddings(position_ids.astype("i4"))
        token_type_embeddings = self.token_type_embeddings(token_type_ids.astype("i4"))

        if self.config.rescale_embeddings:
            inputs_embeds *= self.config.hidden_size**0.5

        # Sum all embeddings
        hidden_states = inputs_embeds + token_type_embeddings + position_embeds

        # Layer Norm
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfAttention with Bert->BigBird
class FlaxBigBirdSelfAttention(nn.Module):
    config: BigBirdConfig
    causal: bool = False
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.head_dim = self.config.hidden_size // self.config.num_attention_heads
        if self.config.hidden_size % self.config.num_attention_heads != 0:
            raise ValueError(
                "`config.hidden_size`: {self.config.hidden_size} has to be a multiple of `config.num_attention_heads` "
                "                   : {self.config.num_attention_heads}"
            )

        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

        if self.causal:
            self.causal_mask = make_causal_mask(
                jnp.ones((1, self.config.max_position_embeddings), dtype="bool"), dtype="bool"
            )

    def _split_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.num_attention_heads, self.head_dim))

    def _merge_heads(self, hidden_states):
        return hidden_states.reshape(hidden_states.shape[:2] + (self.config.hidden_size,))

    @nn.compact
    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartAttention._concatenate_to_cache
    def _concatenate_to_cache(self, key, value, query, attention_mask):
        """
        This function takes projected key, value states from a single input token and concatenates the states to cached
        states from previous steps. This function is slighly adapted from the official Flax repository:
        https://github.com/google/flax/blob/491ce18759622506588784b4fca0e4bf05f8c8cd/flax/linen/attention.py#L252
        """
        # detect if we're initializing by absence of existing cache data.
        is_initialized = self.has_variable("cache", "cached_key")
        cached_key = self.variable("cache", "cached_key", jnp.zeros, key.shape, key.dtype)
        cached_value = self.variable("cache", "cached_value", jnp.zeros, value.shape, value.dtype)
        cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))

        if is_initialized:
            *batch_dims, max_length, num_heads, depth_per_head = cached_key.value.shape
            # update key, value caches with our new 1d spatial slices
            cur_index = cache_index.value
            indices = (0,) * len(batch_dims) + (cur_index, 0, 0)
            key = lax.dynamic_update_slice(cached_key.value, key, indices)
            value = lax.dynamic_update_slice(cached_value.value, value, indices)
            cached_key.value = key
            cached_value.value = value
            num_updated_cache_vectors = query.shape[1]
            cache_index.value = cache_index.value + num_updated_cache_vectors
            # causal mask for cached decoder self-attention: our single query position should only attend to those key positions that have already been generated and cached, not the remaining zero elements.
            pad_mask = jnp.broadcast_to(
                jnp.arange(max_length) < cur_index + num_updated_cache_vectors,
                tuple(batch_dims) + (1, num_updated_cache_vectors, max_length),
            )
            attention_mask = combine_masks(pad_mask, attention_mask)
        return key, value, attention_mask

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states: Optional[jnp.array] = None,
        init_cache: bool = False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # if key_value_states are provided this layer is used as a cross-attention layer
        # for the decoder
        is_cross_attention = key_value_states is not None
        batch_size = hidden_states.shape[0]

        # get query proj
        query_states = self.query(hidden_states)
        # get key, value proj
        if is_cross_attention:
            # cross_attentions
            key_states = self.key(key_value_states)
            value_states = self.value(key_value_states)
        else:
            # self_attention
            key_states = self.key(hidden_states)
            value_states = self.value(hidden_states)

        query_states = self._split_heads(query_states)
        key_states = self._split_heads(key_states)
        value_states = self._split_heads(value_states)

        # handle cache prepare causal attention mask
        if self.causal:
            query_length, key_length = query_states.shape[1], key_states.shape[1]
            if self.has_variable("cache", "cached_key"):
                mask_shift = self.variables["cache"]["cache_index"]
                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
                causal_mask = lax.dynamic_slice(
                    self.causal_mask, (0, 0, mask_shift, 0), (1, 1, query_length, max_decoder_length)
                )
            else:
                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
            causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])

        # combine masks if needed
        if attention_mask is not None and self.causal:
            attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
            attention_mask = combine_masks(attention_mask, causal_mask)
        elif self.causal:
            attention_mask = causal_mask
        elif attention_mask is not None:
            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))

        # During fast autoregressive decoding, we feed one position at a time,
        # and cache the keys and values step by step.
        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
            key_states, value_states, attention_mask = self._concatenate_to_cache(
                key_states, value_states, query_states, attention_mask
            )

        # Convert the boolean attention mask to an attention bias.
        if attention_mask is not None:
            # attention mask in the form of attention bias
            attention_bias = lax.select(
                attention_mask > 0,
                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
                jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
            )
        else:
            attention_bias = None

        dropout_rng = None
        if not deterministic and self.config.attention_probs_dropout_prob > 0.0:
            dropout_rng = self.make_rng("dropout")

        attn_weights = dot_product_attention_weights(
            query_states,
            key_states,
            bias=attention_bias,
            dropout_rng=dropout_rng,
            dropout_rate=self.config.attention_probs_dropout_prob,
            broadcast_dropout=True,
            deterministic=deterministic,
            dtype=self.dtype,
            precision=None,
        )

        # Mask heads if we want to
        if layer_head_mask is not None:
            attn_weights = jnp.einsum("...hqk,h->...hqk", attn_weights, layer_head_mask)

        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
        attn_output = attn_output.reshape(attn_output.shape[:2] + (-1,))

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs


class FlaxBigBirdBlockSparseAttention(nn.Module):
    config: BigBirdConfig
    block_sparse_seed: int = None
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.query = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            use_bias=self.config.use_bias,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.key = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            use_bias=self.config.use_bias,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )
        self.value = nn.Dense(
            self.config.hidden_size,
            dtype=self.dtype,
            use_bias=self.config.use_bias,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
        )

    @staticmethod
    def transpose_for_scores(x, n_heads, head_size):
        new_x_shape = x.shape[:-1] + (n_heads, head_size)
        x = x.reshape(*new_x_shape)
        return jnp.transpose(x, axes=(0, 2, 1, 3))

    def __call__(
        self,
        hidden_states,
        attention_mask,
        deterministic=True,
        output_attentions=False,
    ):
        n_heads = self.config.num_attention_heads
        head_size = self.config.hidden_size // n_heads

        blocked_encoder_mask, band_mask, from_mask, to_mask = self.create_masks_for_block_sparse_attn(
            attention_mask, self.config.block_size
        )

        query_layer = self.transpose_for_scores(self.query(hidden_states), n_heads, head_size)
        key_layer = self.transpose_for_scores(self.key(hidden_states), n_heads, head_size)
        value_layer = self.transpose_for_scores(self.value(hidden_states), n_heads, head_size)

        indices_prng_key = None
        if not deterministic:
            indices_prng_key = self.make_rng("indices")

        attn_output, attn_weights = self.bigbird_block_sparse_attention(
            query_layer,
            key_layer,
            value_layer,
            band_mask,
            from_mask,
            to_mask,
            blocked_encoder_mask,
            blocked_encoder_mask,
            n_heads,
            head_size,
            indices_prng_key=indices_prng_key,
            deterministic=deterministic,
            plan_from_length=None,
            plan_num_rand_blocks=None,
            output_attentions=output_attentions,
        )

        outputs = (attn_output, attn_weights) if output_attentions else (attn_output,)
        return outputs

    @staticmethod
    def create_masks_for_block_sparse_attn(attention_mask, block_size: int):
        batch_size, seq_length = attention_mask.shape
        if seq_length % block_size != 0:
            raise ValueError(
                f"Sequence length must be multiple of block size, but sequence length is {seq_length}, while block"
                f" size is {block_size}."
            )

        def create_band_mask_from_inputs(from_blocked_mask, to_blocked_mask):
            """
            Create 3D attention mask from a 2D tensor mask.

            Args:
                from_blocked_mask: 2D Tensor of shape [batch_size,
                from_seq_length//from_block_size, from_block_size].
                to_blocked_mask: int32 Tensor of shape [batch_size,
                to_seq_length//to_block_size, to_block_size].

            Returns:
                float Tensor of shape [batch_size, 1, from_seq_length//from_block_size-4, from_block_size,
                3*to_block_size].
            """
            exp_blocked_to_pad = jnp.concatenate(
                [to_blocked_mask[:, 1:-3], to_blocked_mask[:, 2:-2], to_blocked_mask[:, 3:-1]], axis=2
            )
            band_mask = jnp.einsum("blq,blk->blqk", from_blocked_mask[:, 2:-2], exp_blocked_to_pad)
            band_mask = jnp.expand_dims(band_mask, 1)
            return band_mask

        blocked_encoder_mask = attention_mask.reshape(batch_size, seq_length // block_size, block_size)
        band_mask = create_band_mask_from_inputs(blocked_encoder_mask, blocked_encoder_mask)

        from_mask = attention_mask.reshape(batch_size, 1, seq_length, 1)
        to_mask = attention_mask.reshape(batch_size, 1, 1, seq_length)

        return blocked_encoder_mask, band_mask, from_mask, to_mask

    def bigbird_block_sparse_attention(
        self,
        query_layer,
        key_layer,
        value_layer,
        band_mask,
        from_mask,
        to_mask,
        from_blocked_mask,
        to_blocked_mask,
        n_heads,
        head_size,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        deterministic: Optional[bool] = True,
        plan_from_length=None,
        plan_num_rand_blocks=None,
        output_attentions=None,
    ):
        # BigBird block-sparse attention as suggested in paper

        # ITC:
        #     global tokens: 2 x block_size
        #     window tokens: 3 x block_size
        #     random tokens: num_rand_tokens x block_size

        # ETC:
        #     global tokens: extra_globals_tokens + 2 x block_size
        #     window tokens: 3 x block_size
        #     random tokens: num_rand_tokens x block_size

        # Note:
        #     1) Currently, ETC is not supported.
        #     2) Window size is fixed to 3 blocks & it can be changed only by
        #     changing `block_size`.
        #     3) Number of global blocks are fixed (2 blocks here) & global tokens can be
        #     controlled only by `block_size`.

        # attention is calculated separately for q[0], q[1], q[2:-2], q[-2], q[-1] in order to use special trick of
        # shifting tokens (for calculating sliding attention). hence following code can be divided into 5 parts.

        bsz, _, from_seq_len, _ = query_layer.shape
        to_seq_len = key_layer.shape[2]
        from_block_size = to_block_size = self.config.block_size

        if from_seq_len % from_block_size != 0:
            raise ValueError("Query sided sequence length must be multiple of block size")

        if to_seq_len % to_block_size != 0:
            raise ValueError("Key/Value sided sequence length must be multiple of block size")

        if from_seq_len // from_block_size != to_seq_len // to_block_size:
            raise ValueError("Error the number of blocks needs to be same!")

        n_rand_blocks = self.config.num_random_blocks
        rsqrt_d = 1 / jnp.sqrt(head_size)
        attn_mask_penalty = -10000.0

        if from_seq_len in [1024, 3072, 4096]:  # old plans used in paper
            max_seqlen = self.config.max_position_embeddings
            rand_attn = [
                self._bigbird_block_rand_mask(
                    max_seqlen,
                    max_seqlen,
                    from_block_size,
                    to_block_size,
                    n_rand_blocks,
                    indices_prng_key=indices_prng_key,
                    deterministic=deterministic,
                    last_idx=1024,
                )[: (from_seq_len // from_block_size - 2)]
                for _ in range(n_heads)
            ]
        else:
            if plan_from_length is None:
                plan_from_length, plan_num_rand_blocks = self._get_rand_attn_plan(
                    from_seq_len, from_block_size, n_rand_blocks
                )
            rand_attn = self._bigbird_block_rand_mask_with_head(
                from_seq_length=from_seq_len,
                to_seq_length=to_seq_len,
                from_block_size=from_block_size,
                to_block_size=to_block_size,
                num_heads=n_heads,
                plan_from_length=plan_from_length,
                plan_num_rand_blocks=plan_num_rand_blocks,
                indices_prng_key=indices_prng_key,
            )

        rand_attn = jnp.stack(rand_attn, axis=0)
        rand_attn = jnp.broadcast_to(rand_attn, (bsz,) + rand_attn.shape)

        rand_mask = self._create_rand_mask_from_inputs(
            from_blocked_mask, to_blocked_mask, rand_attn, n_heads, n_rand_blocks, bsz, from_seq_len, from_block_size
        )

        blocked_query_matrix = query_layer.reshape(bsz, n_heads, from_seq_len // from_block_size, from_block_size, -1)
        blocked_key_matrix = key_layer.reshape(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)
        blocked_value_matrix = value_layer.reshape(bsz, n_heads, to_seq_len // to_block_size, to_block_size, -1)

        shape = (bsz, n_heads, to_seq_len // to_block_size - 2, n_rand_blocks * to_block_size, -1)
        gathered_key = self.jax_gather(blocked_key_matrix, rand_attn, batch_dims=2).reshape(*shape)
        gathered_value = self.jax_gather(blocked_value_matrix, rand_attn, batch_dims=2).reshape(*shape)

        # 1st PART
        # 1st block (global block) attention scores
        # q[0] x (k[0], k[1], k[2], k[3], k[4] .... )

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
        first_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, 0], key_layer)

        first_product = first_product * rsqrt_d
        first_product += (1.0 - to_mask) * attn_mask_penalty
        first_attn_weights = jax.nn.softmax(first_product, axis=-1)  # [bsz, n_heads, from_block_size, to_seq_len]

        # [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
        first_context_layer = jnp.einsum("bhqk,bhkd->bhqd", first_attn_weights, value_layer)
        first_context_layer = jnp.expand_dims(first_context_layer, 2)

        # 2nd PART
        # 2nd block attention scores
        # q[1] x (sliding_keys, random_keys, global_keys)
        # sliding key blocks -> 2nd, 3rd blocks
        # global key blocks -> 1st block

        second_key_mat = jnp.concatenate(
            [
                blocked_key_matrix[:, :, 0],
                blocked_key_matrix[:, :, 1],
                blocked_key_matrix[:, :, 2],
                blocked_key_matrix[:, :, -1],
                gathered_key[:, :, 0],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        second_value_mat = jnp.concatenate(
            [
                blocked_value_matrix[:, :, 0],
                blocked_value_matrix[:, :, 1],
                blocked_value_matrix[:, :, 2],
                blocked_value_matrix[:, :, -1],
                gathered_value[:, :, 0],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        # ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
        second_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, 1], second_key_mat)
        second_seq_pad = jnp.concatenate(
            [
                to_mask[:, :, :, : 3 * to_block_size],
                to_mask[:, :, :, -to_block_size:],
                jnp.ones([bsz, 1, 1, n_rand_blocks * to_block_size], dtype=to_mask.dtype),
            ],
            axis=3,
        )
        second_rand_pad = jnp.concatenate(
            [
                jnp.ones([bsz, n_heads, from_block_size, 4 * to_block_size], dtype=rand_mask.dtype),
                rand_mask[:, :, 0],
            ],
            axis=3,
        )
        second_product = second_product * rsqrt_d
        second_product += (1.0 - jnp.minimum(second_seq_pad, second_rand_pad)) * attn_mask_penalty
        second_attn_weights = jax.nn.softmax(
            second_product, axis=-1
        )  # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]

        # [bsz, n_heads, from_block_size, (4+r)*to_block_size] x [bsz, n_heads, (4+r)*to_block_size, -1]
        #  ==> [bsz, n_heads, from_block_size, -1]
        second_context_layer = jnp.einsum("bhqk,bhkd->bhqd", second_attn_weights, second_value_mat)
        second_context_layer = jnp.expand_dims(second_context_layer, 2)

        # 3rd PART
        # Middle blocks attention scores
        # q[-2:2] x (sliding_keys, random_keys, global_keys)
        # sliding attn is calculated using special trick of shifting tokens as discussed in paper
        # random keys are generated by taking random indices as per `rand_attn`
        # global keys -> 1st & last block

        exp_blocked_key_matrix = jnp.concatenate(
            [blocked_key_matrix[:, :, 1:-3], blocked_key_matrix[:, :, 2:-2], blocked_key_matrix[:, :, 3:-1]], axis=3
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        exp_blocked_value_matrix = jnp.concatenate(
            [blocked_value_matrix[:, :, 1:-3], blocked_value_matrix[:, :, 2:-2], blocked_value_matrix[:, :, 3:-1]],
            axis=3,
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        middle_query_matrix = blocked_query_matrix[:, :, 2:-2]

        # sliding attention scores for q[-2:2]
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [b, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        inner_band_product = jnp.einsum("bhlqd,bhlkd->bhlqk", middle_query_matrix, exp_blocked_key_matrix)
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, 3*to_block_size]
        inner_band_product = inner_band_product * rsqrt_d

        # randn attention scores for q[-2:2]
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
        # x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
        rand_band_product = jnp.einsum("bhlqd,bhlkd->bhlqk", middle_query_matrix, gathered_key[:, :, 1:-1])
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
        rand_band_product = rand_band_product * rsqrt_d

        # Including 1st block (since it's global)
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1]
        #  ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
        first_band_product = jnp.einsum("bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, 0])
        first_band_product = first_band_product * rsqrt_d

        # Including last block (since it's global)
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1] x [bsz, n_heads, to_block_size, -1]
        #  ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size]
        last_band_product = jnp.einsum("bhlqd,bhkd->bhlqk", middle_query_matrix, blocked_key_matrix[:, :, -1])
        last_band_product = last_band_product * rsqrt_d

        # masking padded tokens
        inner_band_product += (1.0 - band_mask) * attn_mask_penalty
        first_band_product += (1.0 - jnp.expand_dims(to_mask[:, :, :, :to_block_size], 3)) * attn_mask_penalty
        last_band_product += (1.0 - jnp.expand_dims(to_mask[:, :, :, -to_block_size:], 3)) * attn_mask_penalty
        rand_band_product += (1.0 - rand_mask[:, :, 1:-1]) * attn_mask_penalty

        # completing attention scores matrix for all q[-2:2]
        band_product = jnp.concatenate(
            [first_band_product, inner_band_product, rand_band_product, last_band_product], axis=-1
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]

        # safely doing softmax since attention matrix is completed
        attn_weights = jax.nn.softmax(
            band_product, axis=-1
        )  # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, (5+n_rand_blocks)*to_block_size]

        # contribution of sliding keys
        # [bsz, n_heads, m//from_block_size-4, from_block_size, 3*to_block_size]
        # x [bsz, n_heads, from_seq_len//from_block_size-4, 3*to_block_size, -1]
        context_layer = jnp.einsum(
            "bhlqk,bhlkd->bhlqd", attn_weights[:, :, :, :, to_block_size : 4 * to_block_size], exp_blocked_value_matrix
        )
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]

        # adding contribution of random keys
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, n_rand_blocks*to_block_size]
        # x [bsz, n_heads, from_seq_len//from_block_size-4, n_rand_blocks*to_block_size, -1]
        context_layer += jnp.einsum(
            "bhlqk,bhlkd->bhlqd",
            attn_weights[:, :, :, :, 4 * to_block_size : -to_block_size],
            gathered_value[:, :, 1:-1],
        )
        #     ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]

        # adding contribution of global keys
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1]
        #  ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
        context_layer += jnp.einsum(
            "bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, :to_block_size], blocked_value_matrix[:, :, 0]
        )
        # [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, to_block_size] x [bsz, n_heads, to_block_size, -1]
        # ==> [bsz, n_heads, from_seq_len//from_block_size-4, from_block_size, -1]
        context_layer += jnp.einsum(
            "bhlqk,bhkd->bhlqd", attn_weights[:, :, :, :, -to_block_size:], blocked_value_matrix[:, :, -1]
        )

        # 4th PART
        # last 2nd token attention scores
        # q[-2] x (sliding_keys, random_keys, global_keys)
        # sliding key blocks -> last 3 blocks
        # global key block -> 1st block
        # random key block -> based on indices stored in `randn_attn`

        second_last_key_mat = jnp.concatenate(
            [
                blocked_key_matrix[:, :, 0],
                blocked_key_matrix[:, :, -3],
                blocked_key_matrix[:, :, -2],
                blocked_key_matrix[:, :, -1],
                gathered_key[:, :, -1],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+n_random_blocks)*to_block_size, -1]
        second_last_value_mat = jnp.concatenate(
            [
                blocked_value_matrix[:, :, 0],
                blocked_value_matrix[:, :, -3],
                blocked_value_matrix[:, :, -2],
                blocked_value_matrix[:, :, -1],
                gathered_value[:, :, -1],
            ],
            axis=2,
        )  # [bsz, n_heads, (4+r)*to_block_size, -1]

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        # ==> [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]
        second_last_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, -2], second_last_key_mat)
        second_last_seq_pad = jnp.concatenate(
            [
                to_mask[:, :, :, :to_block_size],
                to_mask[:, :, :, -3 * to_block_size :],
                jnp.ones([bsz, 1, 1, n_rand_blocks * to_block_size], dtype=to_mask.dtype),
            ],
            axis=3,
        )
        second_last_rand_pad = jnp.concatenate(
            [
                jnp.ones([bsz, n_heads, from_block_size, 4 * to_block_size], dtype=rand_mask.dtype),
                rand_mask[:, :, -1],
            ],
            axis=3,
        )
        second_last_product = second_last_product * rsqrt_d
        second_last_product += (1.0 - jnp.minimum(second_last_seq_pad, second_last_rand_pad)) * attn_mask_penalty
        second_last_attn_weights = jax.nn.softmax(
            second_last_product, axis=-1
        )  # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size]

        # [bsz, n_heads, from_block_size, (4+n_rand_blocks)*to_block_size] x [bsz, n_heads, (4+n_rand_blocks)*to_block_size, -1]
        # ==> [bsz, n_heads, from_block_size, -1]
        second_last_context_layer = jnp.einsum("bhqk,bhkd->bhqd", second_last_attn_weights, second_last_value_mat)
        second_last_context_layer = jnp.expand_dims(second_last_context_layer, 2)

        # 5th PART
        # last block (global) attention scores
        # q[-1] x (k[0], k[1], k[2], k[3], .... )

        # [bsz, n_heads, from_block_size, -1] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, to_seq_len]
        last_product = jnp.einsum("bhqd,bhkd->bhqk", blocked_query_matrix[:, :, -1], key_layer)
        last_product = last_product * rsqrt_d
        last_product += (1.0 - to_mask) * attn_mask_penalty
        last_attn_weights = jax.nn.softmax(last_product, axis=-1)  # [bsz, n_heads, from_block_size, n]

        # [bsz, n_heads, from_block_size, to_seq_len] x [bsz, n_heads, to_seq_len, -1] ==> [bsz, n_heads, from_block_size, -1]
        last_context_layer = jnp.einsum("bhqk,bhkd->bhqd", last_attn_weights, value_layer)
        last_context_layer = jnp.expand_dims(last_context_layer, 2)

        # combining representations of all tokens
        context_layer = jnp.concatenate(
            [first_context_layer, second_context_layer, context_layer, second_last_context_layer, last_context_layer],
            axis=2,
        )
        context_layer = context_layer.reshape(bsz, n_heads, from_seq_len, -1) * from_mask
        context_layer = jnp.transpose(context_layer, axes=(0, 2, 1, 3)).reshape(bsz, from_seq_len, -1)

        attention_probs = None

        return context_layer, attention_probs

    @staticmethod
    def jax_gather(params, indices, batch_dims=2):
        """
        Gather the indices from params correctly (equivalent to tf.gather but with modifications)

        Args:
            params: (bsz, n_heads, num_blocks, block_size, head_dim)
            indices: (<num_blocks, 1)
        """

        def _jax_gather(params, indices):
            return params[indices]

        for _ in range(batch_dims):
            _jax_gather = jax.vmap(_jax_gather, in_axes=(0, 0))

        return _jax_gather(params, indices)  # params.shape[:batch_dims] + indices.shape + params.shape[batch_dims+1:]

    def _create_rand_mask_from_inputs(
        self,
        from_blocked_mask,
        to_blocked_mask,
        broadcasted_rand_attn,
        num_attention_heads,
        num_random_blocks,
        batch_size,
        from_seq_length,
        from_block_size,
    ):
        """
        Create 3D attention mask from a 2D tensor mask.

        Args:
            from_blocked_mask: 2D Tensor of shape [batch_size, from_seq_length//from_block_size, from_block_size].
            to_blocked_mask: int32 Tensor of shape [batch_size, to_seq_length//to_block_size, to_block_size].
            broadcasted_rand_attn:
                [batch_size, num_attention_heads, from_seq_length//from_block_size-2, num_rand_blocks]
            num_attention_heads: int. Number of attention heads.
            num_random_blocks: int. Number of random chunks per row.
            batch_size: int. Batch size for computation.
            from_seq_length: int. length of from sequence.
            from_block_size: int. size of block in from sequence.

        Returns:
            float Tensor of shape [batch_size, num_attention_heads, from_seq_length//from_block_size-2,
            from_block_size, num_rand_blocks*to_block_size].
        """
        num_windows = from_seq_length // from_block_size - 2
        rand_mask = self.jax_gather(to_blocked_mask, broadcasted_rand_attn, batch_dims=1)
        rand_mask = rand_mask.reshape(
            batch_size, num_attention_heads, num_windows, num_random_blocks * from_block_size
        )
        rand_mask = jnp.einsum("blq,bhlk->bhlqk", from_blocked_mask[:, 1:-1], rand_mask)
        return rand_mask

    @staticmethod
    def _get_rand_attn_plan(from_seq_length, from_block_size, num_rand_blocks):
        """
        Gives the plan of where to put random attention.

        Args:
            from_seq_length: int. length of from sequence.
            from_block_size: int. size of block in from sequence.
            num_rand_blocks: int. Number of random chunks per row.

        Returns:
            plan_from_length: ending location of from block plan_num_rand_blocks: number of random ending location for
            each block
        """

        plan_from_length = []
        plan_num_rand_blocks = []
        if (2 * num_rand_blocks + 5) < (from_seq_length // from_block_size):
            plan_from_length.append(int((2 * num_rand_blocks + 5) * from_block_size))
            plan_num_rand_blocks.append(num_rand_blocks)
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(0)
        elif (num_rand_blocks + 5) < (from_seq_length // from_block_size):
            plan_from_length.append(int((num_rand_blocks + 5) * from_block_size))
            plan_num_rand_blocks.append(num_rand_blocks // 2)
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(num_rand_blocks - (num_rand_blocks // 2))
        else:
            plan_from_length.append(from_seq_length)
            plan_num_rand_blocks.append(num_rand_blocks)

        return plan_from_length, plan_num_rand_blocks

    @staticmethod
    def _bigbird_block_rand_mask(
        from_seq_length,
        to_seq_length,
        from_block_size,
        to_block_size,
        num_rand_blocks,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        deterministic: Optional[bool] = True,
        last_idx: Optional[int] = -1,
    ):
        """
        Create adjacency list of random attention.

        Args:
            from_seq_length: int. length of from sequence.
            to_seq_length: int. length of to sequence.
            from_block_size: int. size of block in from sequence.
            to_block_size: int. size of block in to sequence.
            num_rand_blocks: int. Number of random chunks per row.
            indices_prng_key: jax.random.PRNGKey. PRNG key that is used to perform random jax operations.
            deterministic: bool. When False random attention will be used.
            last_idx: if -1 then num_rand_blocks blocks chosen anywhere in to sequence,
            if positive then num_rand_blocks blocks chosen only up to last_idx.

        Returns:
            adjacency list of size from_seq_length//from_block_size-2 by num_rand_blocks
        """
        # using this method when from_seq_length in [1024, 3072, 4096]

        if from_seq_length // from_block_size != to_seq_length // to_block_size:
            raise ValueError("Error the number of blocks needs to be same!")
        rand_attn = jnp.zeros((from_seq_length // from_block_size - 2, num_rand_blocks), dtype=jnp.int32)
        # deterministic nor randomness
        if deterministic:
            return rand_attn

        middle_seq = jnp.arange(1, to_seq_length // to_block_size - 1, dtype=jnp.int32)
        last = to_seq_length // to_block_size - 1
        if last_idx > (2 * to_block_size):
            last = (last_idx // to_block_size) - 1

        r = num_rand_blocks  # shorthand
        for i in range(1, from_seq_length // from_block_size - 1):
            start = i - 2
            end = i
            if i == 1:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[2:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            elif i == 2:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[3:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            elif i == from_seq_length // from_block_size - 3:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            # Missing -3: should have been sliced till last-3
            elif i == from_seq_length // from_block_size - 2:
                seq_values = jax.random.permutation(indices_prng_key, middle_seq[:last])[:r]
                rand_attn = rand_attn.at[i - 1].set(seq_values)
            # Missing -4: should have been sliced till last-4
            else:
                if start > last:
                    start = last
                    seq_values = jax.random.permutation(indices_prng_key, middle_seq[:start])[:r]
                    rand_attn = rand_attn.at[i - 1].set(seq_values)
                elif (end + 1) == last:
                    seq_values = jax.random.permutation(indices_prng_key, middle_seq[:start])[:r]
                    rand_attn = rand_attn.at[i - 1].set(seq_values)
                else:
                    concat_values = jnp.concatenate((middle_seq[:start], middle_seq[end + 1 : last]))
                    seq_values = jax.random.permutation(indices_prng_key, concat_values)[:r]
                    rand_attn = rand_attn.at[i - 1].set(seq_values)
        return rand_attn

    def _bigbird_block_rand_mask_with_head(
        self,
        from_seq_length,
        to_seq_length,
        from_block_size,
        to_block_size,
        num_heads,
        plan_from_length,
        plan_num_rand_blocks,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        deterministic: Optional[bool] = True,
        window_block_left=1,
        window_block_right=1,
        global_block_top=1,
        global_block_bottom=1,
        global_block_left=1,
        global_block_right=1,
    ):
        """
        Create adjacency list of random attention.

        Args:
            from_seq_length: int. length of from sequence.
            to_seq_length: int. length of to sequence.
            from_block_size: int. size of block in from sequence.
            to_block_size: int. size of block in to sequence.
            num_heads: int. total number of heads.
            plan_from_length: list. plan from length where num_random_blocks are choosen from.
            plan_num_rand_blocks: list. number of rand blocks within the plan.
            indices_prng_key: jax.random.PRNGKey. PRNG key that is used to perform random jax operations.
            deterministic: bool. When False random attention will be used.
            window_block_left: int. number of blocks of window to left of a block.
            window_block_right: int. number of blocks of window to right of a block.
            global_block_top: int. number of blocks at the top.
            global_block_bottom: int. number of blocks at the bottom.
            global_block_left: int. Number of blocks globally used to the left.
            global_block_right: int. Number of blocks globally used to the right.

        Returns:
            adjacency list of size num_head where each element is of size from_seq_length//from_block_size-2 by
            num_rand_blocks
        """
        # using this method when from_seq_length not in [1024, 3072, 4096]

        if from_seq_length // from_block_size != to_seq_length // to_block_size:
            raise ValueError("Error the number of blocks needs to be same!")

        if from_seq_length not in plan_from_length:
            raise ValueError("Error from sequence length not in plan!")

        # Total number of blocks in the mmask
        num_blocks = from_seq_length // from_block_size
        # Number of blocks per plan
        plan_block_length = jnp.array(plan_from_length) // from_block_size
        # till when to follow plan
        max_plan_idx = plan_from_length.index(from_seq_length)

        # Random Attention adjacency list
        rand_attn = [
            jnp.zeros((num_blocks, sum(plan_num_rand_blocks[: max_plan_idx + 1])), dtype=jnp.int32)
            for i in range(num_heads)
        ]

        # deterministic
        if deterministic:
            for nh in range(num_heads):
                rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
            return rand_attn

        # We will go iteratively over the plan blocks and pick random number of
        # Attention blocks from the legally allowed blocks
        for plan_idx in range(max_plan_idx + 1):
            rnd_r_cnt = 0
            if plan_idx > 0:
                # set the row for all from_blocks starting from 0 to
                # plan_block_length[plan_idx-1]
                # column indx start fromm plan_block_length[plan_idx-1] and ends at
                # plan_block_length[plan_idx]
                if plan_num_rand_blocks[plan_idx] > 0:
                    rnd_r_cnt = int(sum(plan_num_rand_blocks[:plan_idx]))
                    curr_r_cnt = int(sum(plan_num_rand_blocks[: plan_idx + 1]))
                    for blk_rw_idx in range(global_block_top, plan_block_length[plan_idx - 1]):
                        for h in range(num_heads):
                            single_block_row_attention = self._get_single_block_row_attention(
                                block_id=blk_rw_idx,
                                to_start_block_id=plan_block_length[plan_idx - 1],
                                to_end_block_id=plan_block_length[plan_idx],
                                num_rand_blocks=plan_num_rand_blocks[plan_idx],
                                window_block_left=window_block_left,
                                window_block_right=window_block_right,
                                global_block_left=global_block_left,
                                global_block_right=global_block_right,
                                indices_prng_key=indices_prng_key,
                            )
                            rand_attn[h] = (
                                rand_attn[h].at[blk_rw_idx, rnd_r_cnt:curr_r_cnt].set(single_block_row_attention)
                            )

                for pl_id in range(plan_idx):
                    if plan_num_rand_blocks[pl_id] == 0:
                        continue
                    for blk_rw_idx in range(plan_block_length[plan_idx - 1], plan_block_length[plan_idx]):
                        rnd_r_cnt = 0
                        to_start_block_id = 0
                        if pl_id > 0:
                            rnd_r_cnt = int(sum(plan_num_rand_blocks[:pl_id]))
                            to_start_block_id = plan_block_length[pl_id - 1]
                        curr_r_cnt = int(sum(plan_num_rand_blocks[: pl_id + 1]))
                        for h in range(num_heads):
                            single_block_row_attention = self._get_single_block_row_attention(
                                block_id=blk_rw_idx,
                                to_start_block_id=to_start_block_id,
                                to_end_block_id=plan_block_length[pl_id],
                                num_rand_blocks=plan_num_rand_blocks[pl_id],
                                window_block_left=window_block_left,
                                window_block_right=window_block_right,
                                global_block_left=global_block_left,
                                global_block_right=global_block_right,
                                indices_prng_key=indices_prng_key,
                            )
                            rand_attn[h] = (
                                rand_attn[h].at[blk_rw_idx, rnd_r_cnt:curr_r_cnt].set(single_block_row_attention)
                            )

            if plan_num_rand_blocks[plan_idx] == 0:
                continue
            curr_r_cnt = int(sum(plan_num_rand_blocks[: plan_idx + 1]))
            from_start_block_id = global_block_top
            to_start_block_id = 0
            if plan_idx > 0:
                rnd_r_cnt = int(sum(plan_num_rand_blocks[:plan_idx]))
                from_start_block_id = plan_block_length[plan_idx - 1]
                to_start_block_id = plan_block_length[plan_idx - 1]
            for blk_rw_idx in range(from_start_block_id, plan_block_length[plan_idx]):
                for h in range(num_heads):
                    single_block_row_attention = self._get_single_block_row_attention(
                        block_id=blk_rw_idx,
                        to_start_block_id=to_start_block_id,
                        to_end_block_id=plan_block_length[plan_idx],
                        num_rand_blocks=plan_num_rand_blocks[plan_idx],
                        window_block_left=window_block_left,
                        window_block_right=window_block_right,
                        global_block_left=global_block_left,
                        global_block_right=global_block_right,
                        indices_prng_key=indices_prng_key,
                    )
                    rand_attn[h] = rand_attn[h].at[blk_rw_idx, rnd_r_cnt:curr_r_cnt].set(single_block_row_attention)

        for nh in range(num_heads):
            rand_attn[nh] = rand_attn[nh][global_block_top : num_blocks - global_block_bottom, :]
        return rand_attn

    @staticmethod
    def _get_single_block_row_attention(
        block_id,
        to_start_block_id,
        to_end_block_id,
        num_rand_blocks,
        indices_prng_key: Optional[jax.random.PRNGKey] = None,
        window_block_left=1,
        window_block_right=1,
        global_block_left=1,
        global_block_right=1,
    ):
        """
        For a single row block get random row attention.

        Args:
            block_id: int. block id of row.
            to_start_block_id: int. random attention column start id.
            to_end_block_id: int. random attention column end id.
            num_rand_blocks: int. number of random blocks to be selected.
            indices_prng_key: jax.random.PRNGKey. PRNG key that is used to perform random jax operations
            window_block_left: int. number of blocks of window to left of a block.
            window_block_right: int. number of blocks of window to right of a block.
            global_block_left: int. Number of blocks globally used to the left.
            global_block_right: int. Number of blocks globally used to the right.

        Returns:
            row containing the random attention vector of size num_rand_blocks.
        """
        # list of to_blocks from which to choose random attention
        to_block_list = jnp.arange(to_start_block_id, to_end_block_id, dtype=jnp.int32)
        # permute the blocks
        perm_block = jax.random.permutation(indices_prng_key, to_block_list)

        # illegal blocks for the current block id, using window
        illegal_blocks = list(range(block_id - window_block_left, block_id + window_block_right + 1))

        # Add blocks at the start and at the end
        illegal_blocks.extend(list(range(global_block_left)))
        illegal_blocks.extend(list(range(to_end_block_id - global_block_right, to_end_block_id)))

        # The second from_block cannot choose random attention on second last to_block
        if block_id == 1:
            illegal_blocks.append(to_end_block_id - 2)

        # The second last from_block cannot choose random attention on second to_block
        if block_id == to_end_block_id - 2:
            illegal_blocks.append(1)

        selected_random_blocks = []

        for i in range(to_end_block_id - to_start_block_id):
            if perm_block[i] not in illegal_blocks:
                selected_random_blocks.append(perm_block[i])
            if len(selected_random_blocks) == num_rand_blocks:
                break
        return jnp.array(selected_random_blocks, dtype=jnp.int32)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertSelfOutput with Bert->BigBird
class FlaxBigBirdSelfOutput(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)

    def __call__(self, hidden_states, input_tensor, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states


class FlaxBigBirdAttention(nn.Module):
    config: BigBirdConfig
    layer_id: int = None
    causal: bool = False
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        if self.config.attention_type == "original_full":
            self.self = FlaxBigBirdSelfAttention(self.config, causal=self.causal, dtype=self.dtype)
        elif self.config.attention_type == "block_sparse":
            self.self = FlaxBigBirdBlockSparseAttention(self.config, block_sparse_seed=self.layer_id, dtype=self.dtype)
        else:
            raise ValueError(
                f"Your `config.attention_type` is {self.config.attention_type} but it can either be `original_full` or"
                " `block_sparse`"
            )

        self.output = FlaxBigBirdSelfOutput(self.config, dtype=self.dtype)

    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        key_value_states=None,
        init_cache=False,
        deterministic=True,
        output_attentions: bool = False,
    ):
        # Attention mask comes in as attention_mask.shape == (*batch_sizes, kv_length)
        # FLAX expects: attention_mask.shape == (*batch_sizes, 1, 1, kv_length) such that it is broadcastable
        # with attn_weights.shape == (*batch_sizes, num_heads, q_length, kv_length)
        if self.config.attention_type == "original_full":
            attn_outputs = self.self(
                hidden_states,
                attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=key_value_states,
                init_cache=init_cache,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
        else:
            attn_outputs = self.self(
                hidden_states,
                attention_mask,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
        attn_output = attn_outputs[0]
        hidden_states = self.output(attn_output, hidden_states, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attn_outputs[1],)

        return outputs


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertIntermediate with Bert->BigBird
class FlaxBigBirdIntermediate(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.intermediate_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.activation = ACT2FN[self.config.hidden_act]

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOutput with Bert->BigBird
class FlaxBigBirdOutput(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.dense = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states, attention_output, deterministic: bool = True):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        hidden_states = self.LayerNorm(hidden_states + attention_output)
        return hidden_states


class FlaxBigBirdLayer(nn.Module):
    config: BigBirdConfig
    layer_id: int = None
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation

    def setup(self):
        self.attention = FlaxBigBirdAttention(
            self.config, layer_id=self.layer_id, causal=self.config.is_decoder, dtype=self.dtype
        )
        self.intermediate = FlaxBigBirdIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxBigBirdOutput(self.config, dtype=self.dtype)
        if self.config.add_cross_attention:
            self.crossattention = FlaxBigBirdAttention(self.config, causal=False, dtype=self.dtype)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayer.__call__ with Bert->BigBird
    def __call__(
        self,
        hidden_states,
        attention_mask,
        layer_head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
    ):
        # Self Attention
        attention_outputs = self.attention(
            hidden_states,
            attention_mask,
            layer_head_mask=layer_head_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
        )
        attention_output = attention_outputs[0]

        # Cross-Attention Block
        if encoder_hidden_states is not None:
            cross_attention_outputs = self.crossattention(
                attention_output,
                attention_mask=encoder_attention_mask,
                layer_head_mask=layer_head_mask,
                key_value_states=encoder_hidden_states,
                deterministic=deterministic,
                output_attentions=output_attentions,
            )
            attention_output = cross_attention_outputs[0]

        hidden_states = self.intermediate(attention_output)
        hidden_states = self.output(hidden_states, attention_output, deterministic=deterministic)

        outputs = (hidden_states,)

        if output_attentions:
            outputs += (attention_outputs[1],)
            if encoder_hidden_states is not None:
                outputs += (cross_attention_outputs[1],)
        return outputs


class FlaxBigBirdLayerCollection(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        if self.gradient_checkpointing:
            FlaxBigBirdCheckpointLayer = remat(FlaxBigBirdLayer, static_argnums=(5, 6, 7))
            self.layers = [
                FlaxBigBirdCheckpointLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]
        else:
            self.layers = [
                FlaxBigBirdLayer(self.config, layer_id=i, name=str(i), dtype=self.dtype)
                for i in range(self.config.num_hidden_layers)
            ]

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLayerCollection.__call__ with Bert->BigBird
    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        all_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None
        all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None

        # Check if head_mask has a correct number of layers specified if desired
        if head_mask is not None:
            if head_mask.shape[0] != (len(self.layers)):
                raise ValueError(
                    f"The head_mask should be specified for {len(self.layers)} layers, but it is for                  "
                    f"       {head_mask.shape[0]}."
                )

        for i, layer in enumerate(self.layers):
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            layer_outputs = layer(
                hidden_states,
                attention_mask,
                head_mask[i] if head_mask is not None else None,
                encoder_hidden_states,
                encoder_attention_mask,
                init_cache,
                deterministic,
                output_attentions,
            )

            hidden_states = layer_outputs[0]

            if output_attentions:
                all_attentions += (layer_outputs[1],)

                if encoder_hidden_states is not None:
                    all_cross_attentions += (layer_outputs[2],)

        if output_hidden_states:
            all_hidden_states += (hidden_states,)

        outputs = (hidden_states, all_hidden_states, all_attentions, all_cross_attentions)

        if not return_dict:
            return tuple(v for v in outputs if v is not None)

        return FlaxBaseModelOutputWithPastAndCrossAttentions(
            last_hidden_state=hidden_states,
            hidden_states=all_hidden_states,
            attentions=all_attentions,
            cross_attentions=all_cross_attentions,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertEncoder with Bert->BigBird
class FlaxBigBirdEncoder(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    gradient_checkpointing: bool = False

    def setup(self):
        self.layer = FlaxBigBirdLayerCollection(
            self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )

    def __call__(
        self,
        hidden_states,
        attention_mask,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        return self.layer(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPredictionHeadTransform with Bert->BigBird
class FlaxBigBirdPredictionHeadTransform(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
        self.activation = ACT2FN[self.config.hidden_act]
        self.LayerNorm = nn.LayerNorm(epsilon=self.config.layer_norm_eps, dtype=self.dtype)

    def __call__(self, hidden_states):
        hidden_states = self.dense(hidden_states)
        hidden_states = self.activation(hidden_states)
        return self.LayerNorm(hidden_states)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertLMPredictionHead with Bert->BigBird, np.ndarray->jnp.ndarray
class FlaxBigBirdLMPredictionHead(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    bias_init: Callable[..., jnp.ndarray] = jax.nn.initializers.zeros

    def setup(self):
        self.transform = FlaxBigBirdPredictionHeadTransform(self.config, dtype=self.dtype)
        self.decoder = nn.Dense(self.config.vocab_size, dtype=self.dtype, use_bias=False)
        self.bias = self.param("bias", self.bias_init, (self.config.vocab_size,))

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.transform(hidden_states)

        if shared_embedding is not None:
            hidden_states = self.decoder.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
        else:
            hidden_states = self.decoder(hidden_states)

        bias = jnp.asarray(self.bias, self.dtype)
        hidden_states += bias
        return hidden_states


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertOnlyMLMHead with Bert->BigBird
class FlaxBigBirdOnlyMLMHead(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.predictions = FlaxBigBirdLMPredictionHead(self.config, dtype=self.dtype)

    def __call__(self, hidden_states, shared_embedding=None):
        hidden_states = self.predictions(hidden_states, shared_embedding=shared_embedding)
        return hidden_states


class FlaxBigBirdPreTrainingHeads(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.predictions = FlaxBigBirdLMPredictionHead(self.config, dtype=self.dtype)
        self.seq_relationship = nn.Dense(2, dtype=self.dtype)

    def __call__(self, hidden_states, pooled_output, shared_embedding=None):
        prediction_scores = self.predictions(hidden_states, shared_embedding=shared_embedding)
        seq_relationship_score = self.seq_relationship(pooled_output)
        return prediction_scores, seq_relationship_score


class FlaxBigBirdPreTrainedModel(FlaxPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = BigBirdConfig
    base_model_prefix = "bert"
    module_class: nn.Module = None

    def __init__(
        self,
        config: BigBirdConfig,
        input_shape: Optional[tuple] = None,
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        gradient_checkpointing: bool = False,
        **kwargs,
    ):
        module = self.module_class(config=config, dtype=dtype, gradient_checkpointing=gradient_checkpointing, **kwargs)
        if config.attention_type == "block_sparse" and input_shape is None:
            input_shape = (1, 12 * config.block_size)
        elif input_shape is None:
            input_shape = (1, 1)

        super().__init__(config, module, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)

    # Copied from transformers.models.bert.modeling_flax_bert.FlaxBertPreTrainedModel.enable_gradient_checkpointing
    def enable_gradient_checkpointing(self):
        self._module = self.module_class(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=True,
        )

    def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
        # init input tensors
        input_ids = jnp.zeros(input_shape, dtype="i4")
        token_type_ids = jnp.zeros_like(input_ids)
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_shape)
        attention_mask = jnp.ones_like(input_ids)
        head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        params_rng, dropout_rng, indices_rng = jax.random.split(rng, num=3)
        rngs = {"params": params_rng, "dropout": dropout_rng, "indices": indices_rng}

        if self.config.add_cross_attention:
            encoder_hidden_states = jnp.zeros(input_shape + (self.config.hidden_size,))
            encoder_attention_mask = attention_mask
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                encoder_hidden_states,
                encoder_attention_mask,
                return_dict=False,
            )
        else:
            module_init_outputs = self.module.init(
                rngs,
                input_ids,
                attention_mask,
                token_type_ids,
                position_ids,
                head_mask,
                return_dict=False,
            )

        random_params = module_init_outputs["params"]

        if params is not None:
            random_params = flatten_dict(unfreeze(random_params))
            params = flatten_dict(unfreeze(params))
            for missing_key in self._missing_keys:
                params[missing_key] = random_params[missing_key]
            self._missing_keys = set()
            return freeze(unflatten_dict(params))
        else:
            return random_params

    # Copied from transformers.models.bart.modeling_flax_bart.FlaxBartDecoderPreTrainedModel.init_cache
    def init_cache(self, batch_size, max_length):
        r"""
        Args:
            batch_size (`int`):
                batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
            max_length (`int`):
                maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
                cache.
        """
        # init input variables to retrieve cache
        input_ids = jnp.ones((batch_size, max_length), dtype="i4")
        attention_mask = jnp.ones_like(input_ids, dtype="i4")
        position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        init_variables = self.module.init(
            jax.random.PRNGKey(0), input_ids, attention_mask, position_ids, return_dict=False, init_cache=True
        )
        return unfreeze(init_variables["cache"])

    @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        encoder_hidden_states=None,
        encoder_attention_mask=None,
        params: dict = None,
        dropout_rng: Optional[jax.random.PRNGKey] = None,
        indices_rng: Optional[jax.random.PRNGKey] = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        past_key_values: dict = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        # Handle any PRNG if needed
        rngs = {}
        if indices_rng is not None:
            rngs["indices"] = indices_rng

        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        inputs = {"params": params or self.params}

        if self.config.add_cross_attention:
            # if past_key_values are passed then cache is already initialized a private flag init_cache has to be passed
            # down to ensure cache is used. It has to be made sure that cache is marked as mutable so that it can be
            # changed by FlaxBigBirdAttention module
            if past_key_values:
                inputs["cache"] = past_key_values
                mutable = ["cache"]
            else:
                mutable = False

            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_attention_mask,
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
                mutable=mutable,
            )

            # add updated cache to model output
            if past_key_values is not None and return_dict:
                outputs, past_key_values = outputs
                outputs["past_key_values"] = unfreeze(past_key_values["cache"])
                return outputs
            elif past_key_values is not None and not return_dict:
                outputs, past_key_values = outputs
                outputs = outputs[:1] + (unfreeze(past_key_values["cache"]),) + outputs[1:]

        else:
            outputs = self.module.apply(
                inputs,
                jnp.array(input_ids, dtype="i4"),
                jnp.array(attention_mask, dtype="i4"),
                token_type_ids=jnp.array(token_type_ids, dtype="i4"),
                position_ids=jnp.array(position_ids, dtype="i4"),
                head_mask=jnp.array(head_mask, dtype="i4"),
                deterministic=not train,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
                rngs=rngs,
            )

        return outputs


class FlaxBigBirdModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
    add_pooling_layer: bool = True
    gradient_checkpointing: bool = False

    def setup(self):
        self.embeddings = FlaxBigBirdEmbeddings(self.config, dtype=self.dtype)
        self.encoder = FlaxBigBirdEncoder(
            self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.pooler = nn.Dense(
            self.config.hidden_size,
            kernel_init=jax.nn.initializers.normal(self.config.initializer_range),
            dtype=self.dtype,
        )

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        hidden_states = self.embeddings(
            input_ids, token_type_ids, position_ids, attention_mask, deterministic=deterministic
        )
        outputs = self.encoder(
            hidden_states,
            attention_mask,
            head_mask=head_mask,
            deterministic=deterministic,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )
        hidden_states = outputs[0]

        pooled = nn.tanh(self.pooler(hidden_states[:, 0, :])) if self.add_pooling_layer else None

        if not return_dict:
            # if pooled is None, don't return it
            if pooled is None:
                return (hidden_states,) + outputs[1:]
            return (hidden_states, pooled) + outputs[1:]

        return FlaxBaseModelOutputWithPoolingAndCrossAttentions(
            last_hidden_state=hidden_states,
            pooler_output=pooled,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    "The bare BigBird Model transformer outputting raw hidden-states without any specific head on top.",
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertModel with Bert->BigBird
class FlaxBigBirdModel(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdModule


append_call_sample_docstring(FlaxBigBirdModel, _CHECKPOINT_FOR_DOC, FlaxBaseModelOutputWithPooling, _CONFIG_FOR_DOC)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForPreTrainingModule with Bert->BigBird
class FlaxBigBirdForPreTrainingModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.cls = FlaxBigBirdPreTrainingHeads(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        if self.config.tie_word_embeddings:
            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        hidden_states = outputs[0]
        pooled_output = outputs[1]

        prediction_scores, seq_relationship_score = self.cls(
            hidden_states, pooled_output, shared_embedding=shared_embedding
        )

        if not return_dict:
            return (prediction_scores, seq_relationship_score) + outputs[2:]

        return FlaxBigBirdForPreTrainingOutput(
            prediction_logits=prediction_scores,
            seq_relationship_logits=seq_relationship_score,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with two heads on top as done during the pretraining: a `masked language modeling` head and a `next
    sentence prediction (classification)` head.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForPreTraining with Bert->BigBird
class FlaxBigBirdForPreTraining(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForPreTrainingModule


FLAX_BIG_BIRD_FOR_PRETRAINING_DOCSTRING = """
    Returns:

    Example:

    ```python
    >>> from transformers import AutoTokenizer, FlaxBigBirdForPreTraining

    >>> tokenizer = AutoTokenizer.from_pretrained("google/bigbird-roberta-base")
    >>> model = FlaxBigBirdForPreTraining.from_pretrained("google/bigbird-roberta-base")

    >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="np")
    >>> outputs = model(**inputs)

    >>> prediction_logits = outputs.prediction_logits
    >>> seq_relationship_logits = outputs.seq_relationship_logits
    ```
"""

overwrite_call_docstring(
    FlaxBigBirdForPreTraining,
    BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length") + FLAX_BIG_BIRD_FOR_PRETRAINING_DOCSTRING,
)
append_replace_return_docstrings(
    FlaxBigBirdForPreTraining, output_type=FlaxBigBirdForPreTrainingOutput, config_class=_CONFIG_FOR_DOC
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMaskedLMModule with Bert->BigBird
class FlaxBigBirdForMaskedLMModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.cls = FlaxBigBirdOnlyMLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.cls(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxMaskedLMOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings("""BigBird Model with a `language modeling` head on top.""", BIG_BIRD_START_DOCSTRING)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMaskedLM with Bert->BigBird
class FlaxBigBirdForMaskedLM(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForMaskedLMModule


append_call_sample_docstring(FlaxBigBirdForMaskedLM, _CHECKPOINT_FOR_DOC, FlaxMaskedLMOutput, _CONFIG_FOR_DOC)


class FlaxBigBirdClassificationHead(nn.Module):
    """Head for sentence-level classification tasks."""

    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dense = nn.Dense(self.config.hidden_size, dtype=self.dtype)
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.out_proj = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(self, features, deterministic=True):
        x = features[:, 0, :]  # take <s> token (equiv. to [CLS])
        x = self.dropout(x, deterministic=deterministic)
        x = self.dense(x)
        x = ACT2FN[self.config.hidden_act](x)
        x = self.dropout(x, deterministic=deterministic)
        x = self.out_proj(x)
        return x


class FlaxBigBirdForSequenceClassificationModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config, dtype=self.dtype, gradient_checkpointing=self.gradient_checkpointing
        )
        self.classifier = FlaxBigBirdClassificationHead(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = outputs[0]
        logits = self.classifier(sequence_output, deterministic=deterministic)

        if not return_dict:
            return (logits,) + outputs[2:]

        return FlaxSequenceClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model transformer with a sequence classification/regression head on top (a linear layer on top of the
    pooled output) e.g. for GLUE tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForSequenceClassification with Bert->BigBird
class FlaxBigBirdForSequenceClassification(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForSequenceClassificationModule


append_call_sample_docstring(
    FlaxBigBirdForSequenceClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxSequenceClassifierOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForMultipleChoiceModule with Bert->BigBird
class FlaxBigBirdForMultipleChoiceModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.classifier = nn.Dense(1, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        num_choices = input_ids.shape[1]
        input_ids = input_ids.reshape(-1, input_ids.shape[-1]) if input_ids is not None else None
        attention_mask = attention_mask.reshape(-1, attention_mask.shape[-1]) if attention_mask is not None else None
        token_type_ids = token_type_ids.reshape(-1, token_type_ids.shape[-1]) if token_type_ids is not None else None
        position_ids = position_ids.reshape(-1, position_ids.shape[-1]) if position_ids is not None else None

        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        pooled_output = outputs[1]
        pooled_output = self.dropout(pooled_output, deterministic=deterministic)
        logits = self.classifier(pooled_output)

        reshaped_logits = logits.reshape(-1, num_choices)

        if not return_dict:
            return (reshaped_logits,) + outputs[2:]

        return FlaxMultipleChoiceModelOutput(
            logits=reshaped_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a
    softmax) e.g. for RocStories/SWAG tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
class FlaxBigBirdForMultipleChoice(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForMultipleChoiceModule

    def __init__(
        self,
        config: BigBirdConfig,
        input_shape: Optional[tuple] = None,
        seed: int = 0,
        dtype: jnp.dtype = jnp.float32,
        _do_init: bool = True,
        **kwargs,
    ):
        if config.attention_type == "block_sparse" and input_shape is None:
            input_shape = (1, 1, 12 * config.block_size)
        elif input_shape is None:
            input_shape = (1, 1)
        super().__init__(config, input_shape=input_shape, seed=seed, dtype=dtype, _do_init=_do_init)


overwrite_call_docstring(
    FlaxBigBirdForMultipleChoice, BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length")
)
append_call_sample_docstring(
    FlaxBigBirdForMultipleChoice,
    _CHECKPOINT_FOR_DOC,
    FlaxMultipleChoiceModelOutput,
    _CONFIG_FOR_DOC,
)


# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassificationModule with Bert->BigBird
class FlaxBigBirdForTokenClassificationModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            dtype=self.dtype,
            add_pooling_layer=False,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        classifier_dropout = (
            self.config.classifier_dropout
            if self.config.classifier_dropout is not None
            else self.config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(rate=classifier_dropout)
        self.classifier = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        hidden_states = self.dropout(hidden_states, deterministic=deterministic)
        logits = self.classifier(hidden_states)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxTokenClassifierOutput(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
    Named-Entity-Recognition (NER) tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForTokenClassification with Bert->BigBird
class FlaxBigBirdForTokenClassification(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForTokenClassificationModule


append_call_sample_docstring(
    FlaxBigBirdForTokenClassification,
    _CHECKPOINT_FOR_DOC,
    FlaxTokenClassifierOutput,
    _CONFIG_FOR_DOC,
)


class FlaxBigBirdForQuestionAnsweringHead(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32

    def setup(self):
        self.dropout = nn.Dropout(rate=self.config.hidden_dropout_prob)
        self.intermediate = FlaxBigBirdIntermediate(self.config, dtype=self.dtype)
        self.output = FlaxBigBirdOutput(self.config, dtype=self.dtype)
        self.qa_outputs = nn.Dense(self.config.num_labels, dtype=self.dtype)

    def __call__(self, encoder_output, deterministic=True):
        hidden_states = self.dropout(encoder_output, deterministic=deterministic)
        hidden_states = self.intermediate(hidden_states)
        hidden_states = self.output(hidden_states, encoder_output)
        hidden_states = self.qa_outputs(hidden_states)
        return hidden_states


class FlaxBigBirdForQuestionAnsweringModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    add_pooling_layer: bool = False
    gradient_checkpointing: bool = False

    def setup(self):
        self.config.num_labels = 2
        self.bert = FlaxBigBirdModule(
            self.config,
            dtype=self.dtype,
            add_pooling_layer=self.add_pooling_layer,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.qa_classifier = FlaxBigBirdForQuestionAnsweringHead(self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        token_type_ids,
        position_ids,
        head_mask,
        logits_mask=None,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids,
            attention_mask,
            token_type_ids,
            position_ids,
            head_mask,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        pooled_output = outputs[1] if self.add_pooling_layer else None
        logits = self.qa_classifier(hidden_states, deterministic=deterministic)

        if logits_mask is not None:
            # removing question tokens from the competition
            logits = logits - logits_mask * 1e6

        start_logits, end_logits = logits.split(self.config.num_labels, axis=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)

        if not return_dict:
            return (start_logits, end_logits) + outputs[1:]

        return FlaxBigBirdForQuestionAnsweringModelOutput(
            start_logits=start_logits,
            end_logits=end_logits,
            pooled_output=pooled_output,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear
    layers on top of the hidden-states output to compute `span start logits` and `span end logits`).
    """,
    BIG_BIRD_START_DOCSTRING,
)
class FlaxBigBirdForQuestionAnswering(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForQuestionAnsweringModule

    @add_start_docstrings_to_model_forward(BIG_BIRD_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def __call__(
        self,
        input_ids,
        attention_mask=None,
        token_type_ids=None,
        position_ids=None,
        head_mask=None,
        question_lengths=None,
        params: dict = None,
        dropout_rng: Optional[jax.random.PRNGKey] = None,
        indices_rng: Optional[jax.random.PRNGKey] = None,
        train: bool = False,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ):
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        return_dict = return_dict if return_dict is not None else self.config.return_dict

        if position_ids is None:
            position_ids = jnp.broadcast_to(jnp.arange(jnp.atleast_2d(input_ids).shape[-1]), input_ids.shape)

        if attention_mask is None:
            attention_mask = jnp.ones_like(input_ids)

        if head_mask is None:
            head_mask = jnp.ones((self.config.num_hidden_layers, self.config.num_attention_heads))

        if question_lengths is None and input_ids is not None:
            # assuming input_ids format: <cls> <question> <sep> context <sep>
            question_lengths = jnp.argmax((input_ids == self.config.sep_token_id).astype("i4"), axis=-1) + 1
            question_lengths = jnp.expand_dims(question_lengths, axis=1)

        seqlen = input_ids.shape[1]

        logits_mask = None
        if question_lengths is not None:
            # setting lengths logits to `-inf`
            logits_mask = self.prepare_question_mask(question_lengths, seqlen)
            if token_type_ids is None:
                token_type_ids = (~logits_mask).astype("i4")
            logits_mask = jnp.expand_dims(logits_mask, axis=2)
            logits_mask = logits_mask.at[:, 0].set(False)

        # init input tensors if not passed
        if token_type_ids is None:
            token_type_ids = jnp.zeros_like(input_ids)

        # Handle any PRNG if needed
        rngs = {}
        if dropout_rng is not None:
            rngs["dropout"] = dropout_rng

        if indices_rng is not None:
            rngs["indices"] = indices_rng

        return self.module.apply(
            {"params": params or self.params},
            jnp.array(input_ids, dtype="i4"),
            jnp.array(attention_mask, dtype="i4"),
            token_type_ids,
            jnp.array(position_ids, dtype="i4"),
            jnp.array(head_mask, dtype="i4"),
            logits_mask,
            not train,
            output_attentions,
            output_hidden_states,
            return_dict,
            rngs=rngs,
        )

    @staticmethod
    def prepare_question_mask(q_lengths, maxlen: int):
        # q_lengths -> (bz, 1)
        mask = jnp.arange(0, maxlen)
        mask = jnp.expand_dims(mask, axis=0) < q_lengths
        return mask


append_call_sample_docstring(
    FlaxBigBirdForQuestionAnswering,
    _CHECKPOINT_FOR_DOC,
    FlaxBigBirdForQuestionAnsweringModelOutput,
    _CONFIG_FOR_DOC,
)


class FlaxBigBirdForCausalLMModule(nn.Module):
    config: BigBirdConfig
    dtype: jnp.dtype = jnp.float32
    gradient_checkpointing: bool = False

    def setup(self):
        self.bert = FlaxBigBirdModule(
            config=self.config,
            add_pooling_layer=False,
            dtype=self.dtype,
            gradient_checkpointing=self.gradient_checkpointing,
        )
        self.cls = FlaxBigBirdOnlyMLMHead(config=self.config, dtype=self.dtype)

    def __call__(
        self,
        input_ids,
        attention_mask,
        position_ids,
        token_type_ids: Optional[jnp.ndarray] = None,
        head_mask: Optional[jnp.ndarray] = None,
        encoder_hidden_states: Optional[jnp.ndarray] = None,
        encoder_attention_mask: Optional[jnp.ndarray] = None,
        init_cache: bool = False,
        deterministic: bool = True,
        output_attentions: bool = False,
        output_hidden_states: bool = False,
        return_dict: bool = True,
    ):
        # Model
        outputs = self.bert(
            input_ids=input_ids,
            attention_mask=attention_mask,
            token_type_ids=token_type_ids,
            position_ids=position_ids,
            head_mask=head_mask,
            encoder_hidden_states=encoder_hidden_states,
            encoder_attention_mask=encoder_attention_mask,
            init_cache=init_cache,
            deterministic=deterministic,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        hidden_states = outputs[0]
        if self.config.tie_word_embeddings:
            shared_embedding = self.bert.variables["params"]["embeddings"]["word_embeddings"]["embedding"]
        else:
            shared_embedding = None

        # Compute the prediction scores
        logits = self.cls(hidden_states, shared_embedding=shared_embedding)

        if not return_dict:
            return (logits,) + outputs[1:]

        return FlaxCausalLMOutputWithCrossAttentions(
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
            cross_attentions=outputs.cross_attentions,
        )


@add_start_docstrings(
    """
    BigBird Model with a language modeling head on top (a linear layer on top of the hidden-states output) e.g for
    autoregressive tasks.
    """,
    BIG_BIRD_START_DOCSTRING,
)
# Copied from transformers.models.bert.modeling_flax_bert.FlaxBertForCausalLM with Bert->BigBird
class FlaxBigBirdForCausalLM(FlaxBigBirdPreTrainedModel):
    module_class = FlaxBigBirdForCausalLMModule

    def prepare_inputs_for_generation(self, input_ids, max_length, attention_mask: Optional[jax.Array] = None):
        # initializing the cache
        batch_size, seq_length = input_ids.shape

        past_key_values = self.init_cache(batch_size, max_length)
        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
        # But since the decoder uses a causal mask, those positions are masked anyway.
        # Thus, we can create a single static attention_mask here, which is more efficient for compilation
        extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
        if attention_mask is not None:
            position_ids = attention_mask.cumsum(axis=-1) - 1
            extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, attention_mask, (0, 0))
        else:
            position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))

        return {
            "past_key_values": past_key_values,
            "attention_mask": extended_attention_mask,
            "position_ids": position_ids,
        }

    def update_inputs_for_generation(self, model_outputs, model_kwargs):
        model_kwargs["past_key_values"] = model_outputs.past_key_values
        model_kwargs["position_ids"] = model_kwargs["position_ids"][:, -1:] + 1
        return model_kwargs


append_call_sample_docstring(
    FlaxBigBirdForCausalLM,
    _CHECKPOINT_FOR_DOC,
    FlaxCausalLMOutputWithCrossAttentions,
    _CONFIG_FOR_DOC,
)
