# 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 2022 Facebook AI Research (FAIR) and The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" TensorFlow DeiT model."""


from __future__ import annotations

import collections.abc
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
    TFBaseModelOutput,
    TFBaseModelOutputWithPooling,
    TFImageClassifierOutput,
    TFMaskedImageModelingOutput,
)
from ...modeling_tf_utils import (
    TFPreTrainedModel,
    TFSequenceClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_deit import DeiTConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "DeiTConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224"
_EXPECTED_OUTPUT_SHAPE = [1, 198, 768]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/deit-base-distilled-patch16-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/deit-base-distilled-patch16-224",
    # See all DeiT models at https://huggingface.co/models?filter=deit
]


@dataclass
class TFDeiTForImageClassificationWithTeacherOutput(ModelOutput):
    """
    Output type of [`DeiTForImageClassificationWithTeacher`].

    Args:
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores as the average of the cls_logits and distillation logits.
        cls_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
            class token).
        distillation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
            distillation token).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (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.
    """

    logits: tf.Tensor = None
    cls_logits: tf.Tensor = None
    distillation_logits: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


class TFDeiTEmbeddings(tf.keras.layers.Layer):
    """
    Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token.
    """

    def __init__(self, config: DeiTConfig, use_mask_token: bool = False, **kwargs) -> None:
        super().__init__(**kwargs)
        self.config = config
        self.use_mask_token = use_mask_token
        self.patch_embeddings = TFDeiTPatchEmbeddings(config=config, name="patch_embeddings")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")

    def build(self, input_shape: tf.TensorShape):
        self.cls_token = self.add_weight(
            shape=(1, 1, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="cls_token",
        )
        self.distillation_token = self.add_weight(
            shape=(1, 1, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="distillation_token",
        )
        self.mask_token = None
        if self.use_mask_token:
            self.mask_token = self.add_weight(
                shape=(1, 1, self.config.hidden_size),
                initializer=tf.keras.initializers.zeros(),
                trainable=True,
                name="mask_token",
            )
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = self.add_weight(
            shape=(1, num_patches + 2, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="position_embeddings",
        )
        super().build(input_shape)

    def call(
        self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None, training: bool = False
    ) -> tf.Tensor:
        embeddings = self.patch_embeddings(pixel_values)
        batch_size, seq_length, _ = shape_list(embeddings)

        if bool_masked_pos is not None:
            mask_tokens = tf.tile(self.mask_token, [batch_size, seq_length, 1])
            # replace the masked visual tokens by mask_tokens
            mask = tf.expand_dims(bool_masked_pos, axis=-1)
            mask = tf.cast(mask, dtype=mask_tokens.dtype)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0)
        distillation_tokens = tf.repeat(self.distillation_token, repeats=batch_size, axis=0)
        embeddings = tf.concat((cls_tokens, distillation_tokens, embeddings), axis=1)
        embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings, training=training)
        return embeddings


class TFDeiTPatchEmbeddings(tf.keras.layers.Layer):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config: DeiTConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = tf.keras.layers.Conv2D(
            hidden_size, kernel_size=patch_size, strides=patch_size, name="projection"
        )

    def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
        batch_size, height, width, num_channels = shape_list(pixel_values)
        if tf.executing_eagerly() and num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if tf.executing_eagerly() and (height != self.image_size[0] or width != self.image_size[1]):
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
            )
        x = self.projection(pixel_values)
        batch_size, height, width, num_channels = shape_list(x)
        x = tf.reshape(x, (batch_size, height * width, num_channels))
        return x


# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->DeiT
class TFDeiTSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number "
                f"of attention heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)

        self.query = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)

    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))

        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
        return tf.transpose(tensor, perm=[0, 2, 1, 3])

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(inputs=hidden_states)
        mixed_key_layer = self.key(inputs=hidden_states)
        mixed_value_layer = self.value(inputs=hidden_states)
        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
        attention_scores = tf.divide(attention_scores, dk)

        # Normalize the attention scores to probabilities.
        attention_probs = stable_softmax(logits=attention_scores, axis=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(inputs=attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = tf.multiply(attention_probs, head_mask)

        attention_output = tf.matmul(attention_probs, value_layer)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])

        # (batch_size, seq_len_q, all_head_size)
        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->DeiT
class TFDeiTSelfOutput(tf.keras.layers.Layer):
    """
    The residual connection is defined in TFDeiTLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)

        return hidden_states


# Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->DeiT
class TFDeiTAttention(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.self_attention = TFDeiTSelfAttention(config, name="attention")
        self.dense_output = TFDeiTSelfOutput(config, name="output")

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(
        self,
        input_tensor: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(
            hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->DeiT
class TFDeiTIntermediate(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )

        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.intermediate_act_fn = config.hidden_act

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


# Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->DeiT
class TFDeiTOutput(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = hidden_states + input_tensor

        return hidden_states


class TFDeiTLayer(tf.keras.layers.Layer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.attention = TFDeiTAttention(config, name="attention")
        self.intermediate = TFDeiTIntermediate(config, name="intermediate")
        self.deit_output = TFDeiTOutput(config, name="output")

        self.layernorm_before = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_before"
        )
        self.layernorm_after = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_after"
        )

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        attention_outputs = self.attention(
            # in DeiT, layernorm is applied before self-attention
            input_tensor=self.layernorm_before(inputs=hidden_states, training=training),
            head_mask=head_mask,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = attention_outputs[0]

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in DeiT, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(inputs=hidden_states, training=training)

        intermediate_output = self.intermediate(hidden_states=layer_output, training=training)

        # second residual connection is done here
        layer_output = self.deit_output(
            hidden_states=intermediate_output, input_tensor=hidden_states, training=training
        )
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->DeiT
class TFDeiTEncoder(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.layer = [TFDeiTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

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

            layer_outputs = layer_module(
                hidden_states=hidden_states,
                head_mask=head_mask[i],
                output_attentions=output_attentions,
                training=training,
            )
            hidden_states = layer_outputs[0]

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

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)

        return TFBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


@keras_serializable
class TFDeiTMainLayer(tf.keras.layers.Layer):
    config_class = DeiTConfig

    def __init__(
        self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.config = config

        self.embeddings = TFDeiTEmbeddings(config, use_mask_token=use_mask_token, name="embeddings")
        self.encoder = TFDeiTEncoder(config, name="encoder")

        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
        self.pooler = TFDeiTPooler(config, name="pooler") if add_pooling_layer else None

    def get_input_embeddings(self) -> TFDeiTPatchEmbeddings:
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        raise NotImplementedError

    def get_head_mask(self, head_mask):
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        return head_mask

    @unpack_inputs
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor, ...]]:
        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.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        # TF 2.0 image layers can't use NCHW format when running on CPU.
        # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
        pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1))

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask)

        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos, training=training)

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output, training=training)
        pooled_output = self.pooler(sequence_output, training=training) if self.pooler is not None else None

        if not return_dict:
            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
            return head_outputs + encoder_outputs[1:]

        return TFBaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


# Copied from transformers.models.vit.modeling_tf_vit.TFViTPreTrainedModel with ViT->DeiT all-casing
class TFDeiTPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DeiTConfig
    base_model_prefix = "deit"
    main_input_name = "pixel_values"


DEIT_START_DOCSTRING = r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular
    TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.

    Parameters:
        config ([`DeiTConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

DEIT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`DeiTImageProcessor.__call__`] for details.

        head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

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

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.",
    DEIT_START_DOCSTRING,
)
class TFDeiTModel(TFDeiTPreTrainedModel):
    def __init__(
        self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(config, **kwargs)

        self.deit = TFDeiTMainLayer(
            config, add_pooling_layer=add_pooling_layer, use_mask_token=use_mask_token, name="deit"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[Tuple, TFBaseModelOutputWithPooling]:
        outputs = self.deit(
            pixel_values=pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTPooler with ViT->DeiT
class TFDeiTPooler(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="tanh",
            name="dense",
        )

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)

        return pooled_output


class TFDeitPixelShuffle(tf.keras.layers.Layer):
    """TF layer implementation of torch.nn.PixelShuffle"""

    def __init__(self, upscale_factor: int, **kwargs) -> None:
        super().__init__(**kwargs)
        if not isinstance(upscale_factor, int) or upscale_factor < 2:
            raise ValueError(f"upscale_factor must be an integer value >= 2 got {upscale_factor}")
        self.upscale_factor = upscale_factor

    def call(self, x: tf.Tensor) -> tf.Tensor:
        hidden_states = x
        batch_size, _, _, num_input_channels = shape_list(hidden_states)
        block_size_squared = self.upscale_factor**2
        output_depth = int(num_input_channels / block_size_squared)
        # When the number of output channels >= 2, PyTorch's PixelShuffle and
        # TF's depth_to_space differ in their output as the order of channels selected for combining
        # is a permutation of the other c.f.
        # https://stackoverflow.com/questions/68272502/tf-depth-to-space-not-same-as-torchs-pixelshuffle-when-output-channels-1
        permutation = tf.constant(
            [[i + j * block_size_squared for i in range(block_size_squared) for j in range(output_depth)]]
        )
        hidden_states = tf.gather(params=hidden_states, indices=tf.tile(permutation, [batch_size, 1]), batch_dims=-1)
        hidden_states = tf.nn.depth_to_space(hidden_states, block_size=self.upscale_factor, data_format="NHWC")
        return hidden_states


class TFDeitDecoder(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.conv2d = tf.keras.layers.Conv2D(
            filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, name="0"
        )
        self.pixel_shuffle = TFDeitPixelShuffle(config.encoder_stride, name="1")

    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = inputs
        hidden_states = self.conv2d(hidden_states)
        hidden_states = self.pixel_shuffle(hidden_states)
        return hidden_states


@add_start_docstrings(
    "DeiT Model with a decoder on top for masked image modeling, as proposed in"
    " [SimMIM](https://arxiv.org/abs/2111.09886).",
    DEIT_START_DOCSTRING,
)
class TFDeiTForMaskedImageModeling(TFDeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="deit")
        self.decoder = TFDeitDecoder(config, name="decoder")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFMaskedImageModelingOutput]:
        r"""
        bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).

        Returns:

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
        >>> model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool)

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 224, 224]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        # Reshape to (batch_size, num_channels, height, width)
        sequence_output = sequence_output[:, 1:-1]
        batch_size, sequence_length, num_channels = shape_list(sequence_output)
        height = width = int(sequence_length**0.5)
        sequence_output = tf.reshape(sequence_output, (batch_size, height, width, num_channels))

        # Reconstruct pixel values
        reconstructed_pixel_values = self.decoder(sequence_output, training=training)
        # TF 2.0 image layers can't use NCHW format when running on CPU, so intermediate layers use NHWC,
        # including the The decoder. We transpose to compute the loss against the pixel values
        # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
        reconstructed_pixel_values = tf.transpose(reconstructed_pixel_values, (0, 3, 1, 2))

        masked_im_loss = None
        if bool_masked_pos is not None:
            size = self.config.image_size // self.config.patch_size
            bool_masked_pos = tf.reshape(bool_masked_pos, (-1, size, size))
            mask = tf.repeat(bool_masked_pos, self.config.patch_size, 1)
            mask = tf.repeat(mask, self.config.patch_size, 2)
            mask = tf.expand_dims(mask, 1)
            mask = tf.cast(mask, tf.float32)

            reconstruction_loss = tf.keras.losses.mean_absolute_error(
                # Swap axes as metric calculation reduces over the final dimension
                tf.transpose(pixel_values, (1, 2, 3, 0)),
                tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)),
            )
            reconstruction_loss = tf.expand_dims(reconstruction_loss, 0)
            total_loss = tf.reduce_sum(reconstruction_loss * mask)
            num_masked_pixels = (tf.reduce_sum(mask) + 1e-5) * self.config.num_channels
            masked_im_loss = total_loss / num_masked_pixels
            masked_im_loss = tf.reshape(masked_im_loss, (1,))

        if not return_dict:
            output = (reconstructed_pixel_values,) + outputs[1:]
            return ((masked_im_loss,) + output) if masked_im_loss is not None else output

        return TFMaskedImageModelingOutput(
            loss=masked_im_loss,
            reconstruction=reconstructed_pixel_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.
    """,
    DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassification(TFDeiTPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: DeiTConfig):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")

        # Classifier head
        self.classifier = (
            tf.keras.layers.Dense(config.num_labels, name="classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="classifier")
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        labels: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tf.Tensor, TFImageClassifierOutput]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFDeiTForImageClassification
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> tf.keras.utils.set_random_seed(3)  # doctest: +IGNORE_RESULT
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here,
        >>> # so the head will be randomly initialized, hence the predictions will be random
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
        >>> model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> # model predicts one of the 1000 ImageNet classes
        >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
        >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
        Predicted class: little blue heron, Egretta caerulea
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        logits = self.classifier(sequence_output[:, 0, :])
        # we don't use the distillation token

        loss = None if labels is None else self.hf_compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return TFImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
    the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

    .. warning::

            This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
            supported.
    """,
    DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassificationWithTeacher(TFDeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")

        # Classifier heads
        self.cls_classifier = (
            tf.keras.layers.Dense(config.num_labels, name="cls_classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="cls_classifier")
        )
        self.distillation_classifier = (
            tf.keras.layers.Dense(config.num_labels, name="distillation_classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="distillation_classifier")
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=TFDeiTForImageClassificationWithTeacherOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFDeiTForImageClassificationWithTeacherOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        cls_logits = self.cls_classifier(sequence_output[:, 0, :])
        distillation_logits = self.distillation_classifier(sequence_output[:, 1, :])

        # during inference, return the average of both classifier predictions
        logits = (cls_logits + distillation_logits) / 2

        if not return_dict:
            output = (logits, cls_logits, distillation_logits) + outputs[1:]
            return output

        return TFDeiTForImageClassificationWithTeacherOutput(
            logits=logits,
            cls_logits=cls_logits,
            distillation_logits=distillation_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )

# coding=utf-8
# Copyright 2022 Facebook AI Research (FAIR) and The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" TensorFlow DeiT model."""


from __future__ import annotations

import collections.abc
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import get_tf_activation
from ...modeling_tf_outputs import (
    TFBaseModelOutput,
    TFBaseModelOutputWithPooling,
    TFImageClassifierOutput,
    TFMaskedImageModelingOutput,
)
from ...modeling_tf_utils import (
    TFPreTrainedModel,
    TFSequenceClassificationLoss,
    get_initializer,
    keras_serializable,
    unpack_inputs,
)
from ...tf_utils import shape_list, stable_softmax
from ...utils import (
    ModelOutput,
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from .configuration_deit import DeiTConfig


logger = logging.get_logger(__name__)

# General docstring
_CONFIG_FOR_DOC = "DeiTConfig"

# Base docstring
_CHECKPOINT_FOR_DOC = "facebook/deit-base-distilled-patch16-224"
_EXPECTED_OUTPUT_SHAPE = [1, 198, 768]

# Image classification docstring
_IMAGE_CLASS_CHECKPOINT = "facebook/deit-base-distilled-patch16-224"
_IMAGE_CLASS_EXPECTED_OUTPUT = "tabby, tabby cat"


TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "facebook/deit-base-distilled-patch16-224",
    # See all DeiT models at https://huggingface.co/models?filter=deit
]


@dataclass
class TFDeiTForImageClassificationWithTeacherOutput(ModelOutput):
    """
    Output type of [`DeiTForImageClassificationWithTeacher`].

    Args:
        logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores as the average of the cls_logits and distillation logits.
        cls_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores of the classification head (i.e. the linear layer on top of the final hidden state of the
            class token).
        distillation_logits (`tf.Tensor` of shape `(batch_size, config.num_labels)`):
            Prediction scores of the distillation head (i.e. the linear layer on top of the final hidden state of the
            distillation token).
        hidden_states (`tuple(tf.Tensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`):
            Tuple of `tf.Tensor` (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(tf.Tensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`):
            Tuple of `tf.Tensor` (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.
    """

    logits: tf.Tensor = None
    cls_logits: tf.Tensor = None
    distillation_logits: tf.Tensor = None
    hidden_states: Tuple[tf.Tensor] | None = None
    attentions: Tuple[tf.Tensor] | None = None


class TFDeiTEmbeddings(tf.keras.layers.Layer):
    """
    Construct the CLS token, distillation token, position and patch embeddings. Optionally, also the mask token.
    """

    def __init__(self, config: DeiTConfig, use_mask_token: bool = False, **kwargs) -> None:
        super().__init__(**kwargs)
        self.config = config
        self.use_mask_token = use_mask_token
        self.patch_embeddings = TFDeiTPatchEmbeddings(config=config, name="patch_embeddings")
        self.dropout = tf.keras.layers.Dropout(config.hidden_dropout_prob, name="dropout")

    def build(self, input_shape: tf.TensorShape):
        self.cls_token = self.add_weight(
            shape=(1, 1, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="cls_token",
        )
        self.distillation_token = self.add_weight(
            shape=(1, 1, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="distillation_token",
        )
        self.mask_token = None
        if self.use_mask_token:
            self.mask_token = self.add_weight(
                shape=(1, 1, self.config.hidden_size),
                initializer=tf.keras.initializers.zeros(),
                trainable=True,
                name="mask_token",
            )
        num_patches = self.patch_embeddings.num_patches
        self.position_embeddings = self.add_weight(
            shape=(1, num_patches + 2, self.config.hidden_size),
            initializer=tf.keras.initializers.zeros(),
            trainable=True,
            name="position_embeddings",
        )
        super().build(input_shape)

    def call(
        self, pixel_values: tf.Tensor, bool_masked_pos: tf.Tensor | None = None, training: bool = False
    ) -> tf.Tensor:
        embeddings = self.patch_embeddings(pixel_values)
        batch_size, seq_length, _ = shape_list(embeddings)

        if bool_masked_pos is not None:
            mask_tokens = tf.tile(self.mask_token, [batch_size, seq_length, 1])
            # replace the masked visual tokens by mask_tokens
            mask = tf.expand_dims(bool_masked_pos, axis=-1)
            mask = tf.cast(mask, dtype=mask_tokens.dtype)
            embeddings = embeddings * (1.0 - mask) + mask_tokens * mask

        cls_tokens = tf.repeat(self.cls_token, repeats=batch_size, axis=0)
        distillation_tokens = tf.repeat(self.distillation_token, repeats=batch_size, axis=0)
        embeddings = tf.concat((cls_tokens, distillation_tokens, embeddings), axis=1)
        embeddings = embeddings + self.position_embeddings
        embeddings = self.dropout(embeddings, training=training)
        return embeddings


class TFDeiTPatchEmbeddings(tf.keras.layers.Layer):
    """
    This class turns `pixel_values` of shape `(batch_size, num_channels, height, width)` into the initial
    `hidden_states` (patch embeddings) of shape `(batch_size, seq_length, hidden_size)` to be consumed by a
    Transformer.
    """

    def __init__(self, config: DeiTConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        image_size, patch_size = config.image_size, config.patch_size
        num_channels, hidden_size = config.num_channels, config.hidden_size

        image_size = image_size if isinstance(image_size, collections.abc.Iterable) else (image_size, image_size)
        patch_size = patch_size if isinstance(patch_size, collections.abc.Iterable) else (patch_size, patch_size)
        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.num_patches = num_patches

        self.projection = tf.keras.layers.Conv2D(
            hidden_size, kernel_size=patch_size, strides=patch_size, name="projection"
        )

    def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
        batch_size, height, width, num_channels = shape_list(pixel_values)
        if tf.executing_eagerly() and num_channels != self.num_channels:
            raise ValueError(
                "Make sure that the channel dimension of the pixel values match with the one set in the configuration."
            )
        if tf.executing_eagerly() and (height != self.image_size[0] or width != self.image_size[1]):
            raise ValueError(
                f"Input image size ({height}*{width}) doesn't match model ({self.image_size[0]}*{self.image_size[1]})."
            )
        x = self.projection(pixel_values)
        batch_size, height, width, num_channels = shape_list(x)
        x = tf.reshape(x, (batch_size, height * width, num_channels))
        return x


# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfAttention with ViT->DeiT
class TFDeiTSelfAttention(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        if config.hidden_size % config.num_attention_heads != 0:
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number "
                f"of attention heads ({config.num_attention_heads})"
            )

        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.sqrt_att_head_size = math.sqrt(self.attention_head_size)

        self.query = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="query"
        )
        self.key = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="key"
        )
        self.value = tf.keras.layers.Dense(
            units=self.all_head_size, kernel_initializer=get_initializer(config.initializer_range), name="value"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.attention_probs_dropout_prob)

    def transpose_for_scores(self, tensor: tf.Tensor, batch_size: int) -> tf.Tensor:
        # Reshape from [batch_size, seq_length, all_head_size] to [batch_size, seq_length, num_attention_heads, attention_head_size]
        tensor = tf.reshape(tensor=tensor, shape=(batch_size, -1, self.num_attention_heads, self.attention_head_size))

        # Transpose the tensor from [batch_size, seq_length, num_attention_heads, attention_head_size] to [batch_size, num_attention_heads, seq_length, attention_head_size]
        return tf.transpose(tensor, perm=[0, 2, 1, 3])

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        batch_size = shape_list(hidden_states)[0]
        mixed_query_layer = self.query(inputs=hidden_states)
        mixed_key_layer = self.key(inputs=hidden_states)
        mixed_value_layer = self.value(inputs=hidden_states)
        query_layer = self.transpose_for_scores(mixed_query_layer, batch_size)
        key_layer = self.transpose_for_scores(mixed_key_layer, batch_size)
        value_layer = self.transpose_for_scores(mixed_value_layer, batch_size)

        # Take the dot product between "query" and "key" to get the raw attention scores.
        # (batch size, num_heads, seq_len_q, seq_len_k)
        attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
        dk = tf.cast(self.sqrt_att_head_size, dtype=attention_scores.dtype)
        attention_scores = tf.divide(attention_scores, dk)

        # Normalize the attention scores to probabilities.
        attention_probs = stable_softmax(logits=attention_scores, axis=-1)

        # This is actually dropping out entire tokens to attend to, which might
        # seem a bit unusual, but is taken from the original Transformer paper.
        attention_probs = self.dropout(inputs=attention_probs, training=training)

        # Mask heads if we want to
        if head_mask is not None:
            attention_probs = tf.multiply(attention_probs, head_mask)

        attention_output = tf.matmul(attention_probs, value_layer)
        attention_output = tf.transpose(attention_output, perm=[0, 2, 1, 3])

        # (batch_size, seq_len_q, all_head_size)
        attention_output = tf.reshape(tensor=attention_output, shape=(batch_size, -1, self.all_head_size))
        outputs = (attention_output, attention_probs) if output_attentions else (attention_output,)

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTSelfOutput with ViT->DeiT
class TFDeiTSelfOutput(tf.keras.layers.Layer):
    """
    The residual connection is defined in TFDeiTLayer instead of here (as is the case with other models), due to the
    layernorm applied before each block.
    """

    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)

        return hidden_states


# Copied from transformers.models.vit.modeling_tf_vit.TFViTAttention with ViT->DeiT
class TFDeiTAttention(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.self_attention = TFDeiTSelfAttention(config, name="attention")
        self.dense_output = TFDeiTSelfOutput(config, name="output")

    def prune_heads(self, heads):
        raise NotImplementedError

    def call(
        self,
        input_tensor: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        self_outputs = self.self_attention(
            hidden_states=input_tensor, head_mask=head_mask, output_attentions=output_attentions, training=training
        )
        attention_output = self.dense_output(
            hidden_states=self_outputs[0], input_tensor=input_tensor, training=training
        )
        outputs = (attention_output,) + self_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTIntermediate with ViT->DeiT
class TFDeiTIntermediate(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.intermediate_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )

        if isinstance(config.hidden_act, str):
            self.intermediate_act_fn = get_tf_activation(config.hidden_act)
        else:
            self.intermediate_act_fn = config.hidden_act

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)

        return hidden_states


# Copied from transformers.models.vit.modeling_tf_vit.TFViTOutput with ViT->DeiT
class TFDeiTOutput(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size, kernel_initializer=get_initializer(config.initializer_range), name="dense"
        )
        self.dropout = tf.keras.layers.Dropout(rate=config.hidden_dropout_prob)

    def call(self, hidden_states: tf.Tensor, input_tensor: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = self.dense(inputs=hidden_states)
        hidden_states = self.dropout(inputs=hidden_states, training=training)
        hidden_states = hidden_states + input_tensor

        return hidden_states


class TFDeiTLayer(tf.keras.layers.Layer):
    """This corresponds to the Block class in the timm implementation."""

    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.attention = TFDeiTAttention(config, name="attention")
        self.intermediate = TFDeiTIntermediate(config, name="intermediate")
        self.deit_output = TFDeiTOutput(config, name="output")

        self.layernorm_before = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_before"
        )
        self.layernorm_after = tf.keras.layers.LayerNormalization(
            epsilon=config.layer_norm_eps, name="layernorm_after"
        )

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        training: bool = False,
    ) -> Tuple[tf.Tensor]:
        attention_outputs = self.attention(
            # in DeiT, layernorm is applied before self-attention
            input_tensor=self.layernorm_before(inputs=hidden_states, training=training),
            head_mask=head_mask,
            output_attentions=output_attentions,
            training=training,
        )
        attention_output = attention_outputs[0]

        # first residual connection
        hidden_states = attention_output + hidden_states

        # in DeiT, layernorm is also applied after self-attention
        layer_output = self.layernorm_after(inputs=hidden_states, training=training)

        intermediate_output = self.intermediate(hidden_states=layer_output, training=training)

        # second residual connection is done here
        layer_output = self.deit_output(
            hidden_states=intermediate_output, input_tensor=hidden_states, training=training
        )
        outputs = (layer_output,) + attention_outputs[1:]  # add attentions if we output them

        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTEncoder with ViT->DeiT
class TFDeiTEncoder(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.layer = [TFDeiTLayer(config, name=f"layer_._{i}") for i in range(config.num_hidden_layers)]

    def call(
        self,
        hidden_states: tf.Tensor,
        head_mask: tf.Tensor,
        output_attentions: bool,
        output_hidden_states: bool,
        return_dict: bool,
        training: bool = False,
    ) -> Union[TFBaseModelOutput, Tuple[tf.Tensor]]:
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None

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

            layer_outputs = layer_module(
                hidden_states=hidden_states,
                head_mask=head_mask[i],
                output_attentions=output_attentions,
                training=training,
            )
            hidden_states = layer_outputs[0]

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

        # Add last layer
        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, all_hidden_states, all_attentions] if v is not None)

        return TFBaseModelOutput(
            last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_attentions
        )


@keras_serializable
class TFDeiTMainLayer(tf.keras.layers.Layer):
    config_class = DeiTConfig

    def __init__(
        self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(**kwargs)
        self.config = config

        self.embeddings = TFDeiTEmbeddings(config, use_mask_token=use_mask_token, name="embeddings")
        self.encoder = TFDeiTEncoder(config, name="encoder")

        self.layernorm = tf.keras.layers.LayerNormalization(epsilon=config.layer_norm_eps, name="layernorm")
        self.pooler = TFDeiTPooler(config, name="pooler") if add_pooling_layer else None

    def get_input_embeddings(self) -> TFDeiTPatchEmbeddings:
        return self.embeddings.patch_embeddings

    def _prune_heads(self, heads_to_prune):
        """
        Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base
        class PreTrainedModel
        """
        raise NotImplementedError

    def get_head_mask(self, head_mask):
        if head_mask is not None:
            raise NotImplementedError
        else:
            head_mask = [None] * self.config.num_hidden_layers

        return head_mask

    @unpack_inputs
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor, ...]]:
        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.use_return_dict

        if pixel_values is None:
            raise ValueError("You have to specify pixel_values")

        # TF 2.0 image layers can't use NCHW format when running on CPU.
        # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
        pixel_values = tf.transpose(pixel_values, (0, 2, 3, 1))

        # Prepare head mask if needed
        # 1.0 in head_mask indicate we keep the head
        # attention_probs has shape bsz x n_heads x N x N
        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
        head_mask = self.get_head_mask(head_mask)

        embedding_output = self.embeddings(pixel_values, bool_masked_pos=bool_masked_pos, training=training)

        encoder_outputs = self.encoder(
            embedding_output,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output, training=training)
        pooled_output = self.pooler(sequence_output, training=training) if self.pooler is not None else None

        if not return_dict:
            head_outputs = (sequence_output, pooled_output) if pooled_output is not None else (sequence_output,)
            return head_outputs + encoder_outputs[1:]

        return TFBaseModelOutputWithPooling(
            last_hidden_state=sequence_output,
            pooler_output=pooled_output,
            hidden_states=encoder_outputs.hidden_states,
            attentions=encoder_outputs.attentions,
        )


# Copied from transformers.models.vit.modeling_tf_vit.TFViTPreTrainedModel with ViT->DeiT all-casing
class TFDeiTPreTrainedModel(TFPreTrainedModel):
    """
    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
    models.
    """

    config_class = DeiTConfig
    base_model_prefix = "deit"
    main_input_name = "pixel_values"


DEIT_START_DOCSTRING = r"""
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer). Use it as a regular
    TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and behavior.

    Parameters:
        config ([`DeiTConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

DEIT_INPUTS_DOCSTRING = r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`DeiTImageProcessor.__call__`] for details.

        head_mask (`tf.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

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

        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""


@add_start_docstrings(
    "The bare DeiT Model transformer outputting raw hidden-states without any specific head on top.",
    DEIT_START_DOCSTRING,
)
class TFDeiTModel(TFDeiTPreTrainedModel):
    def __init__(
        self, config: DeiTConfig, add_pooling_layer: bool = True, use_mask_token: bool = False, **kwargs
    ) -> None:
        super().__init__(config, **kwargs)

        self.deit = TFDeiTMainLayer(
            config, add_pooling_layer=add_pooling_layer, use_mask_token=use_mask_token, name="deit"
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=TFBaseModelOutputWithPooling,
        config_class=_CONFIG_FOR_DOC,
        modality="vision",
        expected_output=_EXPECTED_OUTPUT_SHAPE,
    )
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[Tuple, TFBaseModelOutputWithPooling]:
        outputs = self.deit(
            pixel_values=pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )
        return outputs


# Copied from transformers.models.vit.modeling_tf_vit.TFViTPooler with ViT->DeiT
class TFDeiTPooler(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs):
        super().__init__(**kwargs)

        self.dense = tf.keras.layers.Dense(
            units=config.hidden_size,
            kernel_initializer=get_initializer(config.initializer_range),
            activation="tanh",
            name="dense",
        )

    def call(self, hidden_states: tf.Tensor) -> tf.Tensor:
        # We "pool" the model by simply taking the hidden state corresponding
        # to the first token.
        first_token_tensor = hidden_states[:, 0]
        pooled_output = self.dense(inputs=first_token_tensor)

        return pooled_output


class TFDeitPixelShuffle(tf.keras.layers.Layer):
    """TF layer implementation of torch.nn.PixelShuffle"""

    def __init__(self, upscale_factor: int, **kwargs) -> None:
        super().__init__(**kwargs)
        if not isinstance(upscale_factor, int) or upscale_factor < 2:
            raise ValueError(f"upscale_factor must be an integer value >= 2 got {upscale_factor}")
        self.upscale_factor = upscale_factor

    def call(self, x: tf.Tensor) -> tf.Tensor:
        hidden_states = x
        batch_size, _, _, num_input_channels = shape_list(hidden_states)
        block_size_squared = self.upscale_factor**2
        output_depth = int(num_input_channels / block_size_squared)
        # When the number of output channels >= 2, PyTorch's PixelShuffle and
        # TF's depth_to_space differ in their output as the order of channels selected for combining
        # is a permutation of the other c.f.
        # https://stackoverflow.com/questions/68272502/tf-depth-to-space-not-same-as-torchs-pixelshuffle-when-output-channels-1
        permutation = tf.constant(
            [[i + j * block_size_squared for i in range(block_size_squared) for j in range(output_depth)]]
        )
        hidden_states = tf.gather(params=hidden_states, indices=tf.tile(permutation, [batch_size, 1]), batch_dims=-1)
        hidden_states = tf.nn.depth_to_space(hidden_states, block_size=self.upscale_factor, data_format="NHWC")
        return hidden_states


class TFDeitDecoder(tf.keras.layers.Layer):
    def __init__(self, config: DeiTConfig, **kwargs) -> None:
        super().__init__(**kwargs)
        self.conv2d = tf.keras.layers.Conv2D(
            filters=config.encoder_stride**2 * config.num_channels, kernel_size=1, name="0"
        )
        self.pixel_shuffle = TFDeitPixelShuffle(config.encoder_stride, name="1")

    def call(self, inputs: tf.Tensor, training: bool = False) -> tf.Tensor:
        hidden_states = inputs
        hidden_states = self.conv2d(hidden_states)
        hidden_states = self.pixel_shuffle(hidden_states)
        return hidden_states


@add_start_docstrings(
    "DeiT Model with a decoder on top for masked image modeling, as proposed in"
    " [SimMIM](https://arxiv.org/abs/2111.09886).",
    DEIT_START_DOCSTRING,
)
class TFDeiTForMaskedImageModeling(TFDeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, use_mask_token=True, name="deit")
        self.decoder = TFDeitDecoder(config, name="decoder")

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFMaskedImageModelingOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        bool_masked_pos: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFMaskedImageModelingOutput]:
        r"""
        bool_masked_pos (`tf.Tensor` of type bool and shape `(batch_size, num_patches)`):
            Boolean masked positions. Indicates which patches are masked (1) and which aren't (0).

        Returns:

        Examples:
        ```python
        >>> from transformers import AutoImageProcessor, TFDeiTForMaskedImageModeling
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
        >>> model = TFDeiTForMaskedImageModeling.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> num_patches = (model.config.image_size // model.config.patch_size) ** 2
        >>> pixel_values = image_processor(images=image, return_tensors="tf").pixel_values
        >>> # create random boolean mask of shape (batch_size, num_patches)
        >>> bool_masked_pos = tf.cast(tf.random.uniform((1, num_patches), minval=0, maxval=2, dtype=tf.int32), tf.bool)

        >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
        >>> loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
        >>> list(reconstructed_pixel_values.shape)
        [1, 3, 224, 224]
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            bool_masked_pos=bool_masked_pos,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        # Reshape to (batch_size, num_channels, height, width)
        sequence_output = sequence_output[:, 1:-1]
        batch_size, sequence_length, num_channels = shape_list(sequence_output)
        height = width = int(sequence_length**0.5)
        sequence_output = tf.reshape(sequence_output, (batch_size, height, width, num_channels))

        # Reconstruct pixel values
        reconstructed_pixel_values = self.decoder(sequence_output, training=training)
        # TF 2.0 image layers can't use NCHW format when running on CPU, so intermediate layers use NHWC,
        # including the The decoder. We transpose to compute the loss against the pixel values
        # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
        reconstructed_pixel_values = tf.transpose(reconstructed_pixel_values, (0, 3, 1, 2))

        masked_im_loss = None
        if bool_masked_pos is not None:
            size = self.config.image_size // self.config.patch_size
            bool_masked_pos = tf.reshape(bool_masked_pos, (-1, size, size))
            mask = tf.repeat(bool_masked_pos, self.config.patch_size, 1)
            mask = tf.repeat(mask, self.config.patch_size, 2)
            mask = tf.expand_dims(mask, 1)
            mask = tf.cast(mask, tf.float32)

            reconstruction_loss = tf.keras.losses.mean_absolute_error(
                # Swap axes as metric calculation reduces over the final dimension
                tf.transpose(pixel_values, (1, 2, 3, 0)),
                tf.transpose(reconstructed_pixel_values, (1, 2, 3, 0)),
            )
            reconstruction_loss = tf.expand_dims(reconstruction_loss, 0)
            total_loss = tf.reduce_sum(reconstruction_loss * mask)
            num_masked_pixels = (tf.reduce_sum(mask) + 1e-5) * self.config.num_channels
            masked_im_loss = total_loss / num_masked_pixels
            masked_im_loss = tf.reshape(masked_im_loss, (1,))

        if not return_dict:
            output = (reconstructed_pixel_values,) + outputs[1:]
            return ((masked_im_loss,) + output) if masked_im_loss is not None else output

        return TFMaskedImageModelingOutput(
            loss=masked_im_loss,
            reconstruction=reconstructed_pixel_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DeiT Model transformer with an image classification head on top (a linear layer on top of the final hidden state of
    the [CLS] token) e.g. for ImageNet.
    """,
    DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassification(TFDeiTPreTrainedModel, TFSequenceClassificationLoss):
    def __init__(self, config: DeiTConfig):
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")

        # Classifier head
        self.classifier = (
            tf.keras.layers.Dense(config.num_labels, name="classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="classifier")
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @replace_return_docstrings(output_type=TFImageClassifierOutput, config_class=_CONFIG_FOR_DOC)
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        labels: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tf.Tensor, TFImageClassifierOutput]:
        r"""
        labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).

        Returns:

        Examples:

        ```python
        >>> from transformers import AutoImageProcessor, TFDeiTForImageClassification
        >>> import tensorflow as tf
        >>> from PIL import Image
        >>> import requests

        >>> tf.keras.utils.set_random_seed(3)  # doctest: +IGNORE_RESULT
        >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
        >>> image = Image.open(requests.get(url, stream=True).raw)

        >>> # note: we are loading a TFDeiTForImageClassificationWithTeacher from the hub here,
        >>> # so the head will be randomly initialized, hence the predictions will be random
        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224")
        >>> model = TFDeiTForImageClassification.from_pretrained("facebook/deit-base-distilled-patch16-224")

        >>> inputs = image_processor(images=image, return_tensors="tf")
        >>> outputs = model(**inputs)
        >>> logits = outputs.logits
        >>> # model predicts one of the 1000 ImageNet classes
        >>> predicted_class_idx = tf.math.argmax(logits, axis=-1)[0]
        >>> print("Predicted class:", model.config.id2label[int(predicted_class_idx)])
        Predicted class: little blue heron, Egretta caerulea
        ```"""
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        logits = self.classifier(sequence_output[:, 0, :])
        # we don't use the distillation token

        loss = None if labels is None else self.hf_compute_loss(labels, logits)

        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output

        return TFImageClassifierOutput(
            loss=loss,
            logits=logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )


@add_start_docstrings(
    """
    DeiT Model transformer with image classification heads on top (a linear layer on top of the final hidden state of
    the [CLS] token and a linear layer on top of the final hidden state of the distillation token) e.g. for ImageNet.

    .. warning::

            This model supports inference-only. Fine-tuning with distillation (i.e. with a teacher) is not yet
            supported.
    """,
    DEIT_START_DOCSTRING,
)
class TFDeiTForImageClassificationWithTeacher(TFDeiTPreTrainedModel):
    def __init__(self, config: DeiTConfig) -> None:
        super().__init__(config)

        self.num_labels = config.num_labels
        self.deit = TFDeiTMainLayer(config, add_pooling_layer=False, name="deit")

        # Classifier heads
        self.cls_classifier = (
            tf.keras.layers.Dense(config.num_labels, name="cls_classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="cls_classifier")
        )
        self.distillation_classifier = (
            tf.keras.layers.Dense(config.num_labels, name="distillation_classifier")
            if config.num_labels > 0
            else tf.keras.layers.Activation("linear", name="distillation_classifier")
        )

    @unpack_inputs
    @add_start_docstrings_to_model_forward(DEIT_INPUTS_DOCSTRING)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,
        output_type=TFDeiTForImageClassificationWithTeacherOutput,
        config_class=_CONFIG_FOR_DOC,
        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
    )
    def call(
        self,
        pixel_values: tf.Tensor | None = None,
        head_mask: tf.Tensor | None = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        training: bool = False,
    ) -> Union[tuple, TFDeiTForImageClassificationWithTeacherOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        outputs = self.deit(
            pixel_values,
            head_mask=head_mask,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            training=training,
        )

        sequence_output = outputs[0]

        cls_logits = self.cls_classifier(sequence_output[:, 0, :])
        distillation_logits = self.distillation_classifier(sequence_output[:, 1, :])

        # during inference, return the average of both classifier predictions
        logits = (cls_logits + distillation_logits) / 2

        if not return_dict:
            output = (logits, cls_logits, distillation_logits) + outputs[1:]
            return output

        return TFDeiTForImageClassificationWithTeacherOutput(
            logits=logits,
            cls_logits=cls_logits,
            distillation_logits=distillation_logits,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions,
        )
