# What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
Deprecate models. # What does this PR do?

This PR creates the precedent of deprecating models in the library. By deprecating we indicate that we will stop maintaining such models, but there is no intention of actually removing those models and breaking support for them (they might one day move into a separate repo/on the Hub but we would still add the necessary imports to make sure backward compatibility stays). The main point is that we stop testing those models (to ease a bit the burden on our CI).

Deprecated models are moved in models/deprecated so direct import of objects from their modeling files will break (though that's easily fixed by adding the `.deprecated` in the path). They are removed from the `tests` folder and a mention is added in the doc page of the model.

The heuristic to pick the deprecated models in this PR is: models older than a year that got less than a cumulated 1,000 downloads (over all checkpoints) in the last 30 days (counting deduplicated downloads).
# Copyright 2021 The HuggingFace 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.
import copy
import dataclasses
import warnings
from abc import ABC, abstractmethod
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union

import numpy as np
from packaging import version

from ..utils import TensorType, is_torch_available, is_vision_available, logging
from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size


if TYPE_CHECKING:
    from ..configuration_utils import PretrainedConfig
    from ..feature_extraction_utils import FeatureExtractionMixin
    from ..image_processing_utils import ImageProcessingMixin
    from ..tokenization_utils_base import PreTrainedTokenizerBase


if is_vision_available():
    from PIL import Image

logger = logging.get_logger(__name__)


DEFAULT_ONNX_OPSET = 11

# 2 Gb
EXTERNAL_DATA_FORMAT_SIZE_LIMIT = 2 * 1024 * 1024 * 1024


@dataclasses.dataclass
class PatchingSpec:
    """
    Data class that holds patching specifications.

    Args:
        o: Module / object where the op to patch is located
        name: Name of the op to monkey patch
        custom_op: Custom op that patches the original op
        orig_op: Original op that is being patched
        op_wrapper: Wrapper (optional) that wraps both the original and custom ops.
            It is useful for ops that are class or static methods for instance.
    """

    o: Any
    name: str
    custom_op: Callable
    orig_op: Optional[Callable] = None
    op_wrapper: Optional[Callable] = None


class OnnxConfig(ABC):
    """
    Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
    """

    default_fixed_batch = 2
    default_fixed_sequence = 8
    default_fixed_num_choices = 4
    torch_onnx_minimum_version = version.parse("1.8")
    _tasks_to_common_outputs = {
        "causal-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "default": OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}}),
        "image-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "image-segmentation": OrderedDict(
            {
                "logits": {0: "batch", 1: "sequence"},
                "pred_boxes": {0: "batch", 1: "sequence"},
                "pred_masks": {0: "batch", 1: "sequence"},
            }
        ),
        "masked-im": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "masked-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "multiple-choice": OrderedDict({"logits": {0: "batch"}}),
        "object-detection": OrderedDict(
            {
                "logits": {0: "batch", 1: "sequence"},
                "pred_boxes": {0: "batch", 1: "sequence"},
            }
        ),
        "question-answering": OrderedDict(
            {
                "start_logits": {0: "batch", 1: "sequence"},
                "end_logits": {0: "batch", 1: "sequence"},
            }
        ),
        "semantic-segmentation": OrderedDict({"logits": {0: "batch", 1: "num_labels", 2: "height", 3: "width"}}),
        "seq2seq-lm": OrderedDict({"logits": {0: "batch", 1: "decoder_sequence"}}),
        "sequence-classification": OrderedDict({"logits": {0: "batch"}}),
        "token-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "vision2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "speech2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
    }

    def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None):
        self._config = config

        if task not in self._tasks_to_common_outputs:
            raise ValueError(
                f"{task} is not a supported task, supported tasks: {self._tasks_to_common_outputs.keys()}"
            )
        self.task = task

        self._patching_specs = []
        for spec in patching_specs if patching_specs is not None else []:
            final_spec = spec
            if spec.orig_op is None:
                final_spec = dataclasses.replace(spec, orig_op=getattr(spec.o, spec.name))
            self._patching_specs.append(final_spec)

    @classmethod
    def from_model_config(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfig":
        """
        Instantiate a OnnxConfig for a specific model

        Args:
            config: The model's configuration to use when exporting to ONNX

        Returns:
            OnnxConfig for this model
        """
        return cls(config, task=task)

    @property
    @abstractmethod
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        """
        Mapping containing the axis definition of the input tensors to provide to the model

        Returns:
            For each input: its name associated to the axes symbolic name and the axis position within the tensor
        """
        raise NotImplementedError()

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        """
        Mapping containing the axis definition of the output tensors to provide to the model

        Returns:
            For each output: its name associated to the axes symbolic name and the axis position within the tensor
        """
        common_outputs = self._tasks_to_common_outputs[self.task]
        return copy.deepcopy(common_outputs)

    @property
    def values_override(self) -> Optional[Mapping[str, Any]]:
        """
        Dictionary of keys to override in the model's config before exporting

        Returns:
            Dictionary with the keys (and their corresponding values) to override
        """
        if hasattr(self._config, "use_cache"):
            return {"use_cache": False}

        return None

    @property
    def default_batch_size(self) -> int:
        """
        The default batch size to use if no other indication

        Returns:
            Integer > 0
        """
        # Using 2 avoid ONNX making assumption about single sample batch
        return OnnxConfig.default_fixed_batch

    @property
    def default_sequence_length(self) -> int:
        """
        The default sequence length to use if no other indication

        Returns:
            Integer > 0
        """
        return OnnxConfig.default_fixed_sequence

    @property
    def default_num_choices(self) -> int:
        """
        The default number of choices to use if no other indication

        Returns:
            Integer > 0
        """
        return OnnxConfig.default_fixed_num_choices

    @property
    def default_onnx_opset(self) -> int:
        """
        Which onnx opset to use when exporting the model

        Returns:
            Integer ONNX Opset version
        """
        return DEFAULT_ONNX_OPSET

    @property
    def atol_for_validation(self) -> float:
        """
        What absolute tolerance value to use during model conversion validation.

        Returns:
            Float absolute tolerance value.
        """
        return 1e-5

    @property
    def is_torch_support_available(self) -> bool:
        """
        The minimum PyTorch version required to export the model.

        Returns:
            `bool`: Whether the installed version of PyTorch is compatible with the model.
        """
        if is_torch_available():
            from transformers.utils import get_torch_version

            return version.parse(get_torch_version()) >= self.torch_onnx_minimum_version
        else:
            return False

    @staticmethod
    def use_external_data_format(num_parameters: int) -> bool:
        """
        Flag indicating if the model requires using external data format

        Args:
            num_parameters: Number of parameter on the model

        Returns:
            True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise
        """

        return (
            compute_serialized_parameters_size(num_parameters, ParameterFormat.Float)
            >= EXTERNAL_DATA_FORMAT_SIZE_LIMIT
        )

    def _generate_dummy_images(
        self, batch_size: int = 2, num_channels: int = 3, image_height: int = 40, image_width: int = 40
    ):
        images = []
        for _ in range(batch_size):
            data = np.random.rand(image_height, image_width, num_channels) * 255
            images.append(Image.fromarray(data.astype("uint8")).convert("RGB"))
        return images

    def _generate_dummy_audio(
        self, batch_size: int = 2, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220
    ):
        audio_data = []
        for _ in range(batch_size):
            # time variable
            t = np.linspace(0, time_duration, int(time_duration * sampling_rate), endpoint=False)

            # generate pure sine wave at `frequency` Hz
            audio_data.append(0.5 * np.sin(2 * np.pi * frequency * t))

        return audio_data

    def generate_dummy_inputs(
        self,
        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"],
        batch_size: int = -1,
        seq_length: int = -1,
        num_choices: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
        num_channels: int = 3,
        image_width: int = 40,
        image_height: int = 40,
        sampling_rate: int = 22050,
        time_duration: float = 5.0,
        frequency: int = 220,
        tokenizer: "PreTrainedTokenizerBase" = None,
    ) -> Mapping[str, Any]:
        """
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]):
                The preprocessor associated with this model configuration.
            batch_size (`int`, *optional*, defaults to -1):
                The batch size to export the model for (-1 means dynamic axis).
            num_choices (`int`, *optional*, defaults to -1):
                The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
            seq_length (`int`, *optional*, defaults to -1):
                The sequence length to export the model for (-1 means dynamic axis).
            is_pair (`bool`, *optional*, defaults to `False`):
                Indicate if the input is a pair (sentence 1, sentence 2)
            framework (`TensorType`, *optional*, defaults to `None`):
                The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
            num_channels (`int`, *optional*, defaults to 3):
                The number of channels of the generated images.
            image_width (`int`, *optional*, defaults to 40):
                The width of the generated images.
            image_height (`int`, *optional*, defaults to 40):
                The height of the generated images.
            sampling_rate (`int`, *optional* defaults to 22050)
                The sampling rate for audio data generation.
            time_duration (`float`, *optional* defaults to 5.0)
                Total seconds of sampling for audio data generation.
            frequency (`int`, *optional* defaults to 220)
                The desired natural frequency of generated audio.

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        """
        from ..feature_extraction_utils import FeatureExtractionMixin
        from ..image_processing_utils import ImageProcessingMixin
        from ..tokenization_utils_base import PreTrainedTokenizerBase

        if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
            raise ValueError("You cannot provide both a tokenizer and a preprocessor to generate dummy inputs.")
        if tokenizer is not None:
            warnings.warn(
                "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use"
                " `preprocessor` instead.",
                FutureWarning,
            )
            logger.warning("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.")
            preprocessor = tokenizer
        if isinstance(preprocessor, PreTrainedTokenizerBase):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(
                batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
            )
            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            token_to_add = preprocessor.num_special_tokens_to_add(is_pair)
            seq_length = compute_effective_axis_dimension(
                seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
            )
            # Generate dummy inputs according to compute batch and sequence
            input_token = (
                preprocessor.unk_token
                if (preprocessor.unk_token is not None and len(preprocessor.unk_token) > 0)
                else "0"
            )
            dummy_input = [" ".join([input_token]) * seq_length] * batch_size
            if self.task == "multiple-choice":
                # If dynamic axis (-1) we forward with a fixed dimension of 4 candidate answers to avoid optimizations
                # made by ONNX
                num_choices = compute_effective_axis_dimension(
                    num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0
                )
                dummy_input = dummy_input * num_choices
                # The shape of the tokenized inputs values is [batch_size * num_choices, seq_length]
                tokenized_input = preprocessor(dummy_input, text_pair=dummy_input)
                # Unflatten the tokenized inputs values expanding it to the shape [batch_size, num_choices, seq_length]
                for k, v in tokenized_input.items():
                    tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)]
                return dict(tokenized_input.convert_to_tensors(tensor_type=framework))
            return dict(preprocessor(dummy_input, return_tensors=framework))
        elif isinstance(preprocessor, ImageProcessingMixin):
            if preprocessor.model_input_names[0] != "pixel_values":
                raise ValueError(
                    f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects"
                    f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}'
                )
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
            return dict(preprocessor(images=dummy_input, return_tensors=framework))
        elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
            return dict(preprocessor(images=dummy_input, return_tensors=framework))
        elif (
            isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "input_features"
        ):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_audio(batch_size, sampling_rate, time_duration, frequency)
            return dict(preprocessor(dummy_input, return_tensors=framework))
        else:
            raise ValueError(
                "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."
            )

    def generate_dummy_inputs_onnxruntime(self, reference_model_inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        """
        Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq
        models which have the encoder and decoder exported as separate ONNX files.

        Args:
            reference_model_inputs ([`Mapping[str, Tensor]`):
                Reference inputs for the model.

        Returns:
            `Mapping[str, Tensor]`: The mapping holding the kwargs to provide to the model's forward function
        """
        return reference_model_inputs

    def patch_ops(self):
        for spec in self._patching_specs:
            custom_op = spec.custom_op if spec.op_wrapper is None else spec.op_wrapper(spec.custom_op)
            setattr(spec.o, spec.name, custom_op)

    def restore_ops(self):
        for spec in self._patching_specs:
            orig_op = spec.orig_op if spec.op_wrapper is None else spec.op_wrapper(spec.orig_op)
            setattr(spec.o, spec.name, orig_op)

    @classmethod
    def flatten_output_collection_property(cls, name: str, field: Iterable[Any]) -> Dict[str, Any]:
        """
        Flatten any potential nested structure expanding the name of the field with the index of the element within the
        structure.

        Args:
            name: The name of the nested structure
            field: The structure to, potentially, be flattened

        Returns:
            (Dict[str, Any]): Outputs with flattened structure and key mapping this new structure.

        """
        from itertools import chain

        return {f"{name}.{idx}": item for idx, item in enumerate(chain.from_iterable(field))}


class OnnxConfigWithPast(OnnxConfig, ABC):
    def __init__(
        self,
        config: "PretrainedConfig",
        task: str = "default",
        patching_specs: List[PatchingSpec] = None,
        use_past: bool = False,
    ):
        super().__init__(config, task=task, patching_specs=patching_specs)
        self.use_past = use_past

    @classmethod
    def with_past(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfigWithPast":
        """
        Instantiate a OnnxConfig with `use_past` attribute set to True

        Args:
            config: The underlying model's config to use when exporting to ONNX

        Returns:
            OnnxConfig with `.use_past = True`
        """
        return cls(config, task=task, use_past=True)

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        common_outputs = super().outputs
        if self.use_past:
            self.fill_with_past_key_values_(common_outputs, direction="outputs")

        return common_outputs

    @property
    def values_override(self) -> Optional[Mapping[str, Any]]:
        if hasattr(self._config, "use_cache"):
            return {"use_cache": self.use_past}

        return None

    @property
    def num_layers(self) -> int:
        """
        The number of layers attribute retrieved from the model config. Override this for model configs where the
        number of layers attribute is not called `num_layers`.
        """
        if not hasattr(self._config, "num_layers"):
            raise AttributeError(
                "could not find the number of layers attribute in the model configuration, override the num_layers"
                " property of the model OnnxConfig to solve this"
            )
        return self._config.num_layers

    @property
    def num_attention_heads(self) -> int:
        """
        The number of attention heads attribute retrieved from the model config. Override this for model configs where
        the number of attention heads attribute is not called `num_attention_heads`.
        """
        if not hasattr(self._config, "num_attention_heads"):
            raise AttributeError(
                "could not find the number of attention heads attribute in the model configuration, override the"
                " num_attention_heads property of the model OnnxConfig to solve this"
            )
        return self._config.num_attention_heads

    def generate_dummy_inputs(
        self,
        tokenizer: "PreTrainedTokenizerBase",
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        # TODO: should we set seq_length = 1 when self.use_past = True?
        common_inputs = super().generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch

            batch, seqlen = common_inputs["input_ids"].shape
            # Not using the same length for past_key_values
            past_key_values_length = seqlen + 2
            shape = (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )

            if "attention_mask" in common_inputs:
                mask_dtype = common_inputs["attention_mask"].dtype
                common_inputs["attention_mask"] = torch.cat(
                    [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)],
                    dim=1,
                )

            common_inputs["past_key_values"] = []
            for _ in range(self.num_layers):
                common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))

        return common_inputs

    def fill_with_past_key_values_(
        self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str, inverted_values_shape: bool = False
    ):
        """
        Fill the input_or_outputs mapping with past_key_values dynamic axes considering.

        Args:
            inputs_or_outputs: The mapping to fill.
            direction: either "inputs" or "outputs", it specifies whether input_or_outputs is the input mapping or the
                output mapping, this is important for axes naming.
            inverted_values_shape:
                If `True`, store values on dynamic axis 1, else on axis 2.

        """
        if direction not in ["inputs", "outputs"]:
            raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

        name = "past_key_values" if direction == "inputs" else "present"
        for i in range(self.num_layers):
            inputs_or_outputs[f"{name}.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
            if inverted_values_shape:
                inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 1: "past_sequence + sequence"}
            else:
                inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}

    def _flatten_past_key_values_(self, flattened_output, name, idx, t):
        flattened_output[f"{name}.{idx}.key"] = t[0]
        flattened_output[f"{name}.{idx}.value"] = t[1]

    def flatten_output_collection_property(self, name: str, field: Iterable[Any]) -> Dict[str, Any]:
        flattened_output = {}
        if name in ["present", "past_key_values"]:
            for idx, t in enumerate(field):
                self._flatten_past_key_values_(flattened_output, name, idx, t)
        else:
            flattened_output = super().flatten_output_collection_property(name, field)

        return flattened_output


class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast):
    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        common_outputs = super(OnnxConfigWithPast, self).outputs
        # Renaming the outputs axes properly.
        for name, axes_names in common_outputs.items():
            sequence_name = "encoder_sequence" if "encoder" in name else "decoder_sequence"
            for axis_idx, name in axes_names.items():
                if "sequence" in name:
                    axes_names[axis_idx] = sequence_name
                # We reset the value as the order in common_outputs (OrderedDict) is lost otherwise
                else:
                    axes_names[axis_idx] = name
        if self.use_past:
            self.fill_with_past_key_values_(common_outputs, direction="outputs")

        return common_outputs

    @property
    def num_layers(self) -> Tuple[int]:
        try:
            num_layers = super().num_layers
            num_layers = (num_layers, num_layers)
        except AttributeError:
            if hasattr(self._config, "encoder_layers") and hasattr(self._config, "decoder_layers"):
                num_layers = (self._config.encoder_layers, self._config.decoder_layers)
            else:
                raise AttributeError(
                    "could not find the number of encoder and decoder layers attributes in the model configuration,"
                    " override the num_layers property of the model OnnxConfig to solve this"
                )

        return num_layers

    @property
    def num_attention_heads(self) -> Tuple[int]:
        try:
            num_attention_heads = super().num_attention_heads
            num_attention_heads = (num_attention_heads, num_attention_heads)
        except AttributeError:
            if hasattr(self._config, "encoder_attention_heads") and hasattr(self._config, "decoder_attention_heads"):
                num_attention_heads = (self._config.encoder_attention_heads, self._config.decoder_attention_heads)
            else:
                raise AttributeError(
                    "could not find the number of attention heads for the encoder and the decoder attributes in the"
                    " model configuration, override the num_attention_heads property of the model OnnxConfig to solve"
                    " this"
                )
        return num_attention_heads

    def generate_dummy_inputs(
        self,
        tokenizer: "PreTrainedTokenizerBase",
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        encoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        # Generate decoder inputs
        decoder_seq_length = seq_length if not self.use_past else 1
        decoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=decoder_seq_length, is_pair=is_pair, framework=framework
        )
        decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        common_inputs = dict(**encoder_inputs, **decoder_inputs)

        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch
            batch = common_inputs["input_ids"].shape[0]
            encoder_seq_length = common_inputs["input_ids"].shape[1]
            decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
            num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
            encoder_shape = (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            decoder_shape = (
                batch,
                num_decoder_attention_heads,
                # Not using the same length for past_key_values
                decoder_seq_length + 3,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            common_inputs["past_key_values"] = []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            num_encoder_layers, num_decoder_layers = self.num_layers
            min_num_layers = min(num_encoder_layers, num_decoder_layers)
            max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
            remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

            for _ in range(min_num_layers):
                # For encoder-decoder models, past_key_values contains pre-computed values for both the encoder and the
                # decoder layers, hence a tuple of 4 tensors instead of 2
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(decoder_shape),
                        torch.zeros(decoder_shape),
                        torch.zeros(encoder_shape),
                        torch.zeros(encoder_shape),
                    )
                )

            # TODO: test this.
            shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
            for _ in range(min_num_layers, max_num_layers):
                common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))

        return common_inputs

    def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str):
        if direction not in ["inputs", "outputs"]:
            raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

        name = "past_key_values" if direction == "inputs" else "present"

        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        num_encoder_layers, num_decoder_layers = self.num_layers
        min_num_layers = min(num_encoder_layers, num_decoder_layers)
        max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
        remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        encoder_sequence = "past_encoder_sequence"
        decoder_sequence = "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(min_num_layers):
            inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence}
            inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence}
            inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence}
            inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence}

        for i in range(min_num_layers, max_num_layers):
            if remaining_side_name == "encoder":
                axes_info = {0: "batch", 2: encoder_sequence}
            else:
                axes_info = {0: "batch", 2: decoder_sequence}
            inputs_or_outputs[f"{name}.{i}.{remaining_side_name}.key"] = axes_info

    def _flatten_past_key_values_(self, flattened_output, name, idx, t):
        flattened_output[f"{name}.{idx}.decoder.key"] = t[0]
        flattened_output[f"{name}.{idx}.decoder.value"] = t[1]
        flattened_output[f"{name}.{idx}.encoder.key"] = t[2]
        flattened_output[f"{name}.{idx}.encoder.value"] = t[3]

# Copyright 2021 The HuggingFace 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.
import copy
import dataclasses
import warnings
from abc import ABC, abstractmethod
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Union

import numpy as np
from packaging import version

from ..utils import TensorType, is_torch_available, is_vision_available, logging
from .utils import ParameterFormat, compute_effective_axis_dimension, compute_serialized_parameters_size


if TYPE_CHECKING:
    from ..configuration_utils import PretrainedConfig
    from ..feature_extraction_utils import FeatureExtractionMixin
    from ..image_processing_utils import ImageProcessingMixin
    from ..tokenization_utils_base import PreTrainedTokenizerBase


if is_vision_available():
    from PIL import Image

logger = logging.get_logger(__name__)


DEFAULT_ONNX_OPSET = 11

# 2 Gb
EXTERNAL_DATA_FORMAT_SIZE_LIMIT = 2 * 1024 * 1024 * 1024


@dataclasses.dataclass
class PatchingSpec:
    """
    Data class that holds patching specifications.

    Args:
        o: Module / object where the op to patch is located
        name: Name of the op to monkey patch
        custom_op: Custom op that patches the original op
        orig_op: Original op that is being patched
        op_wrapper: Wrapper (optional) that wraps both the original and custom ops.
            It is useful for ops that are class or static methods for instance.
    """

    o: Any
    name: str
    custom_op: Callable
    orig_op: Optional[Callable] = None
    op_wrapper: Optional[Callable] = None


class OnnxConfig(ABC):
    """
    Base class for ONNX exportable model describing metadata on how to export the model through the ONNX format.
    """

    default_fixed_batch = 2
    default_fixed_sequence = 8
    default_fixed_num_choices = 4
    torch_onnx_minimum_version = version.parse("1.8")
    _tasks_to_common_outputs = {
        "causal-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "default": OrderedDict({"last_hidden_state": {0: "batch", 1: "sequence"}}),
        "image-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "image-segmentation": OrderedDict(
            {
                "logits": {0: "batch", 1: "sequence"},
                "pred_boxes": {0: "batch", 1: "sequence"},
                "pred_masks": {0: "batch", 1: "sequence"},
            }
        ),
        "masked-im": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "masked-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "multiple-choice": OrderedDict({"logits": {0: "batch"}}),
        "object-detection": OrderedDict(
            {
                "logits": {0: "batch", 1: "sequence"},
                "pred_boxes": {0: "batch", 1: "sequence"},
            }
        ),
        "question-answering": OrderedDict(
            {
                "start_logits": {0: "batch", 1: "sequence"},
                "end_logits": {0: "batch", 1: "sequence"},
            }
        ),
        "semantic-segmentation": OrderedDict({"logits": {0: "batch", 1: "num_labels", 2: "height", 3: "width"}}),
        "seq2seq-lm": OrderedDict({"logits": {0: "batch", 1: "decoder_sequence"}}),
        "sequence-classification": OrderedDict({"logits": {0: "batch"}}),
        "token-classification": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "vision2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
        "speech2seq-lm": OrderedDict({"logits": {0: "batch", 1: "sequence"}}),
    }

    def __init__(self, config: "PretrainedConfig", task: str = "default", patching_specs: List[PatchingSpec] = None):
        self._config = config

        if task not in self._tasks_to_common_outputs:
            raise ValueError(
                f"{task} is not a supported task, supported tasks: {self._tasks_to_common_outputs.keys()}"
            )
        self.task = task

        self._patching_specs = []
        for spec in patching_specs if patching_specs is not None else []:
            final_spec = spec
            if spec.orig_op is None:
                final_spec = dataclasses.replace(spec, orig_op=getattr(spec.o, spec.name))
            self._patching_specs.append(final_spec)

    @classmethod
    def from_model_config(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfig":
        """
        Instantiate a OnnxConfig for a specific model

        Args:
            config: The model's configuration to use when exporting to ONNX

        Returns:
            OnnxConfig for this model
        """
        return cls(config, task=task)

    @property
    @abstractmethod
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        """
        Mapping containing the axis definition of the input tensors to provide to the model

        Returns:
            For each input: its name associated to the axes symbolic name and the axis position within the tensor
        """
        raise NotImplementedError()

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        """
        Mapping containing the axis definition of the output tensors to provide to the model

        Returns:
            For each output: its name associated to the axes symbolic name and the axis position within the tensor
        """
        common_outputs = self._tasks_to_common_outputs[self.task]
        return copy.deepcopy(common_outputs)

    @property
    def values_override(self) -> Optional[Mapping[str, Any]]:
        """
        Dictionary of keys to override in the model's config before exporting

        Returns:
            Dictionary with the keys (and their corresponding values) to override
        """
        if hasattr(self._config, "use_cache"):
            return {"use_cache": False}

        return None

    @property
    def default_batch_size(self) -> int:
        """
        The default batch size to use if no other indication

        Returns:
            Integer > 0
        """
        # Using 2 avoid ONNX making assumption about single sample batch
        return OnnxConfig.default_fixed_batch

    @property
    def default_sequence_length(self) -> int:
        """
        The default sequence length to use if no other indication

        Returns:
            Integer > 0
        """
        return OnnxConfig.default_fixed_sequence

    @property
    def default_num_choices(self) -> int:
        """
        The default number of choices to use if no other indication

        Returns:
            Integer > 0
        """
        return OnnxConfig.default_fixed_num_choices

    @property
    def default_onnx_opset(self) -> int:
        """
        Which onnx opset to use when exporting the model

        Returns:
            Integer ONNX Opset version
        """
        return DEFAULT_ONNX_OPSET

    @property
    def atol_for_validation(self) -> float:
        """
        What absolute tolerance value to use during model conversion validation.

        Returns:
            Float absolute tolerance value.
        """
        return 1e-5

    @property
    def is_torch_support_available(self) -> bool:
        """
        The minimum PyTorch version required to export the model.

        Returns:
            `bool`: Whether the installed version of PyTorch is compatible with the model.
        """
        if is_torch_available():
            from transformers.utils import get_torch_version

            return version.parse(get_torch_version()) >= self.torch_onnx_minimum_version
        else:
            return False

    @staticmethod
    def use_external_data_format(num_parameters: int) -> bool:
        """
        Flag indicating if the model requires using external data format

        Args:
            num_parameters: Number of parameter on the model

        Returns:
            True if model.num_parameters() * size_of(float32) >= 2Gb False otherwise
        """

        return (
            compute_serialized_parameters_size(num_parameters, ParameterFormat.Float)
            >= EXTERNAL_DATA_FORMAT_SIZE_LIMIT
        )

    def _generate_dummy_images(
        self, batch_size: int = 2, num_channels: int = 3, image_height: int = 40, image_width: int = 40
    ):
        images = []
        for _ in range(batch_size):
            data = np.random.rand(image_height, image_width, num_channels) * 255
            images.append(Image.fromarray(data.astype("uint8")).convert("RGB"))
        return images

    def _generate_dummy_audio(
        self, batch_size: int = 2, sampling_rate: int = 22050, time_duration: float = 5.0, frequency: int = 220
    ):
        audio_data = []
        for _ in range(batch_size):
            # time variable
            t = np.linspace(0, time_duration, int(time_duration * sampling_rate), endpoint=False)

            # generate pure sine wave at `frequency` Hz
            audio_data.append(0.5 * np.sin(2 * np.pi * frequency * t))

        return audio_data

    def generate_dummy_inputs(
        self,
        preprocessor: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin", "ImageProcessingMixin"],
        batch_size: int = -1,
        seq_length: int = -1,
        num_choices: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
        num_channels: int = 3,
        image_width: int = 40,
        image_height: int = 40,
        sampling_rate: int = 22050,
        time_duration: float = 5.0,
        frequency: int = 220,
        tokenizer: "PreTrainedTokenizerBase" = None,
    ) -> Mapping[str, Any]:
        """
        Generate inputs to provide to the ONNX exporter for the specific framework

        Args:
            preprocessor: ([`PreTrainedTokenizerBase`], [`FeatureExtractionMixin`], or [`ImageProcessingMixin`]):
                The preprocessor associated with this model configuration.
            batch_size (`int`, *optional*, defaults to -1):
                The batch size to export the model for (-1 means dynamic axis).
            num_choices (`int`, *optional*, defaults to -1):
                The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
            seq_length (`int`, *optional*, defaults to -1):
                The sequence length to export the model for (-1 means dynamic axis).
            is_pair (`bool`, *optional*, defaults to `False`):
                Indicate if the input is a pair (sentence 1, sentence 2)
            framework (`TensorType`, *optional*, defaults to `None`):
                The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
            num_channels (`int`, *optional*, defaults to 3):
                The number of channels of the generated images.
            image_width (`int`, *optional*, defaults to 40):
                The width of the generated images.
            image_height (`int`, *optional*, defaults to 40):
                The height of the generated images.
            sampling_rate (`int`, *optional* defaults to 22050)
                The sampling rate for audio data generation.
            time_duration (`float`, *optional* defaults to 5.0)
                Total seconds of sampling for audio data generation.
            frequency (`int`, *optional* defaults to 220)
                The desired natural frequency of generated audio.

        Returns:
            Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
        """
        from ..feature_extraction_utils import FeatureExtractionMixin
        from ..image_processing_utils import ImageProcessingMixin
        from ..tokenization_utils_base import PreTrainedTokenizerBase

        if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
            raise ValueError("You cannot provide both a tokenizer and a preprocessor to generate dummy inputs.")
        if tokenizer is not None:
            warnings.warn(
                "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use"
                " `preprocessor` instead.",
                FutureWarning,
            )
            logger.warning("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.")
            preprocessor = tokenizer
        if isinstance(preprocessor, PreTrainedTokenizerBase):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(
                batch_size, fixed_dimension=OnnxConfig.default_fixed_batch, num_token_to_add=0
            )
            # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
            token_to_add = preprocessor.num_special_tokens_to_add(is_pair)
            seq_length = compute_effective_axis_dimension(
                seq_length, fixed_dimension=OnnxConfig.default_fixed_sequence, num_token_to_add=token_to_add
            )
            # Generate dummy inputs according to compute batch and sequence
            input_token = (
                preprocessor.unk_token
                if (preprocessor.unk_token is not None and len(preprocessor.unk_token) > 0)
                else "0"
            )
            dummy_input = [" ".join([input_token]) * seq_length] * batch_size
            if self.task == "multiple-choice":
                # If dynamic axis (-1) we forward with a fixed dimension of 4 candidate answers to avoid optimizations
                # made by ONNX
                num_choices = compute_effective_axis_dimension(
                    num_choices, fixed_dimension=OnnxConfig.default_fixed_num_choices, num_token_to_add=0
                )
                dummy_input = dummy_input * num_choices
                # The shape of the tokenized inputs values is [batch_size * num_choices, seq_length]
                tokenized_input = preprocessor(dummy_input, text_pair=dummy_input)
                # Unflatten the tokenized inputs values expanding it to the shape [batch_size, num_choices, seq_length]
                for k, v in tokenized_input.items():
                    tokenized_input[k] = [v[i : i + num_choices] for i in range(0, len(v), num_choices)]
                return dict(tokenized_input.convert_to_tensors(tensor_type=framework))
            return dict(preprocessor(dummy_input, return_tensors=framework))
        elif isinstance(preprocessor, ImageProcessingMixin):
            if preprocessor.model_input_names[0] != "pixel_values":
                raise ValueError(
                    f"The `preprocessor` is an image processor ({preprocessor.__class__.__name__}) and expects"
                    f' `model_input_names[0]` to be "pixel_values", but got {preprocessor.model_input_names[0]}'
                )
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
            return dict(preprocessor(images=dummy_input, return_tensors=framework))
        elif isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "pixel_values":
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
            return dict(preprocessor(images=dummy_input, return_tensors=framework))
        elif (
            isinstance(preprocessor, FeatureExtractionMixin) and preprocessor.model_input_names[0] == "input_features"
        ):
            # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
            batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
            dummy_input = self._generate_dummy_audio(batch_size, sampling_rate, time_duration, frequency)
            return dict(preprocessor(dummy_input, return_tensors=framework))
        else:
            raise ValueError(
                "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."
            )

    def generate_dummy_inputs_onnxruntime(self, reference_model_inputs: Mapping[str, Any]) -> Mapping[str, Any]:
        """
        Generate inputs for ONNX Runtime using the reference model inputs. Override this to run inference with seq2seq
        models which have the encoder and decoder exported as separate ONNX files.

        Args:
            reference_model_inputs ([`Mapping[str, Tensor]`):
                Reference inputs for the model.

        Returns:
            `Mapping[str, Tensor]`: The mapping holding the kwargs to provide to the model's forward function
        """
        return reference_model_inputs

    def patch_ops(self):
        for spec in self._patching_specs:
            custom_op = spec.custom_op if spec.op_wrapper is None else spec.op_wrapper(spec.custom_op)
            setattr(spec.o, spec.name, custom_op)

    def restore_ops(self):
        for spec in self._patching_specs:
            orig_op = spec.orig_op if spec.op_wrapper is None else spec.op_wrapper(spec.orig_op)
            setattr(spec.o, spec.name, orig_op)

    @classmethod
    def flatten_output_collection_property(cls, name: str, field: Iterable[Any]) -> Dict[str, Any]:
        """
        Flatten any potential nested structure expanding the name of the field with the index of the element within the
        structure.

        Args:
            name: The name of the nested structure
            field: The structure to, potentially, be flattened

        Returns:
            (Dict[str, Any]): Outputs with flattened structure and key mapping this new structure.

        """
        from itertools import chain

        return {f"{name}.{idx}": item for idx, item in enumerate(chain.from_iterable(field))}


class OnnxConfigWithPast(OnnxConfig, ABC):
    def __init__(
        self,
        config: "PretrainedConfig",
        task: str = "default",
        patching_specs: List[PatchingSpec] = None,
        use_past: bool = False,
    ):
        super().__init__(config, task=task, patching_specs=patching_specs)
        self.use_past = use_past

    @classmethod
    def with_past(cls, config: "PretrainedConfig", task: str = "default") -> "OnnxConfigWithPast":
        """
        Instantiate a OnnxConfig with `use_past` attribute set to True

        Args:
            config: The underlying model's config to use when exporting to ONNX

        Returns:
            OnnxConfig with `.use_past = True`
        """
        return cls(config, task=task, use_past=True)

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        common_outputs = super().outputs
        if self.use_past:
            self.fill_with_past_key_values_(common_outputs, direction="outputs")

        return common_outputs

    @property
    def values_override(self) -> Optional[Mapping[str, Any]]:
        if hasattr(self._config, "use_cache"):
            return {"use_cache": self.use_past}

        return None

    @property
    def num_layers(self) -> int:
        """
        The number of layers attribute retrieved from the model config. Override this for model configs where the
        number of layers attribute is not called `num_layers`.
        """
        if not hasattr(self._config, "num_layers"):
            raise AttributeError(
                "could not find the number of layers attribute in the model configuration, override the num_layers"
                " property of the model OnnxConfig to solve this"
            )
        return self._config.num_layers

    @property
    def num_attention_heads(self) -> int:
        """
        The number of attention heads attribute retrieved from the model config. Override this for model configs where
        the number of attention heads attribute is not called `num_attention_heads`.
        """
        if not hasattr(self._config, "num_attention_heads"):
            raise AttributeError(
                "could not find the number of attention heads attribute in the model configuration, override the"
                " num_attention_heads property of the model OnnxConfig to solve this"
            )
        return self._config.num_attention_heads

    def generate_dummy_inputs(
        self,
        tokenizer: "PreTrainedTokenizerBase",
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        # TODO: should we set seq_length = 1 when self.use_past = True?
        common_inputs = super().generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch

            batch, seqlen = common_inputs["input_ids"].shape
            # Not using the same length for past_key_values
            past_key_values_length = seqlen + 2
            shape = (
                batch,
                self.num_attention_heads,
                past_key_values_length,
                self._config.hidden_size // self.num_attention_heads,
            )

            if "attention_mask" in common_inputs:
                mask_dtype = common_inputs["attention_mask"].dtype
                common_inputs["attention_mask"] = torch.cat(
                    [common_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype)],
                    dim=1,
                )

            common_inputs["past_key_values"] = []
            for _ in range(self.num_layers):
                common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))

        return common_inputs

    def fill_with_past_key_values_(
        self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str, inverted_values_shape: bool = False
    ):
        """
        Fill the input_or_outputs mapping with past_key_values dynamic axes considering.

        Args:
            inputs_or_outputs: The mapping to fill.
            direction: either "inputs" or "outputs", it specifies whether input_or_outputs is the input mapping or the
                output mapping, this is important for axes naming.
            inverted_values_shape:
                If `True`, store values on dynamic axis 1, else on axis 2.

        """
        if direction not in ["inputs", "outputs"]:
            raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

        name = "past_key_values" if direction == "inputs" else "present"
        for i in range(self.num_layers):
            inputs_or_outputs[f"{name}.{i}.key"] = {0: "batch", 2: "past_sequence + sequence"}
            if inverted_values_shape:
                inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 1: "past_sequence + sequence"}
            else:
                inputs_or_outputs[f"{name}.{i}.value"] = {0: "batch", 2: "past_sequence + sequence"}

    def _flatten_past_key_values_(self, flattened_output, name, idx, t):
        flattened_output[f"{name}.{idx}.key"] = t[0]
        flattened_output[f"{name}.{idx}.value"] = t[1]

    def flatten_output_collection_property(self, name: str, field: Iterable[Any]) -> Dict[str, Any]:
        flattened_output = {}
        if name in ["present", "past_key_values"]:
            for idx, t in enumerate(field):
                self._flatten_past_key_values_(flattened_output, name, idx, t)
        else:
            flattened_output = super().flatten_output_collection_property(name, field)

        return flattened_output


class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast):
    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        common_outputs = super(OnnxConfigWithPast, self).outputs
        # Renaming the outputs axes properly.
        for name, axes_names in common_outputs.items():
            sequence_name = "encoder_sequence" if "encoder" in name else "decoder_sequence"
            for axis_idx, name in axes_names.items():
                if "sequence" in name:
                    axes_names[axis_idx] = sequence_name
                # We reset the value as the order in common_outputs (OrderedDict) is lost otherwise
                else:
                    axes_names[axis_idx] = name
        if self.use_past:
            self.fill_with_past_key_values_(common_outputs, direction="outputs")

        return common_outputs

    @property
    def num_layers(self) -> Tuple[int]:
        try:
            num_layers = super().num_layers
            num_layers = (num_layers, num_layers)
        except AttributeError:
            if hasattr(self._config, "encoder_layers") and hasattr(self._config, "decoder_layers"):
                num_layers = (self._config.encoder_layers, self._config.decoder_layers)
            else:
                raise AttributeError(
                    "could not find the number of encoder and decoder layers attributes in the model configuration,"
                    " override the num_layers property of the model OnnxConfig to solve this"
                )

        return num_layers

    @property
    def num_attention_heads(self) -> Tuple[int]:
        try:
            num_attention_heads = super().num_attention_heads
            num_attention_heads = (num_attention_heads, num_attention_heads)
        except AttributeError:
            if hasattr(self._config, "encoder_attention_heads") and hasattr(self._config, "decoder_attention_heads"):
                num_attention_heads = (self._config.encoder_attention_heads, self._config.decoder_attention_heads)
            else:
                raise AttributeError(
                    "could not find the number of attention heads for the encoder and the decoder attributes in the"
                    " model configuration, override the num_attention_heads property of the model OnnxConfig to solve"
                    " this"
                )
        return num_attention_heads

    def generate_dummy_inputs(
        self,
        tokenizer: "PreTrainedTokenizerBase",
        batch_size: int = -1,
        seq_length: int = -1,
        is_pair: bool = False,
        framework: Optional[TensorType] = None,
    ) -> Mapping[str, Any]:
        encoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework
        )

        # Generate decoder inputs
        decoder_seq_length = seq_length if not self.use_past else 1
        decoder_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs(
            tokenizer, batch_size=batch_size, seq_length=decoder_seq_length, is_pair=is_pair, framework=framework
        )
        decoder_inputs = {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
        common_inputs = dict(**encoder_inputs, **decoder_inputs)

        if self.use_past:
            if not is_torch_available():
                raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed.")
            else:
                import torch
            batch = common_inputs["input_ids"].shape[0]
            encoder_seq_length = common_inputs["input_ids"].shape[1]
            decoder_seq_length = common_inputs["decoder_input_ids"].shape[1]
            num_encoder_attention_heads, num_decoder_attention_heads = self.num_attention_heads
            encoder_shape = (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            decoder_shape = (
                batch,
                num_decoder_attention_heads,
                # Not using the same length for past_key_values
                decoder_seq_length + 3,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            common_inputs["past_key_values"] = []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            num_encoder_layers, num_decoder_layers = self.num_layers
            min_num_layers = min(num_encoder_layers, num_decoder_layers)
            max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
            remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

            for _ in range(min_num_layers):
                # For encoder-decoder models, past_key_values contains pre-computed values for both the encoder and the
                # decoder layers, hence a tuple of 4 tensors instead of 2
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(decoder_shape),
                        torch.zeros(decoder_shape),
                        torch.zeros(encoder_shape),
                        torch.zeros(encoder_shape),
                    )
                )

            # TODO: test this.
            shape = encoder_shape if remaining_side_name == "encoder" else decoder_shape
            for _ in range(min_num_layers, max_num_layers):
                common_inputs["past_key_values"].append((torch.zeros(shape), torch.zeros(shape)))

        return common_inputs

    def fill_with_past_key_values_(self, inputs_or_outputs: Mapping[str, Mapping[int, str]], direction: str):
        if direction not in ["inputs", "outputs"]:
            raise ValueError(f'direction must either be "inputs" or "outputs", but {direction} was given')

        name = "past_key_values" if direction == "inputs" else "present"

        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        num_encoder_layers, num_decoder_layers = self.num_layers
        min_num_layers = min(num_encoder_layers, num_decoder_layers)
        max_num_layers = max(num_encoder_layers, num_decoder_layers) - min_num_layers
        remaining_side_name = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        encoder_sequence = "past_encoder_sequence"
        decoder_sequence = "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(min_num_layers):
            inputs_or_outputs[f"{name}.{i}.decoder.key"] = {0: "batch", 2: decoder_sequence}
            inputs_or_outputs[f"{name}.{i}.decoder.value"] = {0: "batch", 2: decoder_sequence}
            inputs_or_outputs[f"{name}.{i}.encoder.key"] = {0: "batch", 2: encoder_sequence}
            inputs_or_outputs[f"{name}.{i}.encoder.value"] = {0: "batch", 2: encoder_sequence}

        for i in range(min_num_layers, max_num_layers):
            if remaining_side_name == "encoder":
                axes_info = {0: "batch", 2: encoder_sequence}
            else:
                axes_info = {0: "batch", 2: decoder_sequence}
            inputs_or_outputs[f"{name}.{i}.{remaining_side_name}.key"] = axes_info

    def _flatten_past_key_values_(self, flattened_output, name, idx, t):
        flattened_output[f"{name}.{idx}.decoder.key"] = t[0]
        flattened_output[f"{name}.{idx}.decoder.value"] = t[1]
        flattened_output[f"{name}.{idx}.encoder.key"] = t[2]
        flattened_output[f"{name}.{idx}.encoder.value"] = t[3]
