





'''simple docstring'''


from copy import deepcopy
from unittest.case import TestCase

import pytest

from datasets.arrow_dataset import Dataset
from datasets.features import Audio, ClassLabel, Features, Image, Sequence, Value
from datasets.info import DatasetInfo
from datasets.tasks import (
    AudioClassification,
    AutomaticSpeechRecognition,
    ImageClassification,
    LanguageModeling,
    QuestionAnsweringExtractive,
    Summarization,
    TextClassification,
    task_template_from_dict,
)
from datasets.utils.py_utils import asdict


lowerCAmelCase      :  Tuple      							={
    '''id''': '''5733be284776f41900661182''',
    '''title''': '''University_of_Notre_Dame''',
    '''context''': '''Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary. Immediately in front of the Main Building and facing it, is a copper statue of Christ with arms upraised with the legend "Venite Ad Me Omnes". Next to the Main Building is the Basilica of the Sacred Heart. Immediately behind the basilica is the Grotto, a Marian place of prayer and reflection. It is a replica of the grotto at Lourdes, France where the Virgin Mary reputedly appeared to Saint Bernadette Soubirous in 1858. At the end of the main drive (and in a direct line that connects through 3 statues and the Gold Dome), is a simple, modern stone statue of Mary.''',
    '''question''': '''To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?''',
    '''answers''': {'''text''': ['''Saint Bernadette Soubirous'''], '''answer_start''': [515]},
}
@pytest.mark.parametrize(
    "task_cls"			,[
        AudioClassification,
        AutomaticSpeechRecognition,
        ImageClassification,
        LanguageModeling,
        QuestionAnsweringExtractive,
        Summarization,
        TextClassification,
    ]			,)
def 							UpperCAmelCase_						(		__lowerCamelCase      :      str				):
   lowercase_    :str					    =				task_cls()
   lowercase_    :Tuple					    =				asdict(__lowerCamelCase				)
   lowercase_    :Dict					    =				task_template_from_dict(__lowerCamelCase				)
   assert task == reloaded
class    a_     :





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				LanguageModeling(text_column="input_text"		)
        assert {"input_text": "text"} == task.column_mapping







     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        lowercase_    :Tuple					    =				Features({"text": Value("string"		)}		)
        lowercase_    :List[Any]					    =				{"text_column": "input_text"}
        lowercase_    :List[str]					    =				LanguageModeling.from_dict(lowercase		)
        assert "language-modeling" == task.task
        assert input_schema == task.input_schema







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        lowercase_    :List[str]					    =				sorted(["pos", "neg"]		)





     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        lowercase_    :List[str]					    =				TextClassification(text_column="input_text"			,   label_column="input_label"		)
        self.assertDictEqual({"input_text": "text", "input_label": "labels"}			,   task.column_mapping		)





     def 							lowercase__   (				self	:      List[Any]		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				Features({"text": Value("string"		)}		)
        # Labels are cast to tuple during `TextClassification.__post_init__`, so we do the same here
        lowercase_    :Any					    =				Features({"labels": ClassLabel}		)
        lowercase_    :str					    =				{"text_column": "input_text", "label_column": "input_labels"}
        lowercase_    :Optional[int]					    =				TextClassification.from_dict(lowercase		)
        self.assertEqual("text-classification"			,   task.task		)
        self.assertEqual(lowercase			,   task.input_schema		)
        self.assertEqual(lowercase			,   task.label_schema		)







     def 							lowercase__   (				self	:      str		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				TextClassification(text_column="input_text"			,   label_column="input_label"		)
        self.assertEqual(task.label_schema["labels"]			,   lowercase		)
        lowercase_    :Tuple					    =				task.align_with_features(Features({"input_label": ClassLabel(names=self.labels		)}		)		)
        self.assertEqual(task.label_schema["labels"]			,   ClassLabel(names=self.labels		)		)







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    :int					    =				QuestionAnsweringExtractive(
            context_column="input_context"			,   question_column="input_question"			,   answers_column="input_answers"		)
        self.assertDictEqual(
            {"input_context": "context", "input_question": "question", "input_answers": "answers"}			,   task.column_mapping		)







     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        lowercase_    :Dict					    =				Features({"question": Value("string"		), "context": Value("string"		)}		)
        lowercase_    :List[Any]					    =				Features(
            {
                "answers": Sequence(
                    {
                        "text": Value("string"		),
                        "answer_start": Value("int32"		),
                    }		)
            }		)
        lowercase_    :int					    =				{
            "context_column": "input_input_context",
            "question_column": "input_question",
            "answers_column": "input_answers",
        }
        lowercase_    :int					    =				QuestionAnsweringExtractive.from_dict(lowercase		)
        self.assertEqual("question-answering-extractive"			,   task.task		)
        self.assertEqual(lowercase			,   task.input_schema		)
        self.assertEqual(lowercase			,   task.label_schema		)







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        lowercase_    :Tuple					    =				Summarization(text_column="input_text"			,   summary_column="input_summary"		)
        self.assertDictEqual({"input_text": "text", "input_summary": "summary"}			,   task.column_mapping		)







     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :int					    =				Features({"text": Value("string"		)}		)
        lowercase_    :str					    =				Features({"summary": Value("string"		)}		)
        lowercase_    :Tuple					    =				{"text_column": "input_text", "summary_column": "input_summary"}
        lowercase_    :Optional[int]					    =				Summarization.from_dict(lowercase		)
        self.assertEqual("summarization"			,   task.task		)
        self.assertEqual(lowercase			,   task.input_schema		)
        self.assertEqual(lowercase			,   task.label_schema		)







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        lowercase_    :Any					    =				AutomaticSpeechRecognition(audio_column="input_audio"			,   transcription_column="input_transcription"		)
        self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}			,   task.column_mapping		)







     def 							lowercase__   (				self	:      Optional[int]		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				Features({"audio": Audio()}		)
        lowercase_    :str					    =				Features({"transcription": Value("string"		)}		)
        lowercase_    :Dict					    =				{
            "audio_column": "input_audio",
            "transcription_column": "input_transcription",
        }
        lowercase_    :Tuple					    =				AutomaticSpeechRecognition.from_dict(lowercase		)
        self.assertEqual("automatic-speech-recognition"			,   task.task		)
        self.assertEqual(lowercase			,   task.input_schema		)
        self.assertEqual(lowercase			,   task.label_schema		)







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      int		):





        """simple docstring"""




        lowercase_    :str					    =				sorted(["pos", "neg"]		)





     def 							lowercase__   (				self	:      List[str]		):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				AudioClassification(audio_column="input_audio"			,   label_column="input_label"		)
        self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}			,   task.column_mapping		)





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :int					    =				Features({"audio": Audio()}		)
        lowercase_    :Tuple					    =				Features({"labels": ClassLabel}		)
        lowercase_    :List[str]					    =				{
            "audio_column": "input_image",
            "label_column": "input_label",
        }
        lowercase_    :List[Any]					    =				AudioClassification.from_dict(lowercase		)
        self.assertEqual("audio-classification"			,   task.task		)
        self.assertEqual(lowercase			,   task.input_schema		)
        self.assertEqual(lowercase			,   task.label_schema		)







     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :Dict					    =				AudioClassification(audio_column="input_audio"			,   label_column="input_label"		)
        self.assertEqual(task.label_schema["labels"]			,   lowercase		)
        lowercase_    :List[Any]					    =				task.align_with_features(Features({"input_label": ClassLabel(names=self.labels		)}		)		)
        self.assertEqual(task.label_schema["labels"]			,   ClassLabel(names=self.labels		)		)







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        lowercase_    :Union[str, Any]					    =				sorted(["pos", "neg"]		)





     def 							lowercase__   (				self	:      Any		):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				ImageClassification(image_column="input_image"			,   label_column="input_label"		)
        self.assertDictEqual({"input_image": "image", "input_label": "labels"}			,   task.column_mapping		)





     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :Dict					    =				Features({"image": Image()}		)
        lowercase_    :str					    =				Features({"labels": ClassLabel}		)
        lowercase_    :Optional[Any]					    =				{
            "image_column": "input_image",
            "label_column": "input_label",
        }
        lowercase_    :str					    =				ImageClassification.from_dict(lowercase		)
        self.assertEqual("image-classification"			,   task.task		)
        self.assertEqual(lowercase			,   task.input_schema		)
        self.assertEqual(lowercase			,   task.label_schema		)







     def 							lowercase__   (				self	:      List[Any]		):





        """simple docstring"""




        lowercase_    :Any					    =				ImageClassification(image_column="input_image"			,   label_column="input_label"		)
        self.assertEqual(task.label_schema["labels"]			,   lowercase		)
        lowercase_    :Dict					    =				task.align_with_features(Features({"input_label": ClassLabel(names=self.labels		)}		)		)
        self.assertEqual(task.label_schema["labels"]			,   ClassLabel(names=self.labels		)		)







class    a_     (       _lowerCAmelCase							):





     def 							lowercase__   (				self	:      Tuple		):





        """simple docstring"""




        lowercase_    :Dict					    =				DatasetInfo(task_templates=QuestionAnsweringExtractive()		)
        lowercase_    :Optional[Any]					    =				Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}			,   info=lowercase		)
        assert isinstance(dataset.info.task_templates			,   lowercase		)
        assert len(dataset.info.task_templates		) == 1

        def keep_task(lowercase	:      List[str]		):
           return x

        def dont_keep_task(lowercase	:      Any		):
           lowercase_    :Optional[Any]					    =				deepcopy(lowercase		)
           lowercase_    :Dict					    =				0
           return out

        lowercase_    :List[str]					    =				dataset.map(lowercase		)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates
        # reload from cache
        lowercase_    :List[Any]					    =				dataset.map(lowercase		)
        assert mapped_dataset.info.task_templates == dataset.info.task_templates

        lowercase_    :str					    =				dataset.map(lowercase		)
        assert mapped_dataset.info.task_templates == []
        # reload from cache
        lowercase_    :Union[str, Any]					    =				dataset.map(lowercase		)
        assert mapped_dataset.info.task_templates == []







     def 							lowercase__   (				self	:      Optional[Any]		):





        """simple docstring"""




        lowercase_    :Optional[Any]					    =				Features({"text": Value("string"		), "label": ClassLabel(names=("pos", "neg")		)}		)
        lowercase_    :str					    =				TextClassification(text_column="text"			,   label_column="label"		)
        lowercase_    :Optional[int]					    =				DatasetInfo(features=lowercase			,   task_templates=lowercase		)
        lowercase_    :Dict					    =				Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}			,   info=lowercase		)

        def process(lowercase	:      Dict		):
           return example

        lowercase_    :Tuple					    =				dataset.remove_columns("label"		)
        lowercase_    :Optional[int]					    =				modified_dataset.map(lowercase		)
        assert mapped_dataset.info.task_templates == []









'''simple docstring'''


from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


lowerCAmelCase      :  str      							=logging.get_logger(__name__)

lowerCAmelCase      :  Union[str, Any]      							={
    '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}
class    a_     (       _lowerCAmelCase							):
     __A  				=    "blenderbot"
     __A  				=    ["past_key_values"]
     __A  				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}





     def __init__(				self	:      Dict			,   lowercase	:      List[Any]=8_008			,   lowercase	:      List[str]=128			,   lowercase	:      Optional[int]=2			,   lowercase	:      Optional[int]=10_240			,   lowercase	:      Tuple=32			,   lowercase	:      int=24			,   lowercase	:      Dict=10_240			,   lowercase	:      Any=32			,   lowercase	:      Optional[int]=0.0			,   lowercase	:      Dict=0.0			,   lowercase	:      int=True			,   lowercase	:      Tuple=True			,   lowercase	:      List[str]="gelu"			,   lowercase	:      int=2_560			,   lowercase	:      Optional[Any]=0.1			,   lowercase	:      Any=0.0			,   lowercase	:      List[str]=0.0			,   lowercase	:      Optional[Any]=0.02			,   lowercase	:      Tuple=1			,   lowercase	:      Union[str, Any]=False			,   lowercase	:      str=0			,   lowercase	:      List[str]=1			,   lowercase	:      Optional[Any]=2			,   lowercase	:      Union[str, Any]=3			,   lowercase	:      List[str]=2			,   **lowercase	:      Optional[Any]			,   ):





        """simple docstring"""




        lowercase_    :str					    =				vocab_size
        lowercase_    :Any					    =				max_position_embeddings
        lowercase_    :List[Any]					    =				d_model
        lowercase_    :Union[str, Any]					    =				encoder_ffn_dim
        lowercase_    :str					    =				encoder_layers
        lowercase_    :Optional[int]					    =				encoder_attention_heads
        lowercase_    :Dict					    =				decoder_ffn_dim
        lowercase_    :str					    =				decoder_layers
        lowercase_    :int					    =				decoder_attention_heads
        lowercase_    :Union[str, Any]					    =				dropout
        lowercase_    :int					    =				attention_dropout
        lowercase_    :int					    =				activation_dropout
        lowercase_    :str					    =				activation_function
        lowercase_    :Tuple					    =				init_std
        lowercase_    :Any					    =				encoder_layerdrop
        lowercase_    :Dict					    =				decoder_layerdrop
        lowercase_    :int					    =				use_cache
        lowercase_    :Dict					    =				encoder_layers
        lowercase_    :Dict					    =				scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            pad_token_id=lowercase			,   bos_token_id=lowercase			,   eos_token_id=lowercase			,   is_encoder_decoder=lowercase			,   decoder_start_token_id=lowercase			,   encoder_no_repeat_ngram_size=lowercase			,   forced_eos_token_id=lowercase			,   **lowercase			,   )







class    a_     (       _lowerCAmelCase							):





     @property
     def 							lowercase__   (				self	:      Union[str, Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
              lowercase_    :int					    =				{0: "batch"}
              lowercase_    :List[str]					    =				{0: "batch", 1: "past_decoder_sequence + sequence"}
           else:
              lowercase_    :Union[str, Any]					    =				{0: "batch", 1: "decoder_sequence"}
              lowercase_    :List[Any]					    =				{0: "batch", 1: "decoder_sequence"}
           if self.use_past:
              self.fill_with_past_key_values_(lowercase			,   direction="inputs"		)
        elif self.task == "causal-lm":
           lowercase_    :List[Any]					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
               ]		)
           if self.use_past:
              lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers
              for i in range(lowercase		):
                 lowercase_    :List[str]					    =				{0: "batch", 2: "past_sequence + sequence"}
                 lowercase_    :Any					    =				{0: "batch", 2: "past_sequence + sequence"}
        else:
           lowercase_    :str					    =				OrderedDict(
               [
                   ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                   ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                   ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                   ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
               ]		)

        return common_inputs





     @property
     # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
     def 							lowercase__   (				self	:      Dict		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Union[str, Any]					    =				super().outputs
        else:
           lowercase_    :Optional[Any]					    =				super(lowercase			,   self		).outputs
           if self.use_past:
              lowercase_    ,							lowercase_    :Any					    =				self.num_layers
              for i in range(lowercase		):
                 lowercase_    :str					    =				{0: "batch", 2: "past_sequence + sequence"}
                 lowercase_    :Any					    =				{0: "batch", 2: "past_sequence + sequence"}
        return common_outputs





     def 							lowercase__   (				self	:      Tuple			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :int					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        # Generate decoder inputs
        lowercase_    :List[Any]					    =				seq_length if not self.use_past else 1
        lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)
        lowercase_    :Tuple					    =				{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        lowercase_    :Any					    =				dict(**lowercase			,   **lowercase		)

        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
              import torch
           lowercase_    ,							lowercase_    :int					    =				common_inputs["input_ids"].shape
           lowercase_    :Tuple					    =				common_inputs["decoder_input_ids"].shape[1]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_attention_heads
           lowercase_    :List[Any]					    =				(
               batch,
               num_encoder_attention_heads,
               encoder_seq_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :List[Any]					    =				decoder_seq_length
           lowercase_    :str					    =				(
               batch,
               num_decoder_attention_heads,
               decoder_past_length,
               self._config.hidden_size // num_decoder_attention_heads,
           )
           lowercase_    :Optional[int]					    =				torch.cat(
               [common_inputs["decoder_attention_mask"], torch.ones(lowercase			,   lowercase		)]			,   dim=1		)
           lowercase_    :int					    =				[]
           lowercase_    ,							lowercase_    :Tuple					    =				self.num_layers

           for _ in range(lowercase		):
              common_inputs["past_key_values"].append(
                  (
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                      torch.zeros(lowercase		),
                  )		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :str					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase			,   lowercase			,   lowercase			,   lowercase			,   lowercase		)

        if self.use_past:
           if not is_torch_available():
              raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."		)
           else:
              import torch
           lowercase_    ,							lowercase_    :List[str]					    =				common_inputs["input_ids"].shape
           lowercase_    :int					    =				seqlen
           lowercase_    ,							lowercase_    :int					    =				self.num_layers
           lowercase_    ,							lowercase_    :Union[str, Any]					    =				self.num_attention_heads
           lowercase_    :Any					    =				(
               batch,
               num_encoder_attention_heads,
               past_key_values_length,
               self._config.hidden_size // num_encoder_attention_heads,
           )
           lowercase_    :Optional[Any]					    =				common_inputs["attention_mask"].dtype
           lowercase_    :List[Any]					    =				torch.cat(
               [common_inputs["attention_mask"], torch.ones(lowercase			,   lowercase			,   dtype=lowercase		)]			,   dim=1		)
           lowercase_    :Any					    =				[
               (torch.zeros(lowercase		), torch.zeros(lowercase		)) for _ in range(lowercase		)
           ]
        return common_inputs





     def 							lowercase__   (				self	:      Dict			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        lowercase_    :Optional[int]					    =				compute_effective_axis_dimension(
            lowercase			,   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
        lowercase_    :Optional[Any]					    =				tokenizer.num_special_tokens_to_add(lowercase		)
        lowercase_    :int					    =				compute_effective_axis_dimension(
            lowercase			,   fixed_dimension=OnnxConfig.default_fixed_sequence			,   num_token_to_add=lowercase		)

        # Generate dummy inputs according to compute batch and sequence
        lowercase_    :int					    =				[" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
        lowercase_    :Dict					    =				dict(tokenizer(lowercase			,   return_tensors=lowercase		)		)
        return common_inputs





     def 							lowercase__   (				self	:      Optional[Any]			,   lowercase	:      PreTrainedTokenizer			,   lowercase	:      int = -1			,   lowercase	:      int = -1			,   lowercase	:      bool = False			,   lowercase	:      Optional[TensorType] = None			,   ):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :str					    =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        elif self.task == "causal-lm":
           lowercase_    :Optional[Any]					    =				self._generate_dummy_inputs_for_causal_lm(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)
        else:
           lowercase_    :List[str]					    =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
               lowercase			,   batch_size=lowercase			,   seq_length=lowercase			,   is_pair=lowercase			,   framework=lowercase		)

        return common_inputs





     def 							lowercase__   (				self	:      Optional[int]			,   lowercase	:      str			,   lowercase	:      Union[str, Any]			,   lowercase	:      int			,   lowercase	:      List[Any]		):





        """simple docstring"""




        if self.task in ["default", "seq2seq-lm"]:
           lowercase_    :Optional[int]					    =				super()._flatten_past_key_values_(lowercase			,   lowercase			,   lowercase			,   lowercase		)
        else:
           lowercase_    :Tuple					    =				super(lowercase			,   self		)._flatten_past_key_values_(
               lowercase			,   lowercase			,   lowercase			,   lowercase		)







     def 							lowercase__   (				self	:      List[Any]			,   lowercase	:      Mapping[str, Mapping[int, str]]			,   lowercase	:      str		):





        """simple docstring"""




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

        lowercase_    :Any					    =				"past_key_values" if direction == "inputs" else "present"
        lowercase_    ,							lowercase_    :Optional[int]					    =				self.num_layers

        lowercase_    :str					    =				"past_encoder_sequence"
        lowercase_    :Tuple					    =				"past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

        for i in range(lowercase		):
           lowercase_    :Dict					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: decoder_sequence}
           lowercase_    :Optional[int]					    =				{0: "batch", 2: encoder_sequence}
           lowercase_    :int					    =				{0: "batch", 2: encoder_sequence}


