'''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


A_	   = {
    "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    :			Optional[Any])    ->		Optional[int]:



						"""simple docstring"""

						snake_case :       Tuple				=    task_cls()
						snake_case :       Union[str, Any]				=    asdict(_lowerCAmelCase)
						snake_case :       int				=    task_template_from_dict(_lowerCAmelCase)
						assert task == reloaded




class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    LanguageModeling(text_column="input_text"					)
									assert {"input_text": "text"} == task.column_mapping






			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    Features({"text": Value("string"					)}					)
									snake_case :       Dict				=    {"text_column": "input_text"}
									snake_case :       List[str]				=    LanguageModeling.from_dict(_a					)
									assert "language-modeling" == task.task
									assert input_schema == task.input_schema







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    sorted(["pos", "neg"]					)



			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    TextClassification(text_column="input_text"				,			label_column="input_label"					)
									self.assertDictEqual({"input_text": "text", "input_label": "labels"}				,			task.column_mapping					)



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






			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    TextClassification(text_column="input_text"				,			label_column="input_label"					)
									self.assertEqual(task.label_schema["labels"]				,			_a					)
									snake_case :       List[Any]				=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels					)}					)					)
									self.assertEqual(task.label_schema["labels"]				,			ClassLabel(names=self.labels					)					)







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






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







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    Summarization(text_column="input_text"				,			summary_column="input_summary"					)
									self.assertDictEqual({"input_text": "text", "input_summary": "summary"}				,			task.column_mapping					)






			def   lowerCamelCase (							self					):
									snake_case :       Dict				=    Features({"text": Value("string"					)}					)
									snake_case :       Optional[Any]				=    Features({"summary": Value("string"					)}					)
									snake_case :       List[Any]				=    {"text_column": "input_text", "summary_column": "input_summary"}
									snake_case :       Any				=    Summarization.from_dict(_a					)
									self.assertEqual("summarization"				,			task.task					)
									self.assertEqual(_a				,			task.input_schema					)
									self.assertEqual(_a				,			task.label_schema					)







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    AutomaticSpeechRecognition(audio_column="input_audio"				,			transcription_column="input_transcription"					)
									self.assertDictEqual({"input_audio": "audio", "input_transcription": "transcription"}				,			task.column_mapping					)






			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    Features({"audio": Audio()}					)
									snake_case :       str				=    Features({"transcription": Value("string"					)}					)
									snake_case :       Any				=    {
									    "audio_column": "input_audio",
									    "transcription_column": "input_transcription",
									}
									snake_case :       Tuple				=    AutomaticSpeechRecognition.from_dict(_a					)
									self.assertEqual("automatic-speech-recognition"				,			task.task					)
									self.assertEqual(_a				,			task.input_schema					)
									self.assertEqual(_a				,			task.label_schema					)







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    sorted(["pos", "neg"]					)



			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    AudioClassification(audio_column="input_audio"				,			label_column="input_label"					)
									self.assertDictEqual({"input_audio": "audio", "input_label": "labels"}				,			task.column_mapping					)



			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    Features({"audio": Audio()}					)
									snake_case :       List[str]				=    Features({"labels": ClassLabel}					)
									snake_case :       Optional[int]				=    {
									    "audio_column": "input_image",
									    "label_column": "input_label",
									}
									snake_case :       List[Any]				=    AudioClassification.from_dict(_a					)
									self.assertEqual("audio-classification"				,			task.task					)
									self.assertEqual(_a				,			task.input_schema					)
									self.assertEqual(_a				,			task.label_schema					)






			def   lowerCamelCase (							self					):
									snake_case :       str				=    AudioClassification(audio_column="input_audio"				,			label_column="input_label"					)
									self.assertEqual(task.label_schema["labels"]				,			_a					)
									snake_case :       List[Any]				=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels					)}					)					)
									self.assertEqual(task.label_schema["labels"]				,			ClassLabel(names=self.labels					)					)







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    sorted(["pos", "neg"]					)



			def   lowerCamelCase (							self					):
									snake_case :       Union[str, Any]				=    ImageClassification(image_column="input_image"				,			label_column="input_label"					)
									self.assertDictEqual({"input_image": "image", "input_label": "labels"}				,			task.column_mapping					)



			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    Features({"image": Image()}					)
									snake_case :       Optional[int]				=    Features({"labels": ClassLabel}					)
									snake_case :       Tuple				=    {
									    "image_column": "input_image",
									    "label_column": "input_label",
									}
									snake_case :       List[str]				=    ImageClassification.from_dict(_a					)
									self.assertEqual("image-classification"				,			task.task					)
									self.assertEqual(_a				,			task.input_schema					)
									self.assertEqual(_a				,			task.label_schema					)






			def   lowerCamelCase (							self					):
									snake_case :       Optional[int]				=    ImageClassification(image_column="input_image"				,			label_column="input_label"					)
									self.assertEqual(task.label_schema["labels"]				,			_a					)
									snake_case :       Optional[int]				=    task.align_with_features(Features({"input_label": ClassLabel(names=self.labels					)}					)					)
									self.assertEqual(task.label_schema["labels"]				,			ClassLabel(names=self.labels					)					)







class 		SCREAMING_SNAKE_CASE_					(_a      ):



			"""simple docstring"""






			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    DatasetInfo(task_templates=QuestionAnsweringExtractive()					)
									snake_case :       List[Any]				=    Dataset.from_dict({k: [v] for k, v in SAMPLE_QUESTION_ANSWERING_EXTRACTIVE.items()}				,			info=_a					)
									assert isinstance(dataset.info.task_templates				,			_a					)
									assert len(dataset.info.task_templates					) == 1

									def keep_task(snake_case_					):
															return x

									def dont_keep_task(snake_case_					):
															snake_case :       List[Any]				=    deepcopy(_a					)
															snake_case :       Optional[Any]				=    0
															return out

									snake_case :       Optional[Any]				=    dataset.map(_a					)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates
									# reload from cache
									snake_case :       Any				=    dataset.map(_a					)
									assert mapped_dataset.info.task_templates == dataset.info.task_templates

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






			def   lowerCamelCase (							self					):
									snake_case :       Any				=    Features({"text": Value("string"					), "label": ClassLabel(names=("pos", "neg")					)}					)
									snake_case :       Tuple				=    TextClassification(text_column="text"				,			label_column="label"					)
									snake_case :       Optional[int]				=    DatasetInfo(features=_a				,			task_templates=_a					)
									snake_case :       Union[str, Any]				=    Dataset.from_dict({"text": ["A sentence."], "label": ["pos"]}				,			info=_a					)

									def process(snake_case_					):
															return example

									snake_case :       Optional[Any]				=    dataset.remove_columns("label"					)
									snake_case :       List[str]				=    modified_dataset.map(_a					)
									assert mapped_dataset.info.task_templates == []










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

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


__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    '''google/bigbird-pegasus-large-arxiv''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-pubmed''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json'''
    ),
    '''google/bigbird-pegasus-large-bigpatent''': (
        '''https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json'''
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}


class 						lowerCamelCase__			(		_a ):
						_lowerCAmelCase             =			'''bigbird_pegasus'''
						_lowerCAmelCase             =			['''past_key_values''']
						_lowerCAmelCase             =			{
						    '''num_attention_heads''': '''encoder_attention_heads''',
						    '''hidden_size''': '''d_model''',
						    '''attention_probs_dropout_prob''': '''attention_dropout''',
						}


						def __init__(			self    :	int   ,							_a    :	List[Any]=9_6_1_0_3   ,							_a    :	Tuple=4_0_9_6   ,							_a    :	int=1_6   ,							_a    :	Dict=4_0_9_6   ,							_a    :	List[str]=1_6   ,							_a    :	Tuple=1_6   ,							_a    :	Union[str, Any]=4_0_9_6   ,							_a    :	int=1_6   ,							_a    :	Any=0.0   ,							_a    :	Union[str, Any]=0.0   ,							_a    :	Tuple=True   ,							_a    :	List[str]=True   ,							_a    :	Union[str, Any]="gelu_new"   ,							_a    :	Tuple=1_0_2_4   ,							_a    :	List[str]=0.1   ,							_a    :	Tuple=0.0   ,							_a    :	Any=0.0   ,							_a    :	int=0.0_2   ,							_a    :	str=2   ,							_a    :	Dict=0.0   ,							_a    :	Dict=True   ,							_a    :	List[str]=0   ,							_a    :	Dict=2   ,							_a    :	Any=1   ,							_a    :	Union[str, Any]="block_sparse"   ,							_a    :	Tuple=6_4   ,							_a    :	Tuple=3   ,							_a    :	List[str]=False   ,							**_a    :	Optional[Any]   ,							):
													a__:							Any									=vocab_size
													a__:							str									=max_position_embeddings
													a__:							str									=d_model
													a__:							int									=encoder_ffn_dim
													a__:							Optional[Any]									=encoder_layers
													a__:							Any									=encoder_attention_heads
													a__:							int									=decoder_ffn_dim
													a__:							Dict									=decoder_layers
													a__:							Dict									=decoder_attention_heads
													a__:							Union[str, Any]									=dropout
													a__:							List[Any]									=attention_dropout
													a__:							int									=activation_dropout
													a__:							Union[str, Any]									=activation_function
													a__:							Optional[int]									=init_std
													a__:							List[Any]									=encoder_layerdrop
													a__:							Dict									=decoder_layerdrop
													a__:							int									=classifier_dropout
													a__:							List[Any]									=use_cache
													a__:							int									=encoder_layers
													a__:							List[Any]									=scale_embedding  # scale factor will be sqrt(d_model) if True

													# extra config
													a__:							Any									=attention_type
													a__:							Any									=block_size
													a__:							int									=num_random_blocks
													a__:							List[str]									=use_bias

													super().__init__(
													    pad_token_id=_a   ,							bos_token_id=_a   ,							eos_token_id=_a   ,							is_encoder_decoder=_a   ,							decoder_start_token_id=_a   ,							**_a   ,							)


class 						lowerCamelCase__			(		_a ):


						@property
						def 	_lowerCamelCase      (			self    :	int      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Optional[Any]									=OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]      )

																				if self.use_past:
																											a__:							List[Any]									={0: "batch"}
																											a__:							int									={0: "batch", 1: "past_decoder_sequence + sequence"}
																				else:
																											a__:							List[str]									={0: "batch", 1: "decoder_sequence"}
																											a__:							Optional[int]									={0: "batch", 1: "decoder_sequence"}

																				if self.use_past:
																											self.fill_with_past_key_values_(_a   ,							direction="inputs"      )
													elif self.task == "causal-lm":
																				# TODO: figure this case out.
																				a__:							List[str]									=OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]      )
																				if self.use_past:
																											a__				,      a__:							str									=self.num_layers
																											for i in range(_a      ):
																																		a__:							Any									={0: "batch", 2: "past_sequence + sequence"}
																																		a__:							Any									={0: "batch", 2: "past_sequence + sequence"}
													else:
																				a__:							int									=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
						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							str									=super().outputs
													else:
																				a__:							Dict									=super(_a   ,							self      ).outputs
																				if self.use_past:
																											a__				,      a__:							Tuple									=self.num_layers
																											for i in range(_a      ):
																																		a__:							str									={0: "batch", 2: "past_sequence + sequence"}
																																		a__:							List[str]									={0: "batch", 2: "past_sequence + sequence"}
													return common_outputs


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													a__:							Union[str, Any]									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )

													# Generate decoder inputs
													a__:							str									=seq_length if not self.use_past else 1
													a__:							Dict									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )
													a__:							List[str]									={F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
													a__:							str									=dict(**_a   ,							**_a      )

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
																				else:
																											import torch
																				a__				,      a__:							str									=common_inputs["input_ids"].shape
																				a__:							List[str]									=common_inputs["decoder_input_ids"].shape[1]
																				a__				,      a__:							Any									=self.num_attention_heads
																				a__:							int									=(
																				    batch,
																				    num_encoder_attention_heads,
																				    encoder_seq_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)
																				a__:							str									=decoder_seq_length + 3
																				a__:							List[str]									=(
																				    batch,
																				    num_decoder_attention_heads,
																				    decoder_past_length,
																				    self._config.hidden_size // num_decoder_attention_heads,
																				)

																				a__:							List[str]									=torch.cat(
																				    [common_inputs["decoder_attention_mask"], torch.ones(_a   ,							_a      )]   ,							dim=1      )

																				a__:							List[str]									=[]
																				# If the number of encoder and decoder layers are present in the model configuration, both are considered
																				a__				,      a__:							int									=self.num_layers
																				a__:							List[Any]									=min(_a   ,							_a      )
																				a__:							str									=max(_a   ,							_a      ) - min_num_layers
																				a__:							Optional[Any]									="encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													a__:							Tuple									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )

													if self.use_past:
																				if not is_torch_available():
																											raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."      )
																				else:
																											import torch
																				a__				,      a__:							Tuple									=common_inputs["input_ids"].shape
																				# Not using the same length for past_key_values
																				a__:							str									=seqlen + 2
																				a__				,      a__:							List[str]									=self.num_layers
																				a__				,      a__:							Any									=self.num_attention_heads
																				a__:							Optional[int]									=(
																				    batch,
																				    num_encoder_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // num_encoder_attention_heads,
																				)

																				a__:							int									=common_inputs["attention_mask"].dtype
																				a__:							str									=torch.cat(
																				    [common_inputs["attention_mask"], torch.ones(_a   ,							_a   ,							dtype=_a      )]   ,							dim=1      )
																				a__:							int									=[
																				    (torch.zeros(_a      ), torch.zeros(_a      )) for _ in range(_a      )
																				]
													return common_inputs


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													a__:							List[str]									=compute_effective_axis_dimension(
													    _a   ,							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
													a__:							Optional[int]									=tokenizer.num_special_tokens_to_add(_a      )
													a__:							Any									=compute_effective_axis_dimension(
													    _a   ,							fixed_dimension=OnnxConfig.default_fixed_sequence   ,							num_token_to_add=_a      )

													# Generate dummy inputs according to compute batch and sequence
													a__:							Any									=[" ".join([tokenizer.unk_token]      ) * seq_length] * batch_size
													a__:							Optional[Any]									=dict(tokenizer(_a   ,							return_tensors=_a      )      )
													return common_inputs


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Any									=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )

													elif self.task == "causal-lm":
																				a__:							Dict									=self._generate_dummy_inputs_for_causal_lm(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )
													else:
																				a__:							int									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )

													return common_inputs







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	int   ,							_a    :	str   ,							_a    :	Any   ,							_a    :	str      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Tuple									=super()._flatten_past_key_values_(_a   ,							_a   ,							_a   ,							_a      )
													else:
																				a__:							Tuple									=super(_a   ,							self      )._flatten_past_key_values_(
																				    _a   ,							_a   ,							_a   ,							_a      )





