

'''simple docstring'''


import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch

if is_datasets_available():
			from datasets import load_dataset

lowerCAmelCase__						:       Any										=						random.Random()




def 	_a       (				__lowerCAmelCase		:      Dict   ,							__lowerCAmelCase		:      int=1.0   ,							__lowerCAmelCase		:      List[Any]=None   ,							__lowerCAmelCase		:      Optional[int]=None     ):

		"""simple docstring"""

		if rng is None:
				snake_case__						:       Optional[int]       						=  global_rng

		snake_case__						:       List[Any]       						=  []
		for batch_idx in range(shape[0]     ):
				values.append([]     )
				for _ in range(shape[1]     ):
						values[-1].append(rng.random() * scale     )

		return values




class     a			(				unittest.TestCase       ):






						"""simple docstring"""




						def __init__(							self					:      Optional[Any]			,			snake_case_					:      List[Any]			,			snake_case_					:      Tuple=7			,			snake_case_					:      str=4_0_0			,			snake_case_					:      Optional[Any]=2_0_0_0			,			snake_case_					:      List[Any]=2_0_4_8			,			snake_case_					:      Optional[int]=1_2_8			,			snake_case_					:      Optional[int]=1			,			snake_case_					:      Dict=5_1_2			,			snake_case_					:      str=3_0			,			snake_case_					:      Optional[int]=4_4_1_0_0			,			):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  parent
								snake_case__						:       int       						=  batch_size
								snake_case__						:       List[str]       						=  min_seq_length
								snake_case__						:       List[str]       						=  max_seq_length
								snake_case__						:       List[str]       						=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								snake_case__						:       int       						=  spectrogram_length
								snake_case__						:       str       						=  feature_size
								snake_case__						:       Optional[int]       						=  num_audio_channels
								snake_case__						:       int       						=  hop_length
								snake_case__						:       Any       						=  chunk_length
								snake_case__						:       Optional[int]       						=  sampling_rate







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



								'''simple docstring'''



								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}







						def       __magic_name__							(							self					:      Optional[Any]			,			snake_case_					:      int=False			,			snake_case_					:      Tuple=False  ):



								'''simple docstring'''



								def _flatten(snake_case_					:      Tuple  ):
										return list(itertools.chain(*_a  )  )

								if equal_length:
										snake_case__						:       Dict       						=  [floats_list((self.max_seq_length, self.feature_size)  ) for _ in range(self.batch_size  )]
								else:
										# make sure that inputs increase in size
										snake_case__						:       Tuple       						=  [
										    floats_list((x, self.feature_size)  )
										    for x in range(self.min_seq_length			,			self.max_seq_length			,			self.seq_length_diff  )
										]
								if numpify:
										snake_case__						:       Tuple       						=  [np.asarray(_a  ) for x in speech_inputs]
								return speech_inputs





@require_torch
@require_torchaudio
class     a			(				__SCREAMING_SNAKE_CASE			,			unittest.TestCase       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    TvltFeatureExtractor







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  TvltFeatureExtractionTester(self  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       List[str]       						=  self.feature_extraction_class(**self.feat_extract_dict  )
								self.assertTrue(hasattr(_a			,			'''spectrogram_length'''  )  )
								self.assertTrue(hasattr(_a			,			'''feature_size'''  )  )
								self.assertTrue(hasattr(_a			,			'''num_audio_channels'''  )  )
								self.assertTrue(hasattr(_a			,			'''hop_length'''  )  )
								self.assertTrue(hasattr(_a			,			'''chunk_length'''  )  )
								self.assertTrue(hasattr(_a			,			'''sampling_rate'''  )  )







						def       __magic_name__							(							self					:      List[Any]  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.feature_extraction_class(**self.feat_extract_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       Any       						=  feat_extract_first.save_pretrained(_a  )[0]
										check_json_file_has_correct_format(_a  )
										snake_case__						:       Optional[int]       						=  self.feature_extraction_class.from_pretrained(_a  )

								snake_case__						:       Any       						=  feat_extract_first.to_dict()
								snake_case__						:       Any       						=  feat_extract_second.to_dict()
								snake_case__						:       List[Any]       						=  dict_first.pop('''mel_filters'''  )
								snake_case__						:       Union[str, Any]       						=  dict_second.pop('''mel_filters'''  )
								self.assertTrue(np.allclose(_a			,			_a  )  )
								self.assertEqual(_a			,			_a  )







						def       __magic_name__							(							self					:      Tuple  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  self.feature_extraction_class(**self.feat_extract_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       int       						=  os.path.join(_a			,			'''feat_extract.json'''  )
										feat_extract_first.to_json_file(_a  )
										snake_case__						:       Dict       						=  self.feature_extraction_class.from_json_file(_a  )

								snake_case__						:       Union[str, Any]       						=  feat_extract_first.to_dict()
								snake_case__						:       str       						=  feat_extract_second.to_dict()
								snake_case__						:       Any       						=  dict_first.pop('''mel_filters'''  )
								snake_case__						:       Any       						=  dict_second.pop('''mel_filters'''  )
								self.assertTrue(np.allclose(_a			,			_a  )  )
								self.assertEqual(_a			,			_a  )







						def       __magic_name__							(							self					:      Optional[Any]  ):



								'''simple docstring'''



								snake_case__						:       Dict       						=  self.feature_extraction_class(**self.feat_extract_dict  )

								# create three inputs of length 800, 1000, and 1200
								snake_case__						:       Optional[int]       						=  [floats_list((1, x)  )[0] for x in range(8_0_0			,			1_4_0_0			,			2_0_0  )]
								snake_case__						:       int       						=  [np.asarray(_a  ) for speech_input in speech_inputs]

								# Test not batched input
								snake_case__						:       Dict       						=  feature_extractor(np_speech_inputs[0]			,			return_tensors='''np'''			,			sampling_rate=4_4_1_0_0  ).audio_values

								self.assertTrue(encoded_audios.ndim == 4  )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

								# Test batched
								snake_case__						:       Optional[int]       						=  feature_extractor(_a			,			return_tensors='''np'''			,			sampling_rate=4_4_1_0_0  ).audio_values

								self.assertTrue(encoded_audios.ndim == 4  )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

								# Test audio masking
								snake_case__						:       Dict       						=  feature_extractor(
								    _a			,			return_tensors='''np'''			,			sampling_rate=4_4_1_0_0			,			mask_audio=_a  ).audio_values

								self.assertTrue(encoded_audios.ndim == 4  )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )

								# Test 2-D numpy arrays are batched.
								snake_case__						:       str       						=  [floats_list((1, x)  )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
								snake_case__						:       Dict       						=  np.asarray(_a  )
								snake_case__						:       List[str]       						=  feature_extractor(_a			,			return_tensors='''np'''			,			sampling_rate=4_4_1_0_0  ).audio_values
								self.assertTrue(encoded_audios.ndim == 4  )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size  )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length  )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels  )







						def       __magic_name__							(							self					:      Dict			,			snake_case_					:      Any  ):



								'''simple docstring'''



								snake_case__						:       str       						=  load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,			'''clean'''			,			split='''validation'''  )
								# automatic decoding with librispeech
								snake_case__						:       List[str]       						=  ds.sort('''id'''  ).select(range(_a  )  )[:num_samples]['''audio''']

								return [x["array"] for x in speech_samples]







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self._load_datasamples(1  )
								snake_case__						:       str       						=  TvltFeatureExtractor()
								snake_case__						:       Dict       						=  feature_extractor(_a			,			return_tensors='''pt'''  ).audio_values

								self.assertEquals(audio_values.shape			,			(1, 1, 1_9_2, 1_2_8)  )

								snake_case__						:       str       						=  torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]  )
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]			,			_a			,			atol=1e-4  )  )






'''simple docstring'''


import warnings
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


lowerCAmelCase__						:       Optional[Any]										=						logging.get_logger(__name__)

lowerCAmelCase__						:       Optional[int]										=						{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    """bart"""
						__UpperCAmelCase			     =    ["""past_key_values"""]
						__UpperCAmelCase			     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







						def __init__(							self					:      Optional[Any]			,			snake_case_					:      Tuple=5_0_2_6_5			,			snake_case_					:      Dict=1_0_2_4			,			snake_case_					:      int=1_2			,			snake_case_					:      int=4_0_9_6			,			snake_case_					:      str=1_6			,			snake_case_					:      List[Any]=1_2			,			snake_case_					:      List[Any]=4_0_9_6			,			snake_case_					:      Any=1_6			,			snake_case_					:      str=0.0			,			snake_case_					:      Optional[int]=0.0			,			snake_case_					:      List[Any]="gelu"			,			snake_case_					:      List[Any]=1_0_2_4			,			snake_case_					:      Union[str, Any]=0.1			,			snake_case_					:      int=0.0			,			snake_case_					:      Tuple=0.0			,			snake_case_					:      Optional[Any]=0.0_2			,			snake_case_					:      Dict=0.0			,			snake_case_					:      str=False			,			snake_case_					:      Optional[int]=True			,			snake_case_					:      Any=3			,			snake_case_					:      int=1			,			snake_case_					:      int=0			,			snake_case_					:      Optional[Any]=2			,			snake_case_					:      str=True			,			snake_case_					:      int=2			,			snake_case_					:      Union[str, Any]=2			,			**snake_case_					:      int			,			):



								'''simple docstring'''



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

								super().__init__(
								    num_labels=snake_case_			,			pad_token_id=snake_case_			,			bos_token_id=snake_case_			,			eos_token_id=snake_case_			,			is_encoder_decoder=snake_case_			,			decoder_start_token_id=snake_case_			,			forced_eos_token_id=snake_case_			,			**snake_case_			,			)

								# ensure backward compatibility for BART CNN models
								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''			,			snake_case_  ):
										snake_case__						:       Union[str, Any]       						=  self.bos_token_id
										warnings.warn(
										    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''  )





class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						@property
						def       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
										snake_case__						:       Optional[int]       						=  OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]  )

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

										if self.use_past:
												self.fill_with_past_key_values_(snake_case_			,			direction='''inputs'''  )
								elif self.task == "causal-lm":
										# TODO: figure this case out.
										snake_case__						:       int       						=  OrderedDict(
										    [
										        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
										        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
										    ]  )
										if self.use_past:
												snake_case__						,       snake_case__						:       Tuple       						=  self.num_layers
												for i in range(snake_case_  ):
														snake_case__						:       List[str]       						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
														snake_case__						:       Tuple       						=  {0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
										snake_case__						:       List[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
						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



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







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



								'''simple docstring'''



								snake_case__						:       Union[str, Any]       						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )

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

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

										snake_case__						:       Optional[Any]       						=  torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(snake_case_			,			snake_case_  )]			,			dim=1  )

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

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







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      PreTrainedTokenizer			,			snake_case_					:      int = -1			,			snake_case_					:      int = -1			,			snake_case_					:      bool = False			,			snake_case_					:      Optional[TensorType] = None			,			):



								'''simple docstring'''



								snake_case__						:       Any       						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    snake_case_			,			snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )

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

										snake_case__						:       List[Any]       						=  common_inputs['''attention_mask'''].dtype
										snake_case__						:       List[Any]       						=  torch.cat(
										    [common_inputs['''attention_mask'''], torch.ones(snake_case_			,			snake_case_			,			dtype=snake_case_  )]			,			dim=1  )
										snake_case__						:       Optional[Any]       						=  [
										    (torch.zeros(snake_case_  ), torch.zeros(snake_case_  )) for _ in range(snake_case_  )
										]
								return common_inputs







						def       __magic_name__							(							self					:      int			,			snake_case_					:      PreTrainedTokenizer			,			snake_case_					:      int = -1			,			snake_case_					:      int = -1			,			snake_case_					:      bool = False			,			snake_case_					:      Optional[TensorType] = None			,			):



								'''simple docstring'''



								snake_case__						:       str       						=  compute_effective_axis_dimension(
								    snake_case_			,			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
								snake_case__						:       List[Any]       						=  tokenizer.num_special_tokens_to_add(snake_case_  )
								snake_case__						:       List[str]       						=  compute_effective_axis_dimension(
								    snake_case_			,			fixed_dimension=OnnxConfig.default_fixed_sequence			,			num_token_to_add=snake_case_  )

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







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



								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
										snake_case__						:       int       						=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
										    snake_case_			,			batch_size=snake_case_			,			seq_length=snake_case_			,			is_pair=snake_case_			,			framework=snake_case_  )

								elif self.task == "causal-lm":
										snake_case__						:       Optional[Any]       						=  self._generate_dummy_inputs_for_causal_lm(
										    snake_case_			,			batch_size=snake_case_			,			seq_length=snake_case_			,			is_pair=snake_case_			,			framework=snake_case_  )
								else:
										snake_case__						:       Optional[int]       						=  self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    snake_case_			,			batch_size=snake_case_			,			seq_length=snake_case_			,			is_pair=snake_case_			,			framework=snake_case_  )

								return common_inputs







						def       __magic_name__							(							self					:      Tuple			,			snake_case_					:      List[str]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Any			,			snake_case_					:      Any  ):



								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
										snake_case__						:       Optional[Any]       						=  super()._flatten_past_key_values_(snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )
								else:
										snake_case__						:       Union[str, Any]       						=  super(snake_case_			,			self  )._flatten_past_key_values_(
										    snake_case_			,			snake_case_			,			snake_case_			,			snake_case_  )



