

'''simple docstring'''


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
			import torch

if is_vision_available():
			import PIL


lowerCAmelCase__						:       Tuple										=						logging.get_logger(__name__)




def 	_a       (				__lowerCAmelCase		:      np.ndarray   ,							__lowerCAmelCase		:      Union[int, Iterable[int]]   ,							__lowerCAmelCase		:      bool   ,							__lowerCAmelCase		:      int     ):

		"""simple docstring"""

		def constraint_to_multiple_of(__lowerCAmelCase		:      Optional[int]   ,							__lowerCAmelCase		:      List[Any]   ,							__lowerCAmelCase		:      Tuple=0   ,							__lowerCAmelCase		:      Dict=None     ):
				snake_case__						:       List[Any]       						=  round(val / multiple     ) * multiple

				if max_val is not None and x > max_val:
						snake_case__						:       Optional[int]       						=  math.floor(val / multiple     ) * multiple

				if x < min_val:
						snake_case__						:       Union[str, Any]       						=  math.ceil(val / multiple     ) * multiple

				return x

		snake_case__						:       List[str]       						=  (output_size, output_size) if isinstance(__lowerCAmelCase   ,							__lowerCAmelCase     ) else output_size

		snake_case__						:       Optional[int]       						=  get_image_size(__lowerCAmelCase     )
		snake_case__						:       Tuple       						=  output_size

		# determine new height and width
		snake_case__						:       int       						=  output_height / input_height
		snake_case__						:       Union[str, Any]       						=  output_width / input_width

		if keep_aspect_ratio:
				# scale as little as possible
				if abs(1 - scale_width     ) < abs(1 - scale_height     ):
						# fit width
						snake_case__						:       Union[str, Any]       						=  scale_width
				else:
						# fit height
						snake_case__						:       List[Any]       						=  scale_height

		snake_case__						:       Tuple       						=  constraint_to_multiple_of(scale_height * input_height   ,							multiple=__lowerCAmelCase     )
		snake_case__						:       Any       						=  constraint_to_multiple_of(scale_width * input_width   ,							multiple=__lowerCAmelCase     )

		return (new_height, new_width)





class     a			(				_snake_case       ):






						"""simple docstring"""




						__UpperCAmelCase			     =    ["""pixel_values"""]







						def __init__(							self					:      Tuple			,			snake_case_					:      Union[str, Any] = True			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      Union[str, Any] = PILImageResampling.BILINEAR			,			snake_case_					:      Dict = False			,			snake_case_					:      Union[str, Any] = 1			,			snake_case_					:      Dict = True			,			snake_case_					:      Optional[Any] = 1 / 2_5_5			,			snake_case_					:      str = True			,			snake_case_					:      List[Any] = None			,			snake_case_					:      Any = None			,			**snake_case_					:      str			,			):



								'''simple docstring'''



								super().__init__(**snake_case_  )
								snake_case__						:       str       						=  size if size is not None else {"height": 3_8_4, "width": 3_8_4}
								snake_case__						:       int       						=  get_size_dict(snake_case_  )
								snake_case__						:       Optional[Any]       						=  do_resize
								snake_case__						:       List[Any]       						=  size
								snake_case__						:       Optional[Any]       						=  keep_aspect_ratio
								snake_case__						:       Dict       						=  ensure_multiple_of
								snake_case__						:       Optional[Any]       						=  resample
								snake_case__						:       Optional[int]       						=  do_rescale
								snake_case__						:       List[str]       						=  rescale_factor
								snake_case__						:       Any       						=  do_normalize
								snake_case__						:       Optional[Any]       						=  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								snake_case__						:       Optional[int]       						=  image_std if image_std is not None else IMAGENET_STANDARD_STD







						def       __magic_name__							(							self					:      List[str]			,			snake_case_					:      Tuple			,			snake_case_					:      int			,			snake_case_					:      List[str] = False			,			snake_case_					:      List[Any] = 1			,			snake_case_					:      Optional[int] = PILImageResampling.BICUBIC			,			snake_case_					:      List[Any] = None			,			**snake_case_					:      Dict			,			):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  get_size_dict(snake_case_  )
								if "height" not in size or "width" not in size:
										raise ValueError(F"""The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}"""  )
								snake_case__						:       List[str]       						=  get_resize_output_image_size(
								    snake_case_			,			output_size=(size['''height'''], size['''width'''])			,			keep_aspect_ratio=snake_case_			,			multiple=snake_case_			,			)
								return resize(snake_case_			,			size=snake_case_			,			resample=snake_case_			,			data_format=snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      List[str]			,			snake_case_					:      int			,			snake_case_					:      int = None			,			**snake_case_					:      List[Any]			,			):



								'''simple docstring'''



								return rescale(snake_case_			,			scale=snake_case_			,			data_format=snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]			,			snake_case_					:      Union[str, Any]			,			snake_case_					:      Any			,			snake_case_					:      List[str]			,			snake_case_					:      int = None			,			**snake_case_					:      Any			,			):



								'''simple docstring'''



								return normalize(snake_case_			,			mean=snake_case_			,			std=snake_case_			,			data_format=snake_case_			,			**snake_case_  )







						def       __magic_name__							(							self					:      int			,			snake_case_					:      int			,			snake_case_					:      str = None			,			snake_case_					:      Tuple = None			,			snake_case_					:      Union[str, Any] = None			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      Dict = None			,			snake_case_					:      Optional[Any] = None			,			snake_case_					:      Any = None			,			snake_case_					:      List[Any] = None			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      Optional[int] = None			,			snake_case_					:      List[Any] = None			,			snake_case_					:      int = ChannelDimension.FIRST			,			**snake_case_					:      List[str]			,			):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  do_resize if do_resize is not None else self.do_resize
								snake_case__						:       Union[str, Any]       						=  size if size is not None else self.size
								snake_case__						:       str       						=  get_size_dict(snake_case_  )
								snake_case__						:       List[Any]       						=  keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								snake_case__						:       List[Any]       						=  ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								snake_case__						:       str       						=  resample if resample is not None else self.resample
								snake_case__						:       Tuple       						=  do_rescale if do_rescale is not None else self.do_rescale
								snake_case__						:       Dict       						=  rescale_factor if rescale_factor is not None else self.rescale_factor
								snake_case__						:       Optional[int]       						=  do_normalize if do_normalize is not None else self.do_normalize
								snake_case__						:       int       						=  image_mean if image_mean is not None else self.image_mean
								snake_case__						:       Optional[Any]       						=  image_std if image_std is not None else self.image_std

								snake_case__						:       List[str]       						=  make_list_of_images(snake_case_  )

								if not valid_images(snake_case_  ):
										raise ValueError(
										    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
										    '''torch.Tensor, tf.Tensor or jax.ndarray.'''  )

								if do_resize and size is None or resample is None:
										raise ValueError('''Size and resample must be specified if do_resize is True.'''  )

								if do_rescale and rescale_factor is None:
										raise ValueError('''Rescale factor must be specified if do_rescale is True.'''  )

								if do_normalize and (image_mean is None or image_std is None):
										raise ValueError('''Image mean and std must be specified if do_normalize is True.'''  )

								# All transformations expect numpy arrays.
								snake_case__						:       int       						=  [to_numpy_array(snake_case_  ) for image in images]

								if do_resize:
										snake_case__						:       List[Any]       						=  [self.resize(image=snake_case_			,			size=snake_case_			,			resample=snake_case_  ) for image in images]

								if do_rescale:
										snake_case__						:       Dict       						=  [self.rescale(image=snake_case_			,			scale=snake_case_  ) for image in images]

								if do_normalize:
										snake_case__						:       List[Any]       						=  [self.normalize(image=snake_case_			,			mean=snake_case_			,			std=snake_case_  ) for image in images]

								snake_case__						:       Union[str, Any]       						=  [to_channel_dimension_format(snake_case_			,			snake_case_  ) for image in images]

								snake_case__						:       Dict       						=  {"pixel_values": images}
								return BatchFeature(data=snake_case_			,			tensor_type=snake_case_  )







						def       __magic_name__							(							self					:      int			,			snake_case_					:      Tuple			,			snake_case_					:      Dict = None  ):



								'''simple docstring'''



								snake_case__						:       List[Any]       						=  outputs.logits

								# Resize logits and compute semantic segmentation maps
								if target_sizes is not None:
										if len(snake_case_  ) != len(snake_case_  ):
												raise ValueError(
												    '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''  )

										if is_torch_tensor(snake_case_  ):
												snake_case__						:       Optional[int]       						=  target_sizes.numpy()

										snake_case__						:       Any       						=  []

										for idx in range(len(snake_case_  )  ):
												snake_case__						:       Dict       						=  torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0  )			,			size=target_sizes[idx]			,			mode='''bilinear'''			,			align_corners=snake_case_  )
												snake_case__						:       int       						=  resized_logits[0].argmax(dim=0  )
												semantic_segmentation.append(snake_case_  )
								else:
										snake_case__						:       Tuple       						=  logits.argmax(dim=1  )
										snake_case__						:       Optional[Any]       						=  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]  )]

								return semantic_segmentation






'''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_  )



