



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import SwinaSRImageProcessor
						from transformers.image_transforms import get_image_size





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def __init__(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__=7						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=18						,	SCREAMING_SNAKE_CASE__=30						,	SCREAMING_SNAKE_CASE__=4_00						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=1 / 2_55						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=8						,	):






					"""simple docstring"""

					_snake_case					:							int					     =   parent
					_snake_case					:							List[Any]					     =   batch_size
					_snake_case					:							Tuple					     =   num_channels
					_snake_case					:							str					     =   image_size
					_snake_case					:							List[str]					     =   min_resolution
					_snake_case					:							List[str]					     =   max_resolution
					_snake_case					:							List[str]					     =   do_rescale
					_snake_case					:							int					     =   rescale_factor
					_snake_case					:							List[str]					     =   do_pad
					_snake_case					:							Optional[Any]					     =   pad_size







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return {
					    "do_rescale": self.do_rescale,
					    "rescale_factor": self.rescale_factor,
					    "do_pad": self.do_pad,
					    "pad_size": self.pad_size,
					}







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__=False      ):






					"""simple docstring"""

					assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

					if equal_resolution:
						_snake_case					:							str					     =   []
						for i in range(self.batch_size      ):
							image_inputs.append(
							    np.random.randint(
							        2_55						,	size=(self.num_channels, self.max_resolution, self.max_resolution)						,	dtype=np.uinta      )      )
					else:
						_snake_case					:							Dict					     =   []
						for i in range(self.batch_size      ):
							_snake_case     , _snake_case					:							Union[str, Any]					     =   np.random.choice(np.arange(self.min_resolution						,	self.max_resolution      )						,	2      )
							image_inputs.append(np.random.randint(2_55						,	size=(self.num_channels, width, height)						,	dtype=np.uinta      )      )

					if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						_snake_case					:							Any					     =   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1      )      ) for x in image_inputs]

					if torchify:
						_snake_case					:							str					     =   [torch.from_numpy(SCREAMING_SNAKE_CASE__      ) for x in image_inputs]

					return image_inputs





@require_torch
@require_vision
class 		__SCREAMING_SNAKE_CASE      (				lowercase__					,			unittest.TestCase      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  SwinaSRImageProcessor if is_vision_available() else None







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   SwinaSRImageProcessingTester(self      )







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return self.image_processor_tester.prepare_image_processor_dict()







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   self.image_processing_class(**self.image_processor_dict      )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"""do_rescale"""      )      )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"""rescale_factor"""      )      )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"""do_pad"""      )      )
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__						,	"""pad_size"""      )      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					pass







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case     , _snake_case					:							str					     =   get_image_size(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							int					     =   self.image_processor_tester.pad_size

					_snake_case					:							Optional[Any]					     =   (old_height // size + 1) * size - old_height
					_snake_case					:							List[Any]					     =   (old_width // size + 1) * size - old_width
					return old_height + pad_height, old_width + pad_width







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							int					     =   self.image_processing_class(**self.image_processor_dict      )
					# create random PIL images
					_snake_case					:							List[Any]					     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__      )
					for image in image_inputs:
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	Image.Image      )

					# Test not batched input
					_snake_case					:							List[Any]					     =   image_processor(image_inputs[0]						,	return_tensors="""pt"""      ).pixel_values
					_snake_case     , _snake_case					:							int					     =   self.calculate_expected_size(np.array(image_inputs[0]      )      )
					self.assertEqual(
					    encoded_images.shape						,	(
					        1,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,	)







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   self.image_processing_class(**self.image_processor_dict      )
					# create random numpy tensors
					_snake_case					:							List[str]					     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__						,	numpify=SCREAMING_SNAKE_CASE__      )
					for image in image_inputs:
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	np.ndarray      )

					# Test not batched input
					_snake_case					:							List[str]					     =   image_processor(image_inputs[0]						,	return_tensors="""pt"""      ).pixel_values
					_snake_case     , _snake_case					:							Tuple					     =   self.calculate_expected_size(image_inputs[0]      )
					self.assertEqual(
					    encoded_images.shape						,	(
					        1,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,	)







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   self.image_processing_class(**self.image_processor_dict      )
					# create random PyTorch tensors
					_snake_case					:							Union[str, Any]					     =   self.image_processor_tester.prepare_inputs(equal_resolution=SCREAMING_SNAKE_CASE__						,	torchify=SCREAMING_SNAKE_CASE__      )
					for image in image_inputs:
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	torch.Tensor      )

					# Test not batched input
					_snake_case					:							str					     =   image_processor(image_inputs[0]						,	return_tensors="""pt"""      ).pixel_values
					_snake_case     , _snake_case					:							int					     =   self.calculate_expected_size(image_inputs[0]      )
					self.assertEqual(
					    encoded_images.shape						,	(
					        1,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )						,	)










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 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  'bigbird_pegasus'
				SCREAMING_SNAKE_CASE_ 				=  ['past_key_values']
				SCREAMING_SNAKE_CASE_ 				=  {
				    'num_attention_heads': 'encoder_attention_heads',
				    'hidden_size': 'd_model',
				    'attention_probs_dropout_prob': 'attention_dropout',
				}







				def __init__(				self						,	SCREAMING_SNAKE_CASE__=9_61_03						,	SCREAMING_SNAKE_CASE__=40_96						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=40_96						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=40_96						,	SCREAMING_SNAKE_CASE__=16						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__="gelu_new"						,	SCREAMING_SNAKE_CASE__=10_24						,	SCREAMING_SNAKE_CASE__=0.1						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=0.02						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=0.0						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=0						,	SCREAMING_SNAKE_CASE__=2						,	SCREAMING_SNAKE_CASE__=1						,	SCREAMING_SNAKE_CASE__="block_sparse"						,	SCREAMING_SNAKE_CASE__=64						,	SCREAMING_SNAKE_CASE__=3						,	SCREAMING_SNAKE_CASE__=False						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

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

					# extra config
					_snake_case					:							str					     =   attention_type
					_snake_case					:							Tuple					     =   block_size
					_snake_case					:							List[Any]					     =   num_random_blocks
					_snake_case					:							List[str]					     =   use_bias

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





class 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

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

						if self.use_past:
							self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__						,	direction="""inputs"""      )
					elif self.task == "causal-lm":
						# TODO: figure this case out.
						_snake_case					:							Any					     =   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(SCREAMING_SNAKE_CASE__      ):
								_snake_case					:							Dict					     =   {0: """batch""", 2: """past_sequence + sequence"""}
								_snake_case					:							int					     =   {0: """batch""", 2: """past_sequence + sequence"""}
					else:
						_snake_case					:							Dict					     =   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      ):






					"""simple docstring"""

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

					# Generate decoder inputs
					_snake_case					:							Union[str, Any]					     =   seq_length if not self.use_past else 1
					_snake_case					:							Tuple					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Union[str, Any]					     =   {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
					_snake_case					:							Optional[int]					     =   dict(**SCREAMING_SNAKE_CASE__						,	**SCREAMING_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					:							int					     =   common_inputs["""input_ids"""].shape
						_snake_case					:							List[Any]					     =   common_inputs["""decoder_input_ids"""].shape[1]
						_snake_case     , _snake_case					:							List[Any]					     =   self.num_attention_heads
						_snake_case					:							Any					     =   (
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						_snake_case					:							int					     =   decoder_seq_length + 3
						_snake_case					:							List[Any]					     =   (
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

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

						_snake_case					:							Dict					     =   []
						# 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(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
						_snake_case					:							Any					     =   max(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ) - min_num_layers
						_snake_case					:							Union[str, Any]					     =   """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

					_snake_case					:							Tuple					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
					    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_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					:							Optional[Any]					     =   common_inputs["""input_ids"""].shape
						# Not using the same length for past_key_values
						_snake_case					:							Dict					     =   seqlen + 2
						_snake_case     , _snake_case					:							Optional[int]					     =   self.num_layers
						_snake_case     , _snake_case					:							List[Any]					     =   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					:							Optional[int]					     =   common_inputs["""attention_mask"""].dtype
						_snake_case					:							str					     =   torch.cat(
						    [common_inputs["""attention_mask"""], torch.ones(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	dtype=SCREAMING_SNAKE_CASE__      )]						,	dim=1      )
						_snake_case					:							Tuple					     =   [
						    (torch.zeros(SCREAMING_SNAKE_CASE__      ), torch.zeros(SCREAMING_SNAKE_CASE__      )) for _ in range(SCREAMING_SNAKE_CASE__      )
						]
					return common_inputs







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   compute_effective_axis_dimension(
					    SCREAMING_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[str]					     =   tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Tuple					     =   compute_effective_axis_dimension(
					    SCREAMING_SNAKE_CASE__						,	fixed_dimension=OnnxConfig.default_fixed_sequence						,	num_token_to_add=SCREAMING_SNAKE_CASE__      )

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







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = -1						,	SCREAMING_SNAKE_CASE__ = False						,	SCREAMING_SNAKE_CASE__ = None						,	):






					"""simple docstring"""

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

					elif self.task == "causal-lm":
						_snake_case					:							List[Any]					     =   self._generate_dummy_inputs_for_causal_lm(
						    SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__						,	seq_length=SCREAMING_SNAKE_CASE__						,	is_pair=SCREAMING_SNAKE_CASE__						,	framework=SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							Dict					     =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__						,	batch_size=SCREAMING_SNAKE_CASE__						,	seq_length=SCREAMING_SNAKE_CASE__						,	is_pair=SCREAMING_SNAKE_CASE__						,	framework=SCREAMING_SNAKE_CASE__      )

					return common_inputs







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					if self.task in ["default", "seq2seq-lm"]:
						_snake_case					:							Union[str, Any]					     =   super()._flatten_past_key_values_(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
					else:
						_snake_case					:							int					     =   super(SCREAMING_SNAKE_CASE__						,	self      )._flatten_past_key_values_(
						    SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )





