




import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel






class 		_lowerCAmelCase       (							unittest.TestCase	):
			"""simple docstring"""
			def __init__(					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE=13     ,		__SCREAMING_SNAKE_CASE=30     ,		__SCREAMING_SNAKE_CASE=2     ,		__SCREAMING_SNAKE_CASE=3     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE=True     ,		__SCREAMING_SNAKE_CASE=32     ,		__SCREAMING_SNAKE_CASE=5     ,		__SCREAMING_SNAKE_CASE=4     ,		__SCREAMING_SNAKE_CASE=37     ,		__SCREAMING_SNAKE_CASE="gelu"     ,		__SCREAMING_SNAKE_CASE=0.1     ,		__SCREAMING_SNAKE_CASE=0.1     ,		__SCREAMING_SNAKE_CASE=10     ,		__SCREAMING_SNAKE_CASE=0.02     ,		)		->							int:





				"""simple docstring"""







				snake_case__						:     List[Any]		=parent
				snake_case__						:     Optional[int]		=batch_size
				snake_case__						:     int		=image_size
				snake_case__						:     int		=patch_size
				snake_case__						:     int		=num_channels
				snake_case__						:     List[Any]		=is_training
				snake_case__						:     Any		=use_labels
				snake_case__						:     str		=hidden_size
				snake_case__						:     Dict		=num_hidden_layers
				snake_case__						:     Dict		=num_attention_heads
				snake_case__						:     Optional[int]		=intermediate_size
				snake_case__						:     Optional[int]		=hidden_act
				snake_case__						:     Optional[Any]		=hidden_dropout_prob
				snake_case__						:     Dict		=attention_probs_dropout_prob
				snake_case__						:     List[Any]		=type_sequence_label_size
				snake_case__						:     List[str]		=initializer_range

				# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
				snake_case__						:     Optional[Any]		=(image_size // patch_size) ** 2
				snake_case__						:     Union[str, Any]		=num_patches + 1
			def 					UpperCAmelCase     (					self       )		->							Optional[int]:





				"""simple docstring"""







				snake_case__						:     int		=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

				snake_case__						:     int		=ViTConfig(
				    image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		intermediate_size=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		is_decoder=_SCREAMING_SNAKE_CASE     ,		initializer_range=self.initializer_range     ,		)

				return config, pixel_values
			def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE       )		->							str:





				"""simple docstring"""







				snake_case__						:     Any		=FlaxViTModel(config=_SCREAMING_SNAKE_CASE       )
				snake_case__						:     int		=model(_SCREAMING_SNAKE_CASE       )
				# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
				snake_case__						:     Optional[Any]		=(self.image_size, self.image_size)
				snake_case__						:     List[Any]		=(self.patch_size, self.patch_size)
				snake_case__						:     Union[str, Any]		=(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
				self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, num_patches + 1, self.hidden_size)       )
			def 					UpperCAmelCase     (					self     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE       )		->							Any:





				"""simple docstring"""







				snake_case__						:     Optional[Any]		=self.type_sequence_label_size
				snake_case__						:     List[Any]		=FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE       )
				snake_case__						:     int		=model(_SCREAMING_SNAKE_CASE       )
				self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)       )

				# test greyscale images
				snake_case__						:     Tuple		=1
				snake_case__						:     str		=FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE       )

				snake_case__						:     int		=floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
				snake_case__						:     Dict		=model(_SCREAMING_SNAKE_CASE       )


			def 					UpperCAmelCase     (					self       )		->							str:





				"""simple docstring"""







				snake_case__						:     Optional[Any]		=self.prepare_config_and_inputs()
				(
				    (
				    snake_case__			
				),    (
				    snake_case__			
				),    			
				)						:     Tuple		=config_and_inputs
				snake_case__						:     Tuple		={'''pixel_values''': pixel_values}
				return config, inputs_dict






@require_flax
class 		_lowerCAmelCase       (							__UpperCamelCase			,    unittest.TestCase	):
			"""simple docstring"""
			lowerCAmelCase__           =(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
			def 					UpperCAmelCase     (					self       )		->							None:





				"""simple docstring"""







				snake_case__						:     Optional[Any]		=FlaxViTModelTester(self       )
				snake_case__						:     List[Any]		=ConfigTester(self     ,		config_class=_SCREAMING_SNAKE_CASE     ,		has_text_modality=_SCREAMING_SNAKE_CASE     ,		hidden_size=37       )
			def 					UpperCAmelCase     (					self       )		->							Dict:





				"""simple docstring"""







				self.config_tester.run_common_tests()
			def 					UpperCAmelCase     (					self       )		->							Optional[Any]:





				"""simple docstring"""







				snake_case__						:     Union[str, Any]		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE       )
			def 					UpperCAmelCase     (					self       )		->							List[Any]:





				"""simple docstring"""







				snake_case__						:     Optional[int]		=self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE       )
			def 					UpperCAmelCase     (					self       )		->							str:





				"""simple docstring"""







				snake_case__,    snake_case__						:     Optional[Any]		=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					snake_case__						:     Optional[int]		=model_class(_SCREAMING_SNAKE_CASE       )
					snake_case__						:     int		=inspect.signature(model.__call__       )
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					snake_case__						:     Union[str, Any]		=[*signature.parameters.keys()]

					snake_case__						:     List[Any]		=['''pixel_values''']
					self.assertListEqual(arg_names[:1]     ,		_SCREAMING_SNAKE_CASE       )
			def 					UpperCAmelCase     (					self       )		->							int:





				"""simple docstring"""







				snake_case__,    snake_case__						:     List[Any]		=self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__       ):
						snake_case__						:     Union[str, Any]		=self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE       )
						snake_case__						:     Optional[Any]		=model_class(_SCREAMING_SNAKE_CASE       )

						@jax.jit
						def model_jitted(__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE       ):
							return model(pixel_values=_SCREAMING_SNAKE_CASE     ,		**_SCREAMING_SNAKE_CASE       )

						with self.subTest('''JIT Enabled'''       ):
							snake_case__						:     Union[str, Any]		=model_jitted(**_SCREAMING_SNAKE_CASE       ).to_tuple()

						with self.subTest('''JIT Disabled'''       ):
							with jax.disable_jit():
								snake_case__						:     Optional[int]		=model_jitted(**_SCREAMING_SNAKE_CASE       ).to_tuple()

						self.assertEqual(len(_SCREAMING_SNAKE_CASE       )     ,		len(_SCREAMING_SNAKE_CASE       )       )
						for jitted_output, output in zip(_SCREAMING_SNAKE_CASE     ,		_SCREAMING_SNAKE_CASE       ):
							self.assertEqual(jitted_output.shape     ,		output.shape       )


			@slow
			def 					UpperCAmelCase     (					self       )		->							Dict:





				"""simple docstring"""







				for model_class_name in self.all_model_classes:
					snake_case__						:     List[str]		=model_class_name.from_pretrained('''google/vit-base-patch16-224'''       )
					snake_case__						:     List[str]		=model(np.ones((1, 3, 224, 224)       )       )
					self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )








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

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


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

lowerCAmelCase__    :int       =				{
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Dict           =      'blenderbot-small'
		_a      :    Tuple           =      ['past_key_values']
		_a      :    List[str]           =      {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=50265     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=2048     ,       _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"     ,       _SCREAMING_SNAKE_CASE=512     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.0     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=False     ,       _SCREAMING_SNAKE_CASE=0     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=2     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Dict:




									"""simple docstring"""





									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     encoder_ffn_dim
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     encoder_attention_heads
									_UpperCAmelCase											=     decoder_ffn_dim
									_UpperCAmelCase											=     decoder_layers
									_UpperCAmelCase											=     decoder_attention_heads
									_UpperCAmelCase											=     dropout
									_UpperCAmelCase											=     attention_dropout
									_UpperCAmelCase											=     activation_dropout
									_UpperCAmelCase											=     activation_function
									_UpperCAmelCase											=     init_std
									_UpperCAmelCase											=     encoder_layerdrop
									_UpperCAmelCase											=     decoder_layerdrop
									_UpperCAmelCase											=     use_cache
									_UpperCAmelCase											=     encoder_layers
									_UpperCAmelCase											=     scale_embedding  # scale factor will be sqrt(d_model) if True

									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     ,       forced_eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE     ,       )



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





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

																if self.use_past:
																							_UpperCAmelCase											=     {0: 'batch'}
																							_UpperCAmelCase											=     {0: 'batch', 1: 'past_decoder_sequence + sequence'}
																else:
																							_UpperCAmelCase											=     {0: 'batch', 1: 'decoder_sequence'}
																							_UpperCAmelCase											=     {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.
																_UpperCAmelCase											=     OrderedDict(
																    [
																        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
																        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
																    ]		)
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									else:
																_UpperCAmelCase											=     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       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super().outputs
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		).outputs
																if self.use_past:
																							_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																							for i in range(_SCREAMING_SNAKE_CASE		):
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
																														_UpperCAmelCase											=     {0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     seq_length if not self.use_past else 1
									_UpperCAmelCase											=     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		)
									_UpperCAmelCase											=     {f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
									_UpperCAmelCase											=     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
																_UpperCAmelCase						,			_UpperCAmelCase											=     common_inputs['input_ids'].shape
																_UpperCAmelCase											=     common_inputs['decoder_input_ids'].shape[1]
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																_UpperCAmelCase											=     decoder_seq_length + 3
																_UpperCAmelCase											=     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['decoder_attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)]     ,       dim=1		)

																_UpperCAmelCase											=     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase											=     min(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     max(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) - min_num_layers
																_UpperCAmelCase											=     '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.
																_UpperCAmelCase											=     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       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     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
																_UpperCAmelCase						,			_UpperCAmelCase											=     common_inputs['input_ids'].shape
																# Not using the same length for past_key_values
																_UpperCAmelCase											=     seqlen + 2
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_layers
																_UpperCAmelCase						,			_UpperCAmelCase											=     self.num_attention_heads
																_UpperCAmelCase											=     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																_UpperCAmelCase											=     common_inputs['attention_mask'].dtype
																_UpperCAmelCase											=     torch.cat(
																    [common_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       dtype=_SCREAMING_SNAKE_CASE		)]     ,       dim=1		)
																_UpperCAmelCase											=     [
																    (torch.zeros(_SCREAMING_SNAKE_CASE		), torch.zeros(_SCREAMING_SNAKE_CASE		)) for _ in range(_SCREAMING_SNAKE_CASE		)
																]
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     tokenizer.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     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
									_UpperCAmelCase											=     [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
									_UpperCAmelCase											=     dict(tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
									return common_inputs
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     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":
																_UpperCAmelCase											=     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:
																_UpperCAmelCase											=     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       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			List[str]:




									"""simple docstring"""





									if self.task in ["default", "seq2seq-lm"]:
																_UpperCAmelCase											=     super()._flatten_past_key_values_(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
									else:
																_UpperCAmelCase											=     super(_SCREAMING_SNAKE_CASE     ,       self		)._flatten_past_key_values_(
																    _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
