


"""simple docstring"""






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      A(  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__=0.0_2					,				)      ->	Any:

					"""simple docstring"""



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

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

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

					_UpperCamelCase					:Tuple					     =	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__   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	FlaxViTModel(config=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:Optional[int]					     =	model(SCREAMING_SNAKE_CASE__   )
					# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
					_UpperCamelCase					:List[Any]					     =	(self.image_size, self.image_size)
					_UpperCamelCase					:List[Any]					     =	(self.patch_size, self.patch_size)
					_UpperCamelCase					:int					     =	(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__   )      ->	List[str]:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	self.type_sequence_label_size
					_UpperCamelCase					:List[str]					     =	FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:List[str]					     =	model(SCREAMING_SNAKE_CASE__   )
					self.parent.assertEqual(result.logits.shape					,				(self.batch_size, self.type_sequence_label_size)   )

					# test greyscale images
					_UpperCamelCase					:Tuple					     =	1
					_UpperCamelCase					:Optional[int]					     =	FlaxViTForImageClassification(SCREAMING_SNAKE_CASE__   )

					_UpperCamelCase					:Tuple					     =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
					_UpperCamelCase					:int					     =	model(SCREAMING_SNAKE_CASE__   )





		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



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






@require_flax
class      A(  lowerCamelCase__				,							unittest.TestCase		):

		"""simple docstring"""





		A    =							(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
		def   _UpperCamelCase( self   )      ->	None:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	FlaxViTModelTester(self   )
					_UpperCamelCase					:Optional[Any]					     =	ConfigTester(self					,				config_class=SCREAMING_SNAKE_CASE__					,				has_text_modality=SCREAMING_SNAKE_CASE__					,				hidden_size=37   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					self.config_tester.run_common_tests()
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:int					     =	self.model_tester.prepare_config_and_inputs_for_common()

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

								_UpperCamelCase					:Tuple					     =	['''pixel_values''']
								self.assertListEqual(arg_names[:1]					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
								with self.subTest(model_class.__name__   ):
											_UpperCamelCase					:List[Any]					     =	self._prepare_for_class(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
											_UpperCamelCase					:Union[str, 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'''   ):
														_UpperCamelCase					:Any					     =	model_jitted(**SCREAMING_SNAKE_CASE__   ).to_tuple()

											with self.subTest('''JIT Disabled'''   ):
														with jax.disable_jit():
																	_UpperCamelCase					:Any					     =	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   )      ->	Optional[int]:

					"""simple docstring"""



					for model_class_name in self.all_model_classes:
								_UpperCamelCase					:Union[str, Any]					     =	model_class_name.from_pretrained('''google/vit-base-patch16-224'''   )
								_UpperCamelCase					:Tuple					     =	model(np.ones((1, 3, 2_24, 2_24)   )   )
								self.assertIsNotNone(SCREAMING_SNAKE_CASE__   )





"""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


UpperCamelCase__				:Dict			   =	logging.get_logger(__name__)

UpperCamelCase__				: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(  lowerCamelCase__		):

		"""simple docstring"""





		A    =							"bart"
		A    =							["past_key_values"]
		A    =							{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
		def __init__( self					,				SCREAMING_SNAKE_CASE__=5_02_65					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=40_96					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=12					,				SCREAMING_SNAKE_CASE__=40_96					,				SCREAMING_SNAKE_CASE__=16					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__="gelu"					,				SCREAMING_SNAKE_CASE__=10_24					,				SCREAMING_SNAKE_CASE__=0.1					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=0.0_2					,				SCREAMING_SNAKE_CASE__=0.0					,				SCREAMING_SNAKE_CASE__=False					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=3					,				SCREAMING_SNAKE_CASE__=1					,				SCREAMING_SNAKE_CASE__=0					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=True					,				SCREAMING_SNAKE_CASE__=2					,				SCREAMING_SNAKE_CASE__=2					,				**SCREAMING_SNAKE_CASE__					,				)      ->	Optional[Any]:

					"""simple docstring"""



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

					super().__init__(
					    num_labels=SCREAMING_SNAKE_CASE__					,				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__					,				)

					# ensure backward compatibility for BART CNN models
					if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''					,				SCREAMING_SNAKE_CASE__   ):
								_UpperCamelCase					: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(  lowerCamelCase__		):

		"""simple docstring"""





		@property
		def   _UpperCamelCase( self   )      ->	Mapping[str, Mapping[int, str]]:

					"""simple docstring"""



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

								if self.use_past:
											_UpperCamelCase					:Optional[Any]					     =	{0: '''batch'''}
											_UpperCamelCase					:int					     =	{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
											_UpperCamelCase					:Optional[Any]					     =	{0: '''batch''', 1: '''decoder_sequence'''}
											_UpperCamelCase					: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.
								_UpperCamelCase					:Tuple					     =	OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
								    ]   )
								if self.use_past:
											_UpperCamelCase     ,      _UpperCamelCase					:List[str]					     =	self.num_layers
											for i in range(SCREAMING_SNAKE_CASE__   ):
														_UpperCamelCase					:int					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
														_UpperCamelCase					:List[Any]					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
					else:
								_UpperCamelCase					:List[Any]					     =	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					:Optional[int]					     =	super().outputs
					else:
								_UpperCamelCase					:List[str]					     =	super(SCREAMING_SNAKE_CASE__					,				self   ).outputs
								if self.use_past:
											_UpperCamelCase     ,      _UpperCamelCase					:int					     =	self.num_layers
											for i in range(SCREAMING_SNAKE_CASE__   ):
														_UpperCamelCase					:Optional[Any]					     =	{0: '''batch''', 2: '''past_sequence + sequence'''}
														_UpperCamelCase					:Optional[Any]					     =	{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					:str					     =	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					:List[Any]					     =	seq_length if not self.use_past else 1
					_UpperCamelCase					: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__   )
					_UpperCamelCase					:Union[str, Any]					     =	{f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
					_UpperCamelCase					:str					     =	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					:Any					     =	common_inputs['''input_ids'''].shape
								_UpperCamelCase					:Dict					     =	common_inputs['''decoder_input_ids'''].shape[1]
								_UpperCamelCase     ,      _UpperCamelCase					:Any					     =	self.num_attention_heads
								_UpperCamelCase					:Dict					     =	(
								    batch,
								    num_encoder_attention_heads,
								    encoder_seq_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)
								_UpperCamelCase					:Tuple					     =	decoder_seq_length + 3
								_UpperCamelCase					:Optional[int]					     =	(
								    batch,
								    num_decoder_attention_heads,
								    decoder_past_length,
								    self._config.hidden_size // num_decoder_attention_heads,
								)

								_UpperCamelCase					:Optional[int]					     =	torch.cat(
								    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )]					,				dim=1   )

								_UpperCamelCase					:Union[str, Any]					     =	[]
								# If the number of encoder and decoder layers are present in the model configuration, both are considered
								_UpperCamelCase     ,      _UpperCamelCase					:int					     =	self.num_layers
								_UpperCamelCase					:Any					     =	min(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   )
								_UpperCamelCase					:Optional[int]					     =	max(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__   ) - min_num_layers
								_UpperCamelCase					:int					     =	'''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					:Optional[int]					     =	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					:Dict					     =	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					:List[Any]					     =	common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								_UpperCamelCase					:str					     =	seqlen + 2
								_UpperCamelCase     ,      _UpperCamelCase					:Dict					     =	self.num_layers
								_UpperCamelCase     ,      _UpperCamelCase					:Union[str, Any]					     =	self.num_attention_heads
								_UpperCamelCase					:List[Any]					     =	(
								    batch,
								    num_encoder_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // num_encoder_attention_heads,
								)

								_UpperCamelCase					:Tuple					     =	common_inputs['''attention_mask'''].dtype
								_UpperCamelCase					:List[Any]					     =	torch.cat(
								    [common_inputs['''attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__					,				SCREAMING_SNAKE_CASE__					,				dtype=SCREAMING_SNAKE_CASE__   )]					,				dim=1   )
								_UpperCamelCase					:int					     =	[
								    (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					:List[str]					     =	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					:str					     =	tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__   )
					_UpperCamelCase					:str					     =	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					:List[Any]					     =	[''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
					_UpperCamelCase					:Optional[Any]					     =	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					:str					     =	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					:Optional[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:
								_UpperCamelCase					:Any					     =	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__   )      ->	Dict:

					"""simple docstring"""



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

