



"""simple docstring"""






import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor

class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					def __init__(	self							:  List[Any],  lowerCamelCase							:  List[str],  lowerCamelCase							:  List[Any]=13,  lowerCamelCase							:  int=30,  lowerCamelCase							:  Optional[Any]=2,  lowerCamelCase							:  Dict=3,  lowerCamelCase							:  int=True,  lowerCamelCase							:  Optional[Any]=True,  lowerCamelCase							:  Tuple=32,  lowerCamelCase							:  Any=5,  lowerCamelCase							:  str=4,  lowerCamelCase							:  int=37,  lowerCamelCase							:  Optional[int]="gelu",  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  List[str]=0.1,  lowerCamelCase							:  int=10,  lowerCamelCase							:  Tuple=0.02,  lowerCamelCase							:  int=None,  )-> List[Any]:
								lowerCamelCase__		:		Union[str, Any]		     =parent
								lowerCamelCase__		:		str		     =batch_size
								lowerCamelCase__		:		Dict		     =image_size
								lowerCamelCase__		:		List[Any]		     =patch_size
								lowerCamelCase__		:		List[Any]		     =num_channels
								lowerCamelCase__		:		Any		     =is_training
								lowerCamelCase__		:		Optional[int]		     =use_labels
								lowerCamelCase__		:		Union[str, Any]		     =hidden_size
								lowerCamelCase__		:		List[Any]		     =num_hidden_layers
								lowerCamelCase__		:		Union[str, Any]		     =num_attention_heads
								lowerCamelCase__		:		Tuple		     =intermediate_size
								lowerCamelCase__		:		List[Any]		     =hidden_act
								lowerCamelCase__		:		Optional[Any]		     =hidden_dropout_prob
								lowerCamelCase__		:		Any		     =attention_probs_dropout_prob
								lowerCamelCase__		:		Tuple		     =type_sequence_label_size
								lowerCamelCase__		:		int		     =initializer_range
								lowerCamelCase__		:		Tuple		     =scope

								# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
								lowerCamelCase__		:		Tuple		     =(image_size // patch_size) ** 2
								lowerCamelCase__		:		Any		     =num_patches + 1



					def 		snake_case	(	self							:  Any					)-> Optional[int]:
								lowerCamelCase__		:		List[str]		     =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

								lowerCamelCase__		:		int		     =None
								if self.use_labels:
											lowerCamelCase__		:		str		     =ids_tensor([self.batch_size],  self.type_sequence_label_size					)

								lowerCamelCase__		:		List[str]		     =self.get_config()

								return config, pixel_values, labels



					def 		snake_case	(	self							:  int					)-> int:
								return ViTMSNConfig(
								    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,  initializer_range=self.initializer_range,  )



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  Optional[Any],  lowerCamelCase							:  Optional[int],  lowerCamelCase							:  Optional[Any]					)-> List[str]:
								lowerCamelCase__		:		Any		     =ViTMSNModel(config=lowerCamelCase					)
								model.to(lowerCamelCase					)
								model.eval()
								lowerCamelCase__		:		Dict		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)					)



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  List[str],  lowerCamelCase							:  Any,  lowerCamelCase							:  str					)-> List[str]:
								lowerCamelCase__		:		Dict		     =self.type_sequence_label_size
								lowerCamelCase__		:		List[str]		     =ViTMSNForImageClassification(lowerCamelCase					)
								model.to(lowerCamelCase					)
								model.eval()
								lowerCamelCase__		:		Tuple		     =model(lowerCamelCase,  labels=lowerCamelCase					)
								print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''					)
								print('''Labels: {labels}'''					)
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)					)

								# test greyscale images
								lowerCamelCase__		:		Optional[Any]		     =1
								lowerCamelCase__		:		str		     =ViTMSNForImageClassification(lowerCamelCase					)
								model.to(lowerCamelCase					)
								model.eval()

								lowerCamelCase__		:		str		     =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
								lowerCamelCase__		:		str		     =model(lowerCamelCase					)
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.type_sequence_label_size)					)




					def 		snake_case	(	self							:  Union[str, Any]					)-> Tuple:
								lowerCamelCase__		:		Optional[int]		     =self.prepare_config_and_inputs()
								lowerCamelCase__     ,       lowerCamelCase__     ,       lowerCamelCase__		:		List[str]		     =config_and_inputs
								lowerCamelCase__		:		Optional[int]		     ={'''pixel_values''': pixel_values}
								return config, inputs_dict





@require_torch
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
					_a             =							(
					    {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
					    if is_torch_available()
					    else {}
					)

					_a             =							False
					_a             =							False
					_a             =							False
					_a             =							False



					def 		snake_case	(	self							:  Union[str, Any]					)-> List[Any]:
								lowerCamelCase__		:		Any		     =ViTMSNModelTester(self					)
								lowerCamelCase__		:		Optional[Any]		     =ConfigTester(self,  config_class=lowerCamelCase,  has_text_modality=lowerCamelCase,  hidden_size=37					)



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



					@unittest.skip(reason='''ViTMSN does not use inputs_embeds'''					)
					def 		snake_case	(	self							:  int					)-> List[Any]:
								pass



					def 		snake_case	(	self							:  List[str]					)-> List[Any]:
								lowerCamelCase__     ,       lowerCamelCase__		:		Optional[Any]		     =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__		:		List[Any]		     =model_class(lowerCamelCase					)
											self.assertIsInstance(model.get_input_embeddings(),  (nn.Module)					)
											lowerCamelCase__		:		List[str]		     =model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowerCamelCase,  nn.Linear					)					)



					def 		snake_case	(	self							:  Any					)-> List[str]:
								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											lowerCamelCase__		:		Optional[int]		     =model_class(lowerCamelCase					)
											lowerCamelCase__		:		str		     =inspect.signature(model.forward					)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowerCamelCase__		:		int		     =[*signature.parameters.keys()]

											lowerCamelCase__		:		Union[str, Any]		     =['''pixel_values''']
											self.assertListEqual(arg_names[:1],  lowerCamelCase					)



					def 		snake_case	(	self							:  Tuple					)-> Union[str, Any]:
								lowerCamelCase__		:		Dict		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase					)



					def 		snake_case	(	self							:  int					)-> int:
								lowerCamelCase__		:		Dict		     =self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*lowerCamelCase					)




					@slow
					def 		snake_case	(	self							:  int					)-> Union[str, Any]:
								for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowerCamelCase__		:		List[str]		     =ViTMSNModel.from_pretrained(lowerCamelCase					)
											self.assertIsNotNone(lowerCamelCase					)






def 					snake_case__       (		):
			"""simple docstring"""
			lowerCamelCase__		:		Union[str, Any]		     =Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
			return image

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


					'''simple docstring'''





					@cached_property
					def 		snake_case	(	self							:  List[str]					)-> List[Any]:
								return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''					) if is_vision_available() else None




					@slow
					def 		snake_case	(	self							:  Optional[int]					)-> List[str]:
								torch.manual_seed(2					)
								lowerCamelCase__		:		Union[str, Any]		     =ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''					).to(lowerCamelCase					)

								lowerCamelCase__		:		Any		     =self.default_image_processor
								lowerCamelCase__		:		List[Any]		     =prepare_img()
								lowerCamelCase__		:		List[str]		     =image_processor(images=lowerCamelCase,  return_tensors='''pt'''					).to(lowerCamelCase					)

								# forward pass
								with torch.no_grad():
											lowerCamelCase__		:		Any		     =model(**lowerCamelCase					)

								# verify the logits
								lowerCamelCase__		:		List[Any]		     =torch.Size((1, 1000)					)
								self.assertEqual(outputs.logits.shape,  lowerCamelCase					)

								lowerCamelCase__		:		int		     =torch.tensor([-0.0_803, -0.4_454, -0.2_375]					).to(lowerCamelCase					)

								self.assertTrue(torch.allclose(outputs.logits[0, :3],  lowerCamelCase,  atol=1E-4					)					)









"""simple docstring"""






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


_lowercase			:			Dict										=						logging.get_logger(__name__)

_lowercase			:			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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'blenderbot-small'
					_a             =							['past_key_values']
					_a             =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



					def __init__(	self							:  Tuple,  lowerCamelCase							:  Any=5_0265,  lowerCamelCase							:  Optional[Any]=512,  lowerCamelCase							:  Union[str, Any]=8,  lowerCamelCase							:  Dict=2048,  lowerCamelCase							:  str=16,  lowerCamelCase							:  List[Any]=8,  lowerCamelCase							:  List[str]=2048,  lowerCamelCase							:  int=16,  lowerCamelCase							:  Any=0.0,  lowerCamelCase							:  Dict=0.0,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  Optional[int]="gelu",  lowerCamelCase							:  Tuple=512,  lowerCamelCase							:  Tuple=0.1,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  List[str]=0.02,  lowerCamelCase							:  Any=1,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Any=0,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  Tuple=2,  lowerCamelCase							:  Dict=2,  **lowerCamelCase							:  Any,  )-> Dict:
								lowerCamelCase__		:		Dict		     =vocab_size
								lowerCamelCase__		:		Dict		     =max_position_embeddings
								lowerCamelCase__		:		Optional[Any]		     =d_model
								lowerCamelCase__		:		Union[str, Any]		     =encoder_ffn_dim
								lowerCamelCase__		:		Optional[Any]		     =encoder_layers
								lowerCamelCase__		:		Any		     =encoder_attention_heads
								lowerCamelCase__		:		Union[str, Any]		     =decoder_ffn_dim
								lowerCamelCase__		:		Optional[int]		     =decoder_layers
								lowerCamelCase__		:		Any		     =decoder_attention_heads
								lowerCamelCase__		:		Optional[int]		     =dropout
								lowerCamelCase__		:		str		     =attention_dropout
								lowerCamelCase__		:		Union[str, Any]		     =activation_dropout
								lowerCamelCase__		:		Tuple		     =activation_function
								lowerCamelCase__		:		str		     =init_std
								lowerCamelCase__		:		List[Any]		     =encoder_layerdrop
								lowerCamelCase__		:		List[str]		     =decoder_layerdrop
								lowerCamelCase__		:		Tuple		     =use_cache
								lowerCamelCase__		:		Optional[Any]		     =encoder_layers
								lowerCamelCase__		:		List[Any]		     =scale_embedding  # scale factor will be sqrt(d_model) if True

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





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					@property
					def 		snake_case	(	self							:  Optional[int]					)-> Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		int		     =OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]					)

											if self.use_past:
														lowerCamelCase__		:		List[str]		     ={0: '''batch'''}
														lowerCamelCase__		:		Tuple		     ={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
											else:
														lowerCamelCase__		:		Union[str, Any]		     ={0: '''batch''', 1: '''decoder_sequence'''}
														lowerCamelCase__		:		Tuple		     ={0: '''batch''', 1: '''decoder_sequence'''}

											if self.use_past:
														self.fill_with_past_key_values_(lowerCamelCase,  direction='''inputs'''					)
								elif self.task == "causal-lm":
											# TODO: figure this case out.
											lowerCamelCase__		:		Optional[Any]		     =OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]					)
											if self.use_past:
														lowerCamelCase__     ,       lowerCamelCase__		:		List[str]		     =self.num_layers
														for i in range(lowerCamelCase					):
																	lowerCamelCase__		:		Optional[Any]		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
																	lowerCamelCase__		:		Optional[int]		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
											lowerCamelCase__		:		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 		snake_case	(	self							:  Dict					)-> Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		Dict		     =super().outputs
								else:
											lowerCamelCase__		:		Optional[Any]		     =super(lowerCamelCase,  self					).outputs
											if self.use_past:
														lowerCamelCase__     ,       lowerCamelCase__		:		str		     =self.num_layers
														for i in range(lowerCamelCase					):
																	lowerCamelCase__		:		Tuple		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
																	lowerCamelCase__		:		Dict		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								lowerCamelCase__		:		Any		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

								# Generate decoder inputs
								lowerCamelCase__		:		str		     =seq_length if not self.use_past else 1
								lowerCamelCase__		:		Tuple		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								lowerCamelCase__		:		str		     ={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								lowerCamelCase__		:		Tuple		     =dict(**lowerCamelCase,  **lowerCamelCase					)

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

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

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

											for _ in range(lowerCamelCase					):
														common_inputs["past_key_values"].append(
														    (
														        torch.zeros(lowerCamelCase					),
														        torch.zeros(lowerCamelCase					),
														        torch.zeros(lowerCamelCase					),
														        torch.zeros(lowerCamelCase					),
														    )					)
											# TODO: test this.
											lowerCamelCase__		:		Union[str, Any]		     =encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
											for _ in range(lowerCamelCase,  lowerCamelCase					):
														common_inputs["past_key_values"].append((torch.zeros(lowerCamelCase					), torch.zeros(lowerCamelCase					))					)
								return common_inputs



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								lowerCamelCase__		:		int		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

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

											lowerCamelCase__		:		str		     =common_inputs['''attention_mask'''].dtype
											lowerCamelCase__		:		int		     =torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(lowerCamelCase,  lowerCamelCase,  dtype=lowerCamelCase					)],  dim=1					)
											lowerCamelCase__		:		str		     =[
											    (torch.zeros(lowerCamelCase					), torch.zeros(lowerCamelCase					)) for _ in range(lowerCamelCase					)
											]
								return common_inputs



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								# Copied from OnnxConfig.generate_dummy_inputs
								# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
								# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
								lowerCamelCase__		:		int		     =compute_effective_axis_dimension(
								    lowerCamelCase,  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
								lowerCamelCase__		:		Optional[int]		     =tokenizer.num_special_tokens_to_add(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =compute_effective_axis_dimension(
								    lowerCamelCase,  fixed_dimension=OnnxConfig.default_fixed_sequence,  num_token_to_add=lowerCamelCase					)

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



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		Union[str, Any]		     =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase					)

								elif self.task == "causal-lm":
											lowerCamelCase__		:		Union[str, Any]		     =self._generate_dummy_inputs_for_causal_lm(
											    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[Any]		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase					)

								return common_inputs




					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any					)-> str:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		Any		     =super()._flatten_past_key_values_(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[str]		     =super(lowerCamelCase,  self					)._flatten_past_key_values_(
											    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)




