
"""simple docstring"""






import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    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 (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Dict		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Tuple=13		, lowerCamelCase_    :List[str]=30		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Optional[int]=3		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :Optional[int]=True		, lowerCamelCase_    :Tuple=32		, lowerCamelCase_    :List[Any]=5		, lowerCamelCase_    :Any=4		, lowerCamelCase_    :Tuple=37		, lowerCamelCase_    :List[str]="gelu"		, lowerCamelCase_    :Dict=0.1		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Optional[Any]=10		, lowerCamelCase_    :Union[str, Any]=0.0_2		, lowerCamelCase_    :Tuple=3		, lowerCamelCase_    :str=None		, lowerCamelCase_    :Any=2		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= parent
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= image_size
			SCREAMING_SNAKE_CASE       :     int				= patch_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_channels
			SCREAMING_SNAKE_CASE       :     Tuple				= is_training
			SCREAMING_SNAKE_CASE       :     str				= use_labels
			SCREAMING_SNAKE_CASE       :     List[str]				= hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Any				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Tuple				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     List[Any]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     str				= type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= initializer_range
			SCREAMING_SNAKE_CASE       :     List[Any]				= scope
			SCREAMING_SNAKE_CASE       :     Any				= encoder_stride

			# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
			SCREAMING_SNAKE_CASE       :     int				= (image_size // patch_size) ** 2
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= num_patches + 2





	def   __lowerCAmelCase (						self    :str					)							->			Dict:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     List[Any]				= None
			if self.use_labels:
					SCREAMING_SNAKE_CASE       :     List[str]				= ids_tensor([self.batch_size]		, self.type_sequence_label_size					)

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self.get_config()

			return config, pixel_values, labels





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




			return DeiTConfig(
			    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=lowerCamelCase_		, initializer_range=self.initializer_range		, encoder_stride=self.encoder_stride		, )





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :Dict		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :int					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= DeiTModel(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_					)
			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)





	def   __lowerCAmelCase (						self    :Tuple		, lowerCamelCase_    :Dict		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= DeiTForMaskedImageModeling(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model(lowerCamelCase_					)
			self.parent.assertEqual(
			    result.reconstruction.shape		, (self.batch_size, self.num_channels, self.image_size, self.image_size)					)

			# test greyscale images
			SCREAMING_SNAKE_CASE       :     str				= 1
			SCREAMING_SNAKE_CASE       :     List[Any]				= DeiTForMaskedImageModeling(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			SCREAMING_SNAKE_CASE       :     str				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_					)
			self.parent.assertEqual(result.reconstruction.shape		, (self.batch_size, 1, self.image_size, self.image_size)					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :List[Any]		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :str					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= DeiTForImageClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(lowerCamelCase_		, labels=lowerCamelCase_					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)					)

			# test greyscale images
			SCREAMING_SNAKE_CASE       :     List[Any]				= 1
			SCREAMING_SNAKE_CASE       :     Tuple				= DeiTForImageClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			SCREAMING_SNAKE_CASE       :     Tuple				= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, labels=lowerCamelCase_					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)					)







	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.prepare_config_and_inputs()
			(
			    (
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					
			)       :     List[str]				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     Any				= {'''pixel_values''': pixel_values}
			return config, inputs_dict





@require_torch
class 				lowercase__(				_UpperCAmelCase		,						_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  (
	    (
	        DeiTModel,
	        DeiTForImageClassification,
	        DeiTForImageClassificationWithTeacher,
	        DeiTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (
	    {
	        """feature-extraction""": DeiTModel,
	        """image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False





	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= DeiTModelTester(self					)
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= ConfigTester(self		, config_class=lowerCamelCase_		, has_text_modality=lowerCamelCase_		, hidden_size=37					)





	def   __lowerCAmelCase (						self    :Dict					)							->			Optional[Any]:







			'''simple docstring'''




			self.config_tester.run_common_tests()





	@unittest.skip(reason='''DeiT does not use inputs_embeds'''					)
	def   __lowerCAmelCase (						self    :List[Any]					)							->			List[str]:







			'''simple docstring'''




			pass





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Dict				= model_class(lowerCamelCase_					)
					self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)					)
					SCREAMING_SNAKE_CASE       :     Dict				= model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(lowerCamelCase_		, nn.Linear					)					)





	def   __lowerCAmelCase (						self    :Tuple					)							->			str:







			'''simple docstring'''




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

			for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE       :     Tuple				= model_class(lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     str				= inspect.signature(model.forward					)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					SCREAMING_SNAKE_CASE       :     List[Any]				= [*signature.parameters.keys()]

					SCREAMING_SNAKE_CASE       :     Tuple				= ['''pixel_values''']
					self.assertListEqual(arg_names[:1]		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any					)							->			Optional[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Tuple					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Optional[Any]		, lowerCamelCase_    :Optional[int]=False					)							->			int:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= super()._prepare_for_class(lowerCamelCase_		, lowerCamelCase_		, return_labels=lowerCamelCase_					)

			if return_labels:
					if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
							del inputs_dict["labels"]

			return inputs_dict





	def   __lowerCAmelCase (						self    :List[Any]					)							->			Dict:







			'''simple docstring'''




			if not self.model_tester.is_training:
					return

			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Any				= self.model_tester.prepare_config_and_inputs_for_common()
			SCREAMING_SNAKE_CASE       :     str				= True

			for model_class in self.all_model_classes:
					# DeiTForImageClassificationWithTeacher supports inference-only
					if (
					    model_class in get_values(lowerCamelCase_					)
					    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
					):
							continue
					SCREAMING_SNAKE_CASE       :     int				= model_class(lowerCamelCase_					)
					model.to(lowerCamelCase_					)
					model.train()
					SCREAMING_SNAKE_CASE       :     Tuple				= self._prepare_for_class(lowerCamelCase_		, lowerCamelCase_		, return_labels=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Any				= model(**lowerCamelCase_					).loss
					loss.backward()





	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[str]				= self.model_tester.prepare_config_and_inputs_for_common()
			if not self.model_tester.is_training:
					return

			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= False
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= True

			for model_class in self.all_model_classes:
					if model_class in get_values(lowerCamelCase_					) or not model_class.supports_gradient_checkpointing:
							continue
					# DeiTForImageClassificationWithTeacher supports inference-only
					if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
							continue
					SCREAMING_SNAKE_CASE       :     Dict				= model_class(lowerCamelCase_					)
					model.gradient_checkpointing_enable()
					model.to(lowerCamelCase_					)
					model.train()
					SCREAMING_SNAKE_CASE       :     Optional[int]				= self._prepare_for_class(lowerCamelCase_		, lowerCamelCase_		, return_labels=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Tuple				= model(**lowerCamelCase_					).loss
					loss.backward()





	def   __lowerCAmelCase (						self    :Tuple					)							->			List[str]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Tuple				= [
			    {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
			    {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
			    {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
			]

			for model_class in self.all_model_classes:
					if (
					    model_class
					    not in [
					        *get_values(lowerCamelCase_					),
					        *get_values(lowerCamelCase_					),
					    ]
					    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
					):
							continue

					for problem_type in problem_types:
							with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"					):
									SCREAMING_SNAKE_CASE       :     str				= problem_type['''title''']
									SCREAMING_SNAKE_CASE       :     Dict				= problem_type['''num_labels''']

									SCREAMING_SNAKE_CASE       :     List[Any]				= model_class(lowerCamelCase_					)
									model.to(lowerCamelCase_					)
									model.train()

									SCREAMING_SNAKE_CASE       :     str				= self._prepare_for_class(lowerCamelCase_		, lowerCamelCase_		, return_labels=lowerCamelCase_					)

									if problem_type["num_labels"] > 1:
											SCREAMING_SNAKE_CASE       :     Any				= inputs['''labels'''].unsqueeze(1					).repeat(1		, problem_type['''num_labels''']					)

									SCREAMING_SNAKE_CASE       :     Union[str, Any]				= inputs['''labels'''].to(problem_type['''dtype''']					)

									# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
									# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
									# they have the same size." which is a symptom something in wrong for the regression problem.
									# See https://github.com/huggingface/transformers/issues/11780
									with warnings.catch_warnings(record=lowerCamelCase_					) as warning_list:
											SCREAMING_SNAKE_CASE       :     List[str]				= model(**lowerCamelCase_					).loss
									for w in warning_list:
											if "Using a target size that is different to the input size" in str(w.message					):
													raise ValueError(
													    f"Something is going wrong in the regression problem: intercepted {w.message}"					)

									loss.backward()







	@slow
	def   __lowerCAmelCase (						self    :Optional[int]					)							->			Tuple:







			'''simple docstring'''




			for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE       :     List[str]				= DeiTModel.from_pretrained(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)





def __A						(   )->							List[Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
		return image




@require_torch
@require_vision
class 				lowercase__(				unittest.TestCase   ):
	'''simple docstring'''

	@cached_property
	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''simple docstring'''




			return (
			    DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''					)
			    if is_vision_available()
			    else None
			)





	@slow
	def   __lowerCAmelCase (						self    :int					)							->			List[str]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Tuple				= DeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''					).to(
			    lowerCamelCase_					)

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

			# forward pass
			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     Tuple				= model(**lowerCamelCase_					)

			# verify the logits
			SCREAMING_SNAKE_CASE       :     str				= torch.Size((1, 10_00)					)
			self.assertEqual(outputs.logits.shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     Tuple				= torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]					).to(lowerCamelCase_					)

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







	@slow
	@require_accelerate
	@require_torch_gpu
	def   __lowerCAmelCase (						self    :List[str]					)							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= DeiTModel.from_pretrained(
			    '''facebook/deit-base-distilled-patch16-224'''		, torch_dtype=torch.floataa		, device_map='''auto'''					)
			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.default_image_processor

			SCREAMING_SNAKE_CASE       :     List[str]				= prepare_img()
			SCREAMING_SNAKE_CASE       :     List[Any]				= image_processor(images=lowerCamelCase_		, return_tensors='''pt'''					)
			SCREAMING_SNAKE_CASE       :     Tuple				= inputs.pixel_values.to(lowerCamelCase_					)

			# forward pass to make sure inference works in fp16
			with torch.no_grad():
					SCREAMING_SNAKE_CASE       :     List[Any]				= model(lowerCamelCase_					)







"""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


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """blenderbot-small"""
	UpperCamelCase				=  ["""past_key_values"""]
	UpperCamelCase				=  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





	def __init__(						self    :Any		, lowerCamelCase_    :Dict=5_02_65		, lowerCamelCase_    :str=5_12		, lowerCamelCase_    :Tuple=8		, lowerCamelCase_    :int=20_48		, lowerCamelCase_    :str=16		, lowerCamelCase_    :Optional[int]=8		, lowerCamelCase_    :str=20_48		, lowerCamelCase_    :Optional[Any]=16		, lowerCamelCase_    :Union[str, Any]=0.0		, lowerCamelCase_    :List[str]=0.0		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :int="gelu"		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :int=0.0		, lowerCamelCase_    :Tuple=0.0		, lowerCamelCase_    :Optional[int]=0.0_2		, lowerCamelCase_    :Union[str, Any]=1		, lowerCamelCase_    :Dict=False		, lowerCamelCase_    :Optional[int]=0		, lowerCamelCase_    :List[Any]=1		, lowerCamelCase_    :Any=2		, lowerCamelCase_    :Optional[Any]=2		, **lowerCamelCase_    :Dict		, )							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= d_model
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     Tuple				= encoder_layers
			SCREAMING_SNAKE_CASE       :     Dict				= encoder_attention_heads
			SCREAMING_SNAKE_CASE       :     Any				= decoder_ffn_dim
			SCREAMING_SNAKE_CASE       :     str				= decoder_layers
			SCREAMING_SNAKE_CASE       :     str				= decoder_attention_heads
			SCREAMING_SNAKE_CASE       :     List[Any]				= dropout
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_dropout
			SCREAMING_SNAKE_CASE       :     Any				= activation_dropout
			SCREAMING_SNAKE_CASE       :     List[str]				= activation_function
			SCREAMING_SNAKE_CASE       :     Optional[int]				= init_std
			SCREAMING_SNAKE_CASE       :     List[Any]				= encoder_layerdrop
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= decoder_layerdrop
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_cache
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= encoder_layers
			SCREAMING_SNAKE_CASE       :     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 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

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







			'''simple docstring'''




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

					if self.use_past:
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {0: '''batch'''}
							SCREAMING_SNAKE_CASE       :     List[Any]				= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= {0: '''batch''', 1: '''decoder_sequence'''}
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= {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.
					SCREAMING_SNAKE_CASE       :     Tuple				= OrderedDict(
					    [
					        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
					        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
					    ]					)
					if self.use_past:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.num_layers
							for i in range(lowerCamelCase_					):
									SCREAMING_SNAKE_CASE       :     int				= {0: '''batch''', 2: '''past_sequence + sequence'''}
									SCREAMING_SNAKE_CASE       :     List[str]				= {0: '''batch''', 2: '''past_sequence + sequence'''}
			else:
					SCREAMING_SNAKE_CASE       :     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   __lowerCAmelCase (						self    :List[str]					)							->			Mapping[str, Mapping[int, str]]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :int		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
			    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)

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

					SCREAMING_SNAKE_CASE       :     List[Any]				= torch.cat(
					    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_					)]		, dim=1					)

					SCREAMING_SNAKE_CASE       :     Optional[int]				= []
					# If the number of encoder and decoder layers are present in the model configuration, both are considered
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Optional[int]				= self.num_layers
					SCREAMING_SNAKE_CASE       :     int				= min(lowerCamelCase_		, lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= max(lowerCamelCase_		, lowerCamelCase_					) - min_num_layers
					SCREAMING_SNAKE_CASE       :     Tuple				= '''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.
					SCREAMING_SNAKE_CASE       :     int				= 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   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= 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
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Tuple				= common_inputs['''input_ids'''].shape
					# Not using the same length for past_key_values
					SCREAMING_SNAKE_CASE       :     List[str]				= seqlen + 2
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     Dict				= self.num_layers
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= self.num_attention_heads
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= (
					    batch,
					    num_encoder_attention_heads,
					    past_key_values_length,
					    self._config.hidden_size // num_encoder_attention_heads,
					)

					SCREAMING_SNAKE_CASE       :     Tuple				= common_inputs['''attention_mask'''].dtype
					SCREAMING_SNAKE_CASE       :     Any				= torch.cat(
					    [common_inputs['''attention_mask'''], torch.ones(lowerCamelCase_		, lowerCamelCase_		, dtype=lowerCamelCase_					)]		, dim=1					)
					SCREAMING_SNAKE_CASE       :     Optional[int]				= [
					    (torch.zeros(lowerCamelCase_					), torch.zeros(lowerCamelCase_					)) for _ in range(lowerCamelCase_					)
					]
			return common_inputs





	def   __lowerCAmelCase (						self    :Union[str, Any]		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     List[str]				= 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
			SCREAMING_SNAKE_CASE       :     int				= tokenizer.num_special_tokens_to_add(lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     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
			SCREAMING_SNAKE_CASE       :     Tuple				= [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
			SCREAMING_SNAKE_CASE       :     Any				= dict(tokenizer(lowerCamelCase_		, return_tensors=lowerCamelCase_					)					)
			return common_inputs





	def   __lowerCAmelCase (						self    :List[Any]		, lowerCamelCase_    :PreTrainedTokenizer		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :int = -1		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[TensorType] = None		, )							->			Mapping[str, Any]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Dict				= 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":
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= self._generate_dummy_inputs_for_causal_lm(
					    lowerCamelCase_		, batch_size=lowerCamelCase_		, seq_length=lowerCamelCase_		, is_pair=lowerCamelCase_		, framework=lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     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   __lowerCAmelCase (						self    :Dict		, lowerCamelCase_    :int		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :List[str]		, lowerCamelCase_    :Dict					)							->			List[Any]:







			'''simple docstring'''




			if self.task in ["default", "seq2seq-lm"]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= super()._flatten_past_key_values_(lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)
			else:
					SCREAMING_SNAKE_CASE       :     Tuple				= super(lowerCamelCase_		, self					)._flatten_past_key_values_(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_					)





