
"""simple docstring"""






import numpy

# List of input, output pairs
lowerCamelCase__ :       List[Any]						     =			(
    ((5, 2, 3), 15),
    ((6, 5, 9), 25),
    ((11, 12, 13), 41),
    ((1, 1, 1), 8),
    ((11, 12, 13), 41),
)
lowerCamelCase__ :       Optional[int]						     =			(((515, 22, 13), 555), ((61, 35, 49), 150))
lowerCamelCase__ :       int						     =			[2, 4, 1, 5]
lowerCamelCase__ :       int						     =			len(train_data)
lowerCamelCase__ :       Dict						     =			0.0_0_9





def __A						(   a_ :							int		,		a_ :							str="train"     )->							Optional[int]:




		'''simple docstring'''






		return calculate_hypothesis_value(__lowerCAmelCase		,		__lowerCAmelCase     ) - output(
		    __lowerCAmelCase		,		__lowerCAmelCase     )





def __A						(   a_ :							Any     )->							Union[str, Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= 0
		for i in range(len(__lowerCAmelCase     ) - 1     ):
				hyp_val += data_input_tuple[i] * parameter_vector[i + 1]
		hyp_val += parameter_vector[0]
		return hyp_val





def __A						(   a_ :							Optional[Any]		,		a_ :							Tuple     )->							Optional[int]:




		'''simple docstring'''






		if data_set == "train":
				return train_data[example_no][1]
		elif data_set == "test":
				return test_data[example_no][1]
		return None





def __A						(   a_ :							int		,		a_ :							Dict     )->							Optional[int]:




		'''simple docstring'''






		if data_set == "train":
				return _hypothesis_value(train_data[example_no][0]     )
		elif data_set == "test":
				return _hypothesis_value(test_data[example_no][0]     )
		return None





def __A						(   a_ :							List[Any]		,		a_ :							Optional[Any]=m     )->							Union[str, Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= 0
		for i in range(__lowerCAmelCase     ):
				if index == -1:
						summation_value += _error(__lowerCAmelCase     )
				else:
						summation_value += _error(__lowerCAmelCase     ) * train_data[i][0][index]
		return summation_value





def __A						(   a_ :							List[Any]     )->							Union[str, Any]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= summation_of_cost_derivative(__lowerCAmelCase		,		__lowerCAmelCase     ) / m
		return cost_derivative_value





def __A						(   )->							Optional[int]:




		'''simple docstring'''






		global parameter_vector
		# Tune these values to set a tolerance value for predicted output
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0.00_0002
		SCREAMING_SNAKE_CASE       :     Any				= 0
		SCREAMING_SNAKE_CASE       :     List[str]				= 0
		while True:
				j += 1
				SCREAMING_SNAKE_CASE       :     Tuple				= [0, 0, 0, 0]
				for i in range(0		,		len(__lowerCAmelCase     )     ):
						SCREAMING_SNAKE_CASE       :     str				= get_cost_derivative(i - 1     )
						SCREAMING_SNAKE_CASE       :     Optional[Any]				= (
						    parameter_vector[i] - LEARNING_RATE * cost_derivative
						)
				if numpy.allclose(
				    __lowerCAmelCase		,		__lowerCAmelCase		,		atol=__lowerCAmelCase		,		rtol=__lowerCAmelCase		,		):
						break
				SCREAMING_SNAKE_CASE       :     Tuple				= temp_parameter_vector
		print(('''Number of iterations:''', j)     )





def __A						(   )->							Tuple:




		'''simple docstring'''






		for i in range(len(__lowerCAmelCase     )     ):
				print(('''Actual output value:''', output(__lowerCAmelCase		,		'''test'''     ))     )
				print(('''Hypothesis output:''', calculate_hypothesis_value(__lowerCAmelCase		,		'''test'''     ))     )


if __name__ == "__main__":
					run_gradient_descent()
					print("\nTesting gradient descent for a linear hypothesis function.\n")
					test_gradient_descent()







"""simple docstring"""






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import (
					    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
					    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
					    LayoutLMvaConfig,
					    LayoutLMvaForQuestionAnswering,
					    LayoutLMvaForSequenceClassification,
					    LayoutLMvaForTokenClassification,
					    LayoutLMvaModel,
					)
					from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor




class 				lowercase__:
	'''simple docstring'''

	def __init__(						self    :Tuple		, lowerCamelCase_    :Tuple		, lowerCamelCase_    :Union[str, Any]=2		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :str=7		, lowerCamelCase_    :Tuple=True		, lowerCamelCase_    :Dict=True		, lowerCamelCase_    :int=True		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :Optional[Any]=99		, lowerCamelCase_    :Any=36		, lowerCamelCase_    :Any=3		, lowerCamelCase_    :str=4		, lowerCamelCase_    :Tuple=37		, lowerCamelCase_    :Optional[int]="gelu"		, lowerCamelCase_    :str=0.1		, lowerCamelCase_    :Optional[int]=0.1		, lowerCamelCase_    :Tuple=5_12		, lowerCamelCase_    :Optional[Any]=16		, lowerCamelCase_    :List[str]=2		, lowerCamelCase_    :Optional[int]=0.0_2		, lowerCamelCase_    :int=6		, lowerCamelCase_    :str=6		, lowerCamelCase_    :Optional[Any]=3		, lowerCamelCase_    :Union[str, Any]=4		, lowerCamelCase_    :List[Any]=None		, lowerCamelCase_    :Tuple=10_00		, )							->			Union[str, Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= parent
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= batch_size
			SCREAMING_SNAKE_CASE       :     List[str]				= num_channels
			SCREAMING_SNAKE_CASE       :     str				= image_size
			SCREAMING_SNAKE_CASE       :     Optional[int]				= patch_size
			SCREAMING_SNAKE_CASE       :     Tuple				= text_seq_length
			SCREAMING_SNAKE_CASE       :     Optional[int]				= is_training
			SCREAMING_SNAKE_CASE       :     Dict				= use_input_mask
			SCREAMING_SNAKE_CASE       :     Any				= use_token_type_ids
			SCREAMING_SNAKE_CASE       :     List[Any]				= use_labels
			SCREAMING_SNAKE_CASE       :     List[Any]				= vocab_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_size
			SCREAMING_SNAKE_CASE       :     Tuple				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[int]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     List[str]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Any				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     int				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= type_sequence_label_size
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= coordinate_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= shape_size
			SCREAMING_SNAKE_CASE       :     Dict				= num_labels
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= num_choices
			SCREAMING_SNAKE_CASE       :     List[str]				= scope
			SCREAMING_SNAKE_CASE       :     Optional[int]				= range_bbox

			# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
			SCREAMING_SNAKE_CASE       :     str				= text_seq_length
			SCREAMING_SNAKE_CASE       :     int				= (image_size // patch_size) ** 2 + 1
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.text_seq_length + self.image_seq_length





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= ids_tensor([self.batch_size, self.text_seq_length]		, self.vocab_size					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= ids_tensor([self.batch_size, self.text_seq_length, 4]		, self.range_bbox					)
			# Ensure that bbox is legal
			for i in range(bbox.shape[0]					):
					for j in range(bbox.shape[1]					):
							if bbox[i, j, 3] < bbox[i, j, 1]:
									SCREAMING_SNAKE_CASE       :     str				= bbox[i, j, 3]
									SCREAMING_SNAKE_CASE       :     List[str]				= bbox[i, j, 1]
									SCREAMING_SNAKE_CASE       :     Any				= t
							if bbox[i, j, 2] < bbox[i, j, 0]:
									SCREAMING_SNAKE_CASE       :     Any				= bbox[i, j, 2]
									SCREAMING_SNAKE_CASE       :     Any				= bbox[i, j, 0]
									SCREAMING_SNAKE_CASE       :     Optional[Any]				= t

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

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= None
			if self.use_input_mask:
					SCREAMING_SNAKE_CASE       :     Dict				= random_attention_mask([self.batch_size, self.text_seq_length]					)

			SCREAMING_SNAKE_CASE       :     Any				= None
			if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE       :     Tuple				= ids_tensor([self.batch_size, self.text_seq_length]		, self.type_vocab_size					)

			SCREAMING_SNAKE_CASE       :     Tuple				= None
			SCREAMING_SNAKE_CASE       :     Optional[int]				= None
			if self.use_labels:
					SCREAMING_SNAKE_CASE       :     List[str]				= ids_tensor([self.batch_size]		, self.type_sequence_label_size					)
					SCREAMING_SNAKE_CASE       :     Dict				= ids_tensor([self.batch_size, self.text_seq_length]		, self.num_labels					)

			SCREAMING_SNAKE_CASE       :     List[str]				= LayoutLMvaConfig(
			    vocab_size=self.vocab_size		, 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		, max_position_embeddings=self.max_position_embeddings		, type_vocab_size=self.type_vocab_size		, initializer_range=self.initializer_range		, coordinate_size=self.coordinate_size		, shape_size=self.shape_size		, input_size=self.image_size		, patch_size=self.patch_size		, )

			return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Dict				= LayoutLMvaModel(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()

			# text + image
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, pixel_values=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[str]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, token_type_ids=lowerCamelCase_					)
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_					)

			self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, self.seq_length, self.hidden_size)					)

			# text only
			SCREAMING_SNAKE_CASE       :     List[str]				= model(lowerCamelCase_					)
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.text_seq_length, self.hidden_size)					)

			# image only
			SCREAMING_SNAKE_CASE       :     List[str]				= model(pixel_values=lowerCamelCase_					)
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.image_seq_length, self.hidden_size)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.num_labels
			SCREAMING_SNAKE_CASE       :     Dict				= LayoutLMvaForSequenceClassification(lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     int				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.num_labels
			SCREAMING_SNAKE_CASE       :     int				= LayoutLMvaForTokenClassification(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, labels=lowerCamelCase_		, )
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.text_seq_length, self.num_labels)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= LayoutLMvaForQuestionAnswering(config=lowerCamelCase_					)
			model.to(lowerCamelCase_					)
			model.eval()
			SCREAMING_SNAKE_CASE       :     List[Any]				= model(
			    lowerCamelCase_		, bbox=lowerCamelCase_		, pixel_values=lowerCamelCase_		, attention_mask=lowerCamelCase_		, token_type_ids=lowerCamelCase_		, start_positions=lowerCamelCase_		, end_positions=lowerCamelCase_		, )
			self.parent.assertEqual(result.start_logits.shape		, (self.batch_size, self.seq_length)					)
			self.parent.assertEqual(result.end_logits.shape		, (self.batch_size, self.seq_length)					)







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= self.prepare_config_and_inputs()
			(
			    (
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					(
			    SCREAMING_SNAKE_CASE
			),					
			)       :     Tuple				= config_and_inputs
			SCREAMING_SNAKE_CASE       :     Dict				= {
			    '''input_ids''': input_ids,
			    '''bbox''': bbox,
			    '''pixel_values''': pixel_values,
			    '''token_type_ids''': token_type_ids,
			    '''attention_mask''': input_mask,
			}
			return config, inputs_dict





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

	UpperCamelCase				=  False
	UpperCamelCase				=  False
	UpperCamelCase				=  False

	UpperCamelCase				=  (
	    (
	        LayoutLMvaModel,
	        LayoutLMvaForSequenceClassification,
	        LayoutLMvaForTokenClassification,
	        LayoutLMvaForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	UpperCamelCase				=  (
	    {"""document-question-answering""": LayoutLMvaForQuestionAnswering, """feature-extraction""": LayoutLMvaModel}
	    if is_torch_available()
	    else {}
	)





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







			'''simple docstring'''




			return True





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= LayoutLMvaModelTester(self					)
			SCREAMING_SNAKE_CASE       :     List[Any]				= ConfigTester(self		, config_class=lowerCamelCase_		, hidden_size=37					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= copy.deepcopy(lowerCamelCase_					)
			if model_class in get_values(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= {
					    k: v.unsqueeze(1					).expand(-1		, self.model_tester.num_choices		, -1					).contiguous()
					    if isinstance(lowerCamelCase_		, torch.Tensor					) and v.ndim > 1
					    else v
					    for k, v in inputs_dict.items()
					}
			if return_labels:
					if model_class in get_values(lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.ones(self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in get_values(lowerCamelCase_					):
							SCREAMING_SNAKE_CASE       :     List[Any]				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Any				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in [
					    *get_values(lowerCamelCase_					),
					]:
							SCREAMING_SNAKE_CASE       :     str				= torch.zeros(
							    self.model_tester.batch_size		, dtype=torch.long		, device=lowerCamelCase_					)
					elif model_class in [
					    *get_values(lowerCamelCase_					),
					]:
							SCREAMING_SNAKE_CASE       :     Dict				= torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.text_seq_length)		, dtype=torch.long		, device=lowerCamelCase_		, )

			return inputs_dict





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







			'''simple docstring'''




			self.config_tester.run_common_tests()





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







			'''simple docstring'''




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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE       :     str				= type
					self.model_tester.create_and_check_model(*lowerCamelCase_					)





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







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :str					)							->			Any:







			'''simple docstring'''




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





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     int				= self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_					)







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







			'''simple docstring'''




			for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= LayoutLMvaModel.from_pretrained(lowerCamelCase_					)
					self.assertIsNotNone(lowerCamelCase_					)





def __A						(   )->							Optional[int]:




		'''simple docstring'''






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




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

	@cached_property
	def   __lowerCAmelCase (						self    :str					)							->			int:







			'''simple docstring'''




			return LayoutLMvaImageProcessor(apply_ocr=lowerCamelCase_					) if is_vision_available() else None







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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''					).to(lowerCamelCase_					)

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

			SCREAMING_SNAKE_CASE       :     Optional[Any]				= torch.tensor([[1, 2]]					)
			SCREAMING_SNAKE_CASE       :     Dict				= torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]					).unsqueeze(0					)

			# forward pass
			SCREAMING_SNAKE_CASE       :     Tuple				= model(
			    input_ids=input_ids.to(lowerCamelCase_					)		, bbox=bbox.to(lowerCamelCase_					)		, pixel_values=pixel_values.to(lowerCamelCase_					)		, )

			# verify the logits
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= torch.Size((1, 1_99, 7_68)					)
			self.assertEqual(outputs.last_hidden_state.shape		, lowerCamelCase_					)

			SCREAMING_SNAKE_CASE       :     List[Any]				= torch.tensor(
			    [[-0.0_5_2_9, 0.3_6_1_8, 0.1_6_3_2], [-0.1_5_8_7, -0.1_6_6_7, -0.0_4_0_0], [-0.1_5_5_7, -0.1_6_7_1, -0.0_5_0_5]]					).to(lowerCamelCase_					)

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





