





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 						_A:


				"""simple docstring"""


				def __init__(			self     ,					_A     ,					_A=2     ,					_A=3     ,					_A=4     ,					_A=2     ,					_A=7     ,					_A=True     ,					_A=True     ,					_A=True     ,					_A=True     ,					_A=99     ,					_A=36     ,					_A=3     ,					_A=4     ,					_A=37     ,					_A="gelu"     ,					_A=0.1     ,					_A=0.1     ,					_A=512     ,					_A=16     ,					_A=2     ,					_A=0.0_2     ,					_A=6     ,					_A=6     ,					_A=3     ,					_A=4     ,					_A=None     ,					_A=1000     ,					):
									__A	:						Tuple       =				parent
									__A	:						Tuple       =				batch_size
									__A	:						Dict       =				num_channels
									__A	:						Tuple       =				image_size
									__A	:						str       =				patch_size
									__A	:						Dict       =				text_seq_length
									__A	:						int       =				is_training
									__A	:						int       =				use_input_mask
									__A	:						Union[str, Any]       =				use_token_type_ids
									__A	:						List[str]       =				use_labels
									__A	:						Union[str, Any]       =				vocab_size
									__A	:						Dict       =				hidden_size
									__A	:						Union[str, Any]       =				num_hidden_layers
									__A	:						Optional[int]       =				num_attention_heads
									__A	:						Any       =				intermediate_size
									__A	:						Optional[Any]       =				hidden_act
									__A	:						Optional[Any]       =				hidden_dropout_prob
									__A	:						Union[str, Any]       =				attention_probs_dropout_prob
									__A	:						int       =				max_position_embeddings
									__A	:						Tuple       =				type_vocab_size
									__A	:						List[str]       =				type_sequence_label_size
									__A	:						List[str]       =				initializer_range
									__A	:						Any       =				coordinate_size
									__A	:						Optional[int]       =				shape_size
									__A	:						Tuple       =				num_labels
									__A	:						Union[str, Any]       =				num_choices
									__A	:						List[str]       =				scope
									__A	:						Union[str, Any]       =				range_bbox

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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Any       =				ids_tensor([self.batch_size, self.text_seq_length]     ,					self.vocab_size     )

									__A	:						int       =				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]:
																								__A	:						Union[str, Any]       =				bbox[i, j, 3]
																								__A	:						Optional[int]       =				bbox[i, j, 1]
																								__A	:						int       =				t
																			if bbox[i, j, 2] < bbox[i, j, 0]:
																								__A	:						Optional[int]       =				bbox[i, j, 2]
																								__A	:						List[Any]       =				bbox[i, j, 0]
																								__A	:						Tuple       =				t

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

									__A	:						Dict       =				None
									if self.use_input_mask:
														__A	:						int       =				random_attention_mask([self.batch_size, self.text_seq_length]     )

									__A	:						Optional[Any]       =				None
									if self.use_token_type_ids:
														__A	:						List[str]       =				ids_tensor([self.batch_size, self.text_seq_length]     ,					self.type_vocab_size     )

									__A	:						Union[str, Any]       =				None
									__A	:						int       =				None
									if self.use_labels:
														__A	:						Union[str, Any]       =				ids_tensor([self.batch_size]     ,					self.type_sequence_label_size     )
														__A	:						Optional[int]       =				ids_tensor([self.batch_size, self.text_seq_length]     ,					self.num_labels     )

									__A	:						Optional[int]       =				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 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[Any]       =				LayoutLMvaModel(config=_A     )
									model.to(_A     )
									model.eval()

									# text + image
									__A	:						Union[str, Any]       =				model(_A     ,					pixel_values=_A     )
									__A	:						Dict       =				model(
									    _A     ,					bbox=_A     ,					pixel_values=_A     ,					attention_mask=_A     ,					token_type_ids=_A     )
									__A	:						Union[str, Any]       =				model(_A     ,					bbox=_A     ,					pixel_values=_A     ,					token_type_ids=_A     )
									__A	:						List[str]       =				model(_A     ,					bbox=_A     ,					pixel_values=_A     )

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

									# text only
									__A	:						Any       =				model(_A     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape     ,					(self.batch_size, self.text_seq_length, self.hidden_size)     )

									# image only
									__A	:						str       =				model(pixel_values=_A     )
									self.parent.assertEqual(
									    result.last_hidden_state.shape     ,					(self.batch_size, self.image_seq_length, self.hidden_size)     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Any       =				self.num_labels
									__A	:						str       =				LayoutLMvaForSequenceClassification(_A     )
									model.to(_A     )
									model.eval()
									__A	:						List[Any]       =				model(
									    _A     ,					bbox=_A     ,					pixel_values=_A     ,					attention_mask=_A     ,					token_type_ids=_A     ,					labels=_A     ,					)
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.num_labels)     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Optional[int]       =				self.num_labels
									__A	:						int       =				LayoutLMvaForTokenClassification(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						Tuple       =				model(
									    _A     ,					bbox=_A     ,					pixel_values=_A     ,					attention_mask=_A     ,					token_type_ids=_A     ,					labels=_A     ,					)
									self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.text_seq_length, self.num_labels)     )



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									__A	:						Union[str, Any]       =				LayoutLMvaForQuestionAnswering(config=_A     )
									model.to(_A     )
									model.eval()
									__A	:						Union[str, Any]       =				model(
									    _A     ,					bbox=_A     ,					pixel_values=_A     ,					attention_mask=_A     ,					token_type_ids=_A     ,					start_positions=_A     ,					end_positions=_A     ,					)
									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 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.prepare_config_and_inputs()
									(
									    (
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				(
									    __A
									)    ,				
									)	:						Any       =				config_and_inputs
									__A	:						Any       =				{
									    '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 						_A(					snake_case__				, snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  str					    =	False
				UpperCamelCase    :  Optional[int]					    =	False
				UpperCamelCase    :  List[Any]					    =	False

				UpperCamelCase    :  Optional[Any]					    =	(
				    (
				        LayoutLMvaModel,
				        LayoutLMvaForSequenceClassification,
				        LayoutLMvaForTokenClassification,
				        LayoutLMvaForQuestionAnswering,
				    )
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase    :  Dict					    =	(
				    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
				    if is_torch_available()
				    else {}
				)



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ,					_A     ):
									# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
									# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
									# the sequence dimension of the text embedding only.
									# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
									return True



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				LayoutLMvaModelTester(self     )
									__A	:						List[Any]       =				ConfigTester(self     ,					config_class=_A     ,					hidden_size=37     )



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

									return inputs_dict



				def 					UpperCAmelCase_		(			self     ):
									self.config_tester.run_common_tests()



				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Tuple       =				self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
														__A	:						List[Any]       =				type
														self.model_tester.create_and_check_model(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Optional[int]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_A     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						Union[str, Any]       =				self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_A     )





				@slow
				def 					UpperCAmelCase_		(			self     ):
									for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														__A	:						str       =				LayoutLMvaModel.from_pretrained(_A     )
														self.assertIsNotNone(_A     )







def  _SCREAMING_SNAKE_CASE				(   )      ->      Any:
					__A	:						Optional[Any]       =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
					return image





@require_torch
class 						_A(					unittest.TestCase   ):


				"""simple docstring"""


				@cached_property
				def 					UpperCAmelCase_		(			self     ):
									return LayoutLMvaImageProcessor(apply_ocr=_A     ) if is_vision_available() else None





				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[str]       =				LayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'     ).to(_A     )

									__A	:						Any       =				self.default_image_processor
									__A	:						List[str]       =				prepare_img()
									__A	:						int       =				image_processor(images=_A     ,					return_tensors='pt'     ).pixel_values.to(_A     )

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

									# forward pass
									__A	:						Tuple       =				model(
									    input_ids=input_ids.to(_A     )     ,					bbox=bbox.to(_A     )     ,					pixel_values=pixel_values.to(_A     )     ,					)

									# verify the logits
									__A	:						Union[str, Any]       =				torch.Size((1, 199, 768)     )
									self.assertEqual(outputs.last_hidden_state.shape     ,					_A     )

									__A	:						Optional[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(_A     )

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











def  _SCREAMING_SNAKE_CASE				(   a					,						a = 0  )      ->      list:
					__A	:						int       =				length or len(a  )
					__A	:						str       =				False
					for i in range(length - 1  ):
										if list_data[i] > list_data[i + 1]:
															__A    ,				__A	:						Optional[int]       =				list_data[i + 1], list_data[i]
															__A	:						Union[str, Any]       =				True

					return list_data if not swapped else bubble_sort(a					,						length - 1  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




