


'''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 						__UpperCamelCase	:





							def __init__(  self		:str  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[Any]=2  ,_UpperCamelCase		:str=3  ,_UpperCamelCase		:Optional[int]=4  ,_UpperCamelCase		:List[Any]=2  ,_UpperCamelCase		:Tuple=7  ,_UpperCamelCase		:Dict=True  ,_UpperCamelCase		:Tuple=True  ,_UpperCamelCase		:Union[str, Any]=True  ,_UpperCamelCase		:Any=True  ,_UpperCamelCase		:str=9_9  ,_UpperCamelCase		:List[Any]=3_6  ,_UpperCamelCase		:Optional[Any]=3  ,_UpperCamelCase		:Optional[int]=4  ,_UpperCamelCase		:List[str]=3_7  ,_UpperCamelCase		:List[str]="gelu"  ,_UpperCamelCase		:Tuple=0.1  ,_UpperCamelCase		:List[Any]=0.1  ,_UpperCamelCase		:Optional[Any]=5_1_2  ,_UpperCamelCase		:Tuple=1_6  ,_UpperCamelCase		:int=2  ,_UpperCamelCase		:int=0.02  ,_UpperCamelCase		:List[Any]=6  ,_UpperCamelCase		:List[Any]=6  ,_UpperCamelCase		:str=3  ,_UpperCamelCase		:Optional[Any]=4  ,_UpperCamelCase		:Optional[Any]=None  ,_UpperCamelCase		:Union[str, Any]=1_0_0_0  ,):
									snake_case_		:							str			       =						parent
									snake_case_		:							Optional[Any]			       =						batch_size
									snake_case_		:							List[str]			       =						num_channels
									snake_case_		:							str			       =						image_size
									snake_case_		:							str			       =						patch_size
									snake_case_		:							int			       =						text_seq_length
									snake_case_		:							List[str]			       =						is_training
									snake_case_		:							int			       =						use_input_mask
									snake_case_		:							Dict			       =						use_token_type_ids
									snake_case_		:							List[str]			       =						use_labels
									snake_case_		:							Optional[int]			       =						vocab_size
									snake_case_		:							Optional[int]			       =						hidden_size
									snake_case_		:							Dict			       =						num_hidden_layers
									snake_case_		:							Dict			       =						num_attention_heads
									snake_case_		:							int			       =						intermediate_size
									snake_case_		:							Optional[Any]			       =						hidden_act
									snake_case_		:							int			       =						hidden_dropout_prob
									snake_case_		:							Tuple			       =						attention_probs_dropout_prob
									snake_case_		:							Optional[int]			       =						max_position_embeddings
									snake_case_		:							Optional[Any]			       =						type_vocab_size
									snake_case_		:							int			       =						type_sequence_label_size
									snake_case_		:							List[Any]			       =						initializer_range
									snake_case_		:							int			       =						coordinate_size
									snake_case_		:							Tuple			       =						shape_size
									snake_case_		:							Optional[int]			       =						num_labels
									snake_case_		:							str			       =						num_choices
									snake_case_		:							Optional[int]			       =						scope
									snake_case_		:							int			       =						range_bbox

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





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Optional[Any]			       =						ids_tensor([self.batch_size, self.text_seq_length]  ,self.vocab_size			)

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

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

									snake_case_		:							List[str]			       =						None
									if self.use_input_mask:
											snake_case_		:							Optional[Any]			       =						random_attention_mask([self.batch_size, self.text_seq_length]			)

									snake_case_		:							str			       =						None
									if self.use_token_type_ids:
											snake_case_		:							Union[str, Any]			       =						ids_tensor([self.batch_size, self.text_seq_length]  ,self.type_vocab_size			)

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

									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 		a__   (  self		:int  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Any  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Union[str, Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Dict			):
									snake_case_		:							int			       =						LayoutLMvaModel(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()

									# text + image
									snake_case_		:							Optional[int]			       =						model(_UpperCamelCase  ,pixel_values=_UpperCamelCase			)
									snake_case_		:							List[str]			       =						model(
									    _UpperCamelCase  ,bbox=_UpperCamelCase  ,pixel_values=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase			)
									snake_case_		:							Tuple			       =						model(_UpperCamelCase  ,bbox=_UpperCamelCase  ,pixel_values=_UpperCamelCase  ,token_type_ids=_UpperCamelCase			)
									snake_case_		:							Optional[Any]			       =						model(_UpperCamelCase  ,bbox=_UpperCamelCase  ,pixel_values=_UpperCamelCase			)

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

									# text only
									snake_case_		:							Optional[Any]			       =						model(_UpperCamelCase			)
									self.parent.assertEqual(
									    result.last_hidden_state.shape  ,(self.batch_size, self.text_seq_length, self.hidden_size)			)

									# image only
									snake_case_		:							List[Any]			       =						model(pixel_values=_UpperCamelCase			)
									self.parent.assertEqual(
									    result.last_hidden_state.shape  ,(self.batch_size, self.image_seq_length, self.hidden_size)			)





							def 		a__   (  self		:List[str]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:int  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Union[str, Any]			):
									snake_case_		:							Union[str, Any]			       =						self.num_labels
									snake_case_		:							Tuple			       =						LayoutLMvaForSequenceClassification(_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							List[Any]			       =						model(
									    _UpperCamelCase  ,bbox=_UpperCamelCase  ,pixel_values=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,labels=_UpperCamelCase  ,)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)			)





							def 		a__   (  self		:int  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Dict  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:str			):
									snake_case_		:							Optional[int]			       =						self.num_labels
									snake_case_		:							Tuple			       =						LayoutLMvaForTokenClassification(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							Optional[int]			       =						model(
									    _UpperCamelCase  ,bbox=_UpperCamelCase  ,pixel_values=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,labels=_UpperCamelCase  ,)
									self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.text_seq_length, self.num_labels)			)





							def 		a__   (  self		:str  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:str  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:Optional[Any]			):
									snake_case_		:							str			       =						LayoutLMvaForQuestionAnswering(config=_UpperCamelCase			)
									model.to(_UpperCamelCase			)
									model.eval()
									snake_case_		:							int			       =						model(
									    _UpperCamelCase  ,bbox=_UpperCamelCase  ,pixel_values=_UpperCamelCase  ,attention_mask=_UpperCamelCase  ,token_type_ids=_UpperCamelCase  ,start_positions=_UpperCamelCase  ,end_positions=_UpperCamelCase  ,)
									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 		a__   (  self		:Union[str, Any]			):
									snake_case_		:							Optional[Any]			       =						self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      (
									    snake_case_
									)       ,      
									)		:							str			       =						config_and_inputs
									snake_case_		:							List[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 						__UpperCamelCase	(						lowercase__						,  lowercase__						,  unittest.TestCase							):
							lowercase			:		Optional[int]   		=  False
							lowercase			:		List[str]   		=  False
							lowercase			:		int   		=  False

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





							def 		a__   (  self		:Any  ,_UpperCamelCase		:List[Any]  ,_UpperCamelCase		:Tuple  ,_UpperCamelCase		:str  ,_UpperCamelCase		:Optional[int]  ,_UpperCamelCase		:Any			):
									# `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 		a__   (  self		:Any			):
									snake_case_		:							str			       =						LayoutLMvaModelTester(self			)
									snake_case_		:							Dict			       =						ConfigTester(self  ,config_class=_UpperCamelCase  ,hidden_size=3_7			)





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

									return inputs_dict





							def 		a__   (  self		:Any			):
									self.config_tester.run_common_tests()





							def 		a__   (  self		:Optional[Any]			):
									snake_case_		:							Any			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:Dict			):
									snake_case_		:							List[str]			       =						self.model_tester.prepare_config_and_inputs()
									for type in ["absolute", "relative_key", "relative_key_query"]:
											snake_case_		:							Dict			       =						type
											self.model_tester.create_and_check_model(*_UpperCamelCase			)





							def 		a__   (  self		:List[str]			):
									snake_case_		:							Any			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase			)





							def 		a__   (  self		:List[Any]			):
									snake_case_		:							Tuple			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase			)





							def 		a__   (  self		:Optional[int]			):
									snake_case_		:							List[str]			       =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase			)





							@slow
							def 		a__   (  self		:Dict			):
									for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_		:							Optional[Any]			       =						LayoutLMvaModel.from_pretrained(_UpperCamelCase			)
											self.assertIsNotNone(_UpperCamelCase			)





def      UpperCAmelCase      (		):







		'''simple docstring'''
		snake_case_		:							Optional[int]			       =						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
		return image






@require_torch
class 						__UpperCamelCase	(						unittest.TestCase							):





							@cached_property
							def 		a__   (  self		:Optional[Any]			):
									return LayoutLMvaImageProcessor(apply_ocr=_UpperCamelCase			) if is_vision_available() else None





							@slow
							def 		a__   (  self		:Tuple			):
									snake_case_		:							Optional[Any]			       =						LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""			).to(_UpperCamelCase			)

									snake_case_		:							Any			       =						self.default_image_processor
									snake_case_		:							str			       =						prepare_img()
									snake_case_		:							List[Any]			       =						image_processor(images=_UpperCamelCase  ,return_tensors="""pt"""			).pixel_values.to(_UpperCamelCase			)

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

									# forward pass
									snake_case_		:							List[Any]			       =						model(
									    input_ids=input_ids.to(_UpperCamelCase			)  ,bbox=bbox.to(_UpperCamelCase			)  ,pixel_values=pixel_values.to(_UpperCamelCase			)  ,)

									# verify the logits
									snake_case_		:							Optional[Any]			       =						torch.Size((1, 1_9_9, 7_6_8)			)
									self.assertEqual(outputs.last_hidden_state.shape  ,_UpperCamelCase			)

									snake_case_		:							Tuple			       =						torch.tensor(
									    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]			).to(_UpperCamelCase			)

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



'''simple docstring'''






def      UpperCAmelCase      (		lowerCamelCase_  :str ):







		'''simple docstring'''
		return " ".join(
		    """""".join(word[::-1] ) if len(lowerCamelCase_ ) > 4 else word for word in sentence.split() )


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(reverse_long_words('Hey wollef sroirraw'))