




import unittest

from transformers import LayoutLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import (
				    LayoutLMForMaskedLM,
				    LayoutLMForQuestionAnswering,
				    LayoutLMForSequenceClassification,
				    LayoutLMForTokenClassification,
				    LayoutLMModel,
				)





class 		A__    :






	def __init__(     self    ,					lowerCamelCase    ,					lowerCamelCase=13    ,					lowerCamelCase=7    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=True    ,					lowerCamelCase=99    ,					lowerCamelCase=32    ,					lowerCamelCase=5    ,					lowerCamelCase=4    ,					lowerCamelCase=37    ,					lowerCamelCase="gelu"    ,					lowerCamelCase=0.1    ,					lowerCamelCase=0.1    ,					lowerCamelCase=512    ,					lowerCamelCase=16    ,					lowerCamelCase=2    ,					lowerCamelCase=0.0_2    ,					lowerCamelCase=3    ,					lowerCamelCase=4    ,					lowerCamelCase=None    ,					lowerCamelCase=1000    ,					)				->       Tuple:




						"""simple docstring"""






						__magic_name__ :							Dict		       =							parent
						__magic_name__ :							int		       =							batch_size
						__magic_name__ :							int		       =							seq_length
						__magic_name__ :							List[str]		       =							is_training
						__magic_name__ :							List[Any]		       =							use_input_mask
						__magic_name__ :							Optional[Any]		       =							use_token_type_ids
						__magic_name__ :							Union[str, Any]		       =							use_labels
						__magic_name__ :							Any		       =							vocab_size
						__magic_name__ :							Optional[Any]		       =							hidden_size
						__magic_name__ :							Optional[Any]		       =							num_hidden_layers
						__magic_name__ :							List[str]		       =							num_attention_heads
						__magic_name__ :							str		       =							intermediate_size
						__magic_name__ :							int		       =							hidden_act
						__magic_name__ :							Union[str, Any]		       =							hidden_dropout_prob
						__magic_name__ :							List[str]		       =							attention_probs_dropout_prob
						__magic_name__ :							List[str]		       =							max_position_embeddings
						__magic_name__ :							Any		       =							type_vocab_size
						__magic_name__ :							List[Any]		       =							type_sequence_label_size
						__magic_name__ :							Optional[Any]		       =							initializer_range
						__magic_name__ :							str		       =							num_labels
						__magic_name__ :							str		       =							num_choices
						__magic_name__ :							Optional[int]		       =							scope
						__magic_name__ :							List[Any]		       =							range_bbox






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




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.vocab_size						)

						__magic_name__ :							Optional[Any]		       =							ids_tensor([self.batch_size, self.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]:
																					__magic_name__ :							Dict		       =							bbox[i, j, 3]
																					__magic_name__ :							Union[str, Any]		       =							bbox[i, j, 1]
																					__magic_name__ :							Union[str, Any]		       =							t
																if bbox[i, j, 2] < bbox[i, j, 0]:
																					__magic_name__ :							str		       =							bbox[i, j, 2]
																					__magic_name__ :							Dict		       =							bbox[i, j, 0]
																					__magic_name__ :							List[str]		       =							t

						__magic_name__ :							Any		       =							None
						if self.use_input_mask:
											__magic_name__ :							Any		       =							ids_tensor([self.batch_size, self.seq_length]    ,					vocab_size=2						)

						__magic_name__ :							int		       =							None
						if self.use_token_type_ids:
											__magic_name__ :							int		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.type_vocab_size						)

						__magic_name__ :							List[Any]		       =							None
						__magic_name__ :							Optional[Any]		       =							None
						__magic_name__ :							List[str]		       =							None
						if self.use_labels:
											__magic_name__ :							int		       =							ids_tensor([self.batch_size]    ,					self.type_sequence_label_size						)
											__magic_name__ :							List[Any]		       =							ids_tensor([self.batch_size, self.seq_length]    ,					self.num_labels						)
											__magic_name__ :							List[str]		       =							ids_tensor([self.batch_size]    ,					self.num_choices						)

						__magic_name__ :							Optional[Any]		       =							self.get_config()

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






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




						"""simple docstring"""






						return LayoutLMConfig(
						    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    ,					)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       int:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							LayoutLMModel(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Any		       =							model(lowerCamelCase    ,					lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							model(lowerCamelCase    ,					lowerCamelCase    ,					token_type_ids=lowerCamelCase						)
						__magic_name__ :							Optional[int]		       =							model(lowerCamelCase    ,					lowerCamelCase						)
						self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)						)
						self.parent.assertEqual(result.pooler_output.shape    ,					(self.batch_size, self.hidden_size)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							LayoutLMForMaskedLM(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Optional[Any]		       =							model(lowerCamelCase    ,					lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.vocab_size)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       str:




						"""simple docstring"""






						__magic_name__ :							int		       =							self.num_labels
						__magic_name__ :							Union[str, Any]		       =							LayoutLMForSequenceClassification(lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Dict		       =							model(
						    lowerCamelCase    ,					lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.num_labels)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.num_labels
						__magic_name__ :							Union[str, Any]		       =							LayoutLMForTokenClassification(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							Optional[int]		       =							model(lowerCamelCase    ,					lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)
						self.parent.assertEqual(result.logits.shape    ,					(self.batch_size, self.seq_length, self.num_labels)						)






	def 			lowercase				(     self    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase    ,					lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							LayoutLMForQuestionAnswering(config=lowerCamelCase						)
						model.to(lowerCamelCase						)
						model.eval()
						__magic_name__ :							List[str]		       =							model(
						    lowerCamelCase    ,					bbox=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 			lowercase				(     self						)				->       Optional[Any]:




						"""simple docstring"""






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





@require_torch
class 		A__    (       __SCREAMING_SNAKE_CASE		,					__SCREAMING_SNAKE_CASE		,					unittest.TestCase			):
	lowerCamelCase__		:			Tuple       =(
	    (
	        LayoutLMModel,
	        LayoutLMForMaskedLM,
	        LayoutLMForSequenceClassification,
	        LayoutLMForTokenClassification,
	        LayoutLMForQuestionAnswering,
	    )
	    if is_torch_available()
	    else None
	)
	lowerCamelCase__		:			int       =(
	    {
	        "document-question-answering": LayoutLMForQuestionAnswering,
	        "feature-extraction": LayoutLMModel,
	        "fill-mask": LayoutLMForMaskedLM,
	        "text-classification": LayoutLMForSequenceClassification,
	        "token-classification": LayoutLMForTokenClassification,
	        "zero-shot": LayoutLMForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowerCamelCase__		:			Dict       =True






	def 			lowercase				(     self						)				->       str:




						"""simple docstring"""






						__magic_name__ :							Optional[int]		       =							LayoutLMModelTester(self						)
						__magic_name__ :							Dict		       =							ConfigTester(self    ,					config_class=lowerCamelCase    ,					hidden_size=37						)






	def 			lowercase				(     self						)				->       Tuple:




						"""simple docstring"""






						self.config_tester.run_common_tests()






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*lowerCamelCase						)






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




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.model_tester.prepare_config_and_inputs()
						for type in ["absolute", "relative_key", "relative_key_query"]:
											__magic_name__ :							List[Any]		       =							type
											self.model_tester.create_and_check_model(*lowerCamelCase						)






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




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       List[str]:




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase						)






	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							List[str]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*lowerCamelCase						)






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




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*lowerCamelCase						)






	@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''						)
	def 			lowercase				(     self						)				->       Dict:




						"""simple docstring"""






						pass







def 			lowerCAmelCase  (     )  ->int:






					"""simple docstring"""






					__magic_name__ :							int		       =							torch.tensor([[101,1019,1014,1016,1037,1_2849,4747,1004,1_4246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,1_1300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,1_9274,2772,6205,2_7814,1_6147,1_6147,4343,2047,1_0283,1_0969,1_4389,1012,2338,102]],     device=UpperCAmelCase	)  # noqa: E231
					__magic_name__ :							Dict		       =							torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],],     device=UpperCAmelCase	)  # noqa: E231
					__magic_name__ :							Optional[Any]		       =							torch.tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]],     device=UpperCAmelCase	)  # noqa: E231
					__magic_name__ :							Dict		       =							torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]],     device=UpperCAmelCase	)  # noqa: E231
					# these are sequence labels (i.e. at the token level)
					__magic_name__ :							List[str]		       =							torch.tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]],     device=UpperCAmelCase	)  # noqa: E231
					# fmt: on

					return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class 		A__    (       unittest.TestCase			):






	@slow
	def 			lowercase				(     self						)				->       List[Any]:




						"""simple docstring"""






						__magic_name__ :							List[Any]		       =							LayoutLMModel.from_pretrained('''microsoft/layoutlm-base-uncased'''						).to(lowerCamelCase						)

						__magic_name__ :							str		       =							prepare_layoutlm_batch_inputs()

						# forward pass
						__magic_name__ :							Union[str, Any]		       =							model(input_ids=lowerCamelCase    ,					bbox=lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase						)

						# test the sequence output on [0, :3, :3]
						__magic_name__ :							str		       =							torch.tensor(
						    [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]    ,					device=lowerCamelCase    ,					)

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

						# test the pooled output on [1, :3]
						__magic_name__ :							Optional[Any]		       =							torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]    ,					device=lowerCamelCase						)

						self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]    ,					lowerCamelCase    ,					atol=1e-3						)						)






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




						"""simple docstring"""






						__magic_name__ :							Tuple		       =							LayoutLMForSequenceClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,					num_labels=2						).to(
						    lowerCamelCase						)

						__magic_name__ :							Tuple		       =							prepare_layoutlm_batch_inputs()

						# forward pass
						__magic_name__ :							List[str]		       =							model(
						    input_ids=lowerCamelCase    ,					bbox=lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=torch.tensor([1, 1]    ,					device=lowerCamelCase						)    ,					)

						# test whether we get a loss as a scalar
						__magic_name__ :							int		       =							outputs.loss
						__magic_name__ :							int		       =							torch.Size([]						)
						self.assertEqual(loss.shape    ,					lowerCamelCase						)

						# test the shape of the logits
						__magic_name__ :							Union[str, Any]		       =							outputs.logits
						__magic_name__ :							int		       =							torch.Size((2, 2)						)
						self.assertEqual(logits.shape    ,					lowerCamelCase						)






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




						"""simple docstring"""






						__magic_name__ :							Union[str, Any]		       =							LayoutLMForTokenClassification.from_pretrained('''microsoft/layoutlm-base-uncased'''    ,					num_labels=13						).to(
						    lowerCamelCase						)

						__magic_name__ :							int		       =							prepare_layoutlm_batch_inputs()

						# forward pass
						__magic_name__ :							str		       =							model(
						    input_ids=lowerCamelCase    ,					bbox=lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase    ,					labels=lowerCamelCase						)

						# test the loss calculation to be around 2.65
						# expected_loss = torch.tensor(2.65, device=torch_device)

						# The loss is currently somewhat random and can vary between 0.1-0.3 atol.
						# self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

						# test the shape of the logits
						__magic_name__ :							Optional[int]		       =							outputs.logits
						__magic_name__ :							Union[str, Any]		       =							torch.Size((2, 25, 13)						)
						self.assertEqual(logits.shape    ,					lowerCamelCase						)






	@slow
	def 			lowercase				(     self						)				->       Any:




						"""simple docstring"""






						__magic_name__ :							Any		       =							LayoutLMForQuestionAnswering.from_pretrained('''microsoft/layoutlm-base-uncased'''						).to(lowerCamelCase						)

						__magic_name__ :							Optional[Any]		       =							prepare_layoutlm_batch_inputs()

						# forward pass
						__magic_name__ :							int		       =							model(input_ids=lowerCamelCase    ,					bbox=lowerCamelCase    ,					attention_mask=lowerCamelCase    ,					token_type_ids=lowerCamelCase						)

						# test the shape of the logits
						__magic_name__ :							Dict		       =							torch.Size((2, 25)						)
						self.assertEqual(outputs.start_logits.shape    ,					lowerCamelCase						)
						self.assertEqual(outputs.end_logits.shape    ,					lowerCamelCase						)









from ..utils import requires_backends


lowercase_		=							None





class 		A__    :






	def __init__(     self    ,					*lowerCamelCase    ,					**lowerCamelCase						)				->       Optional[Any]:




						"""simple docstring"""






						requires_backends(self    ,					['''detectron2''']						)






	@classmethod
	def 			lowercase				(     cls    ,					*lowerCamelCase    ,					**lowerCamelCase						)				->       List[Any]:




						"""simple docstring"""






						requires_backends(cls    ,					['''detectron2''']						)



