






"""simple docstring"""





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       :





			'''simple docstring'''






			def __init__(   self			,				UpperCAmelCase_			,				UpperCAmelCase_=13			,				UpperCAmelCase_=7			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=True			,				UpperCAmelCase_=99			,				UpperCAmelCase_=32			,				UpperCAmelCase_=5			,				UpperCAmelCase_=4			,				UpperCAmelCase_=37			,				UpperCAmelCase_="gelu"			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=0.1			,				UpperCAmelCase_=512			,				UpperCAmelCase_=16			,				UpperCAmelCase_=2			,				UpperCAmelCase_=0.02			,				UpperCAmelCase_=3			,				UpperCAmelCase_=4			,				UpperCAmelCase_=None			,				UpperCAmelCase_=1_000			,				)	-> Optional[int]:




										'''simple docstring'''




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





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




										'''simple docstring'''




										lowercase__:    int        =      ids_tensor([self.batch_size, self.seq_length]			,				self.vocab_size)

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

										lowercase__:    Dict        =      None
										if self.use_input_mask:
																	lowercase__:    str        =      ids_tensor([self.batch_size, self.seq_length]			,				vocab_size=2)

										lowercase__:    str        =      None
										if self.use_token_type_ids:
																	lowercase__:    List[Any]        =      ids_tensor([self.batch_size, self.seq_length]			,				self.type_vocab_size)

										lowercase__:    Tuple        =      None
										lowercase__:    Dict        =      None
										lowercase__:    Any        =      None
										if self.use_labels:
																	lowercase__:    List[Any]        =      ids_tensor([self.batch_size]			,				self.type_sequence_label_size)
																	lowercase__:    Any        =      ids_tensor([self.batch_size, self.seq_length]			,				self.num_labels)
																	lowercase__:    List[str]        =      ids_tensor([self.batch_size]			,				self.num_choices)

										lowercase__:    int        =      self.get_config()

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





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




										'''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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[Any]:




										'''simple docstring'''




										lowercase__:    Any        =      LayoutLMModel(config=UpperCAmelCase_)
										model.to(UpperCAmelCase_)
										model.eval()
										lowercase__:    Tuple        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
										lowercase__:    Dict        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)
										lowercase__:    Any        =      model(UpperCAmelCase_			,				UpperCAmelCase_)
										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			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[Any]:




										'''simple docstring'''




										lowercase__:    int        =      LayoutLMForMaskedLM(config=UpperCAmelCase_)
										model.to(UpperCAmelCase_)
										model.eval()
										lowercase__:    str        =      model(UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
										self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.seq_length, self.vocab_size))





			def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> Optional[int]:




										'''simple docstring'''




										lowercase__:    Union[str, Any]        =      self.num_labels
										lowercase__:    Optional[Any]        =      LayoutLMForSequenceClassification(UpperCAmelCase_)
										model.to(UpperCAmelCase_)
										model.eval()
										lowercase__:    Optional[Any]        =      model(
										    UpperCAmelCase_			,				UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)
										self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels))





			def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> List[str]:




										'''simple docstring'''




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





			def __lowercase    (   self			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_			,				UpperCAmelCase_)	-> str:




										'''simple docstring'''




										lowercase__:    Dict        =      LayoutLMForQuestionAnswering(config=UpperCAmelCase_)
										model.to(UpperCAmelCase_)
										model.eval()
										lowercase__:    List[Any]        =      model(
										    UpperCAmelCase_			,				bbox=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 __lowercase    (   self)	-> Tuple:




										'''simple docstring'''




										lowercase__:    int        =      self.prepare_config_and_inputs()
										(
										    lowercase__     
										):    int        =      config_and_inputs
										lowercase__:    Optional[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       (						lowercase_    ,					lowercase_    ,					unittest.TestCase   ):





			'''simple docstring'''






			UpperCamelCase__      					=    (
			    (
			        LayoutLMModel,
			        LayoutLMForMaskedLM,
			        LayoutLMForSequenceClassification,
			        LayoutLMForTokenClassification,
			        LayoutLMForQuestionAnswering,
			    )
			    if is_torch_available()
			    else None
			)
			UpperCamelCase__      					=    (
			    {
			        """document-question-answering""": LayoutLMForQuestionAnswering,
			        """feature-extraction""": LayoutLMModel,
			        """fill-mask""": LayoutLMForMaskedLM,
			        """text-classification""": LayoutLMForSequenceClassification,
			        """token-classification""": LayoutLMForTokenClassification,
			        """zero-shot""": LayoutLMForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			UpperCamelCase__      					=    True





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




										'''simple docstring'''




										lowercase__:    List[str]        =      LayoutLMModelTester(self)
										lowercase__:    List[str]        =      ConfigTester(self			,				config_class=UpperCAmelCase_			,				hidden_size=37)





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




										'''simple docstring'''




										self.config_tester.run_common_tests()





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




										'''simple docstring'''




										lowercase__:    int        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase_)





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




										'''simple docstring'''




										lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
																	lowercase__:    Union[str, Any]        =      type
																	self.model_tester.create_and_check_model(*UpperCAmelCase_)





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




										'''simple docstring'''




										lowercase__:    Any        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_)





			def __lowercase    (   self)	-> Dict:




										'''simple docstring'''




										lowercase__:    List[Any]        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_)





			def __lowercase    (   self)	-> int:




										'''simple docstring'''




										lowercase__:    Dict        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_)





			def __lowercase    (   self)	-> str:




										'''simple docstring'''




										lowercase__:    Optional[Any]        =      self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_)





			@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests.")
			def __lowercase    (   self)	-> List[Any]:




										'''simple docstring'''




										pass







def     A(  ):
							"""simple docstring"""





							lowercase__:    Any        =      torch.tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]						,       device=snake_case_		)  # noqa: E231
							lowercase__:    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=snake_case_		)  # noqa: E231
							lowercase__:    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=snake_case_		)  # noqa: E231
							lowercase__:    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=snake_case_		)  # noqa: E231
							# these are sequence labels (i.e. at the token level)
							lowercase__:    int        =      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=snake_case_		)  # noqa: E231
							# fmt: on

							return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 		_a       (						unittest.TestCase   ):





			'''simple docstring'''






			@slow
			def __lowercase    (   self)	-> Any:




										'''simple docstring'''




										lowercase__:    str        =      LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

										lowercase__:    Dict        =      prepare_layoutlm_batch_inputs()

										# forward pass
										lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

										# test the sequence output on [0, :3, :3]
										lowercase__:    Optional[int]        =      torch.tensor(
										    [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]			,				device=UpperCAmelCase_			,				)

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

										# test the pooled output on [1, :3]
										lowercase__:    List[Any]        =      torch.tensor([-0.65_80, -0.02_14, 0.85_52]			,				device=UpperCAmelCase_)

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





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




										'''simple docstring'''




										lowercase__:    int        =      LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=2).to(
										    UpperCAmelCase_)

										lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

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

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

										# test the shape of the logits
										lowercase__:    str        =      outputs.logits
										lowercase__:    Any        =      torch.Size((2, 2))
										self.assertEqual(logits.shape			,				UpperCAmelCase_)





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




										'''simple docstring'''




										lowercase__:    Tuple        =      LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"			,				num_labels=13).to(
										    UpperCAmelCase_)

										lowercase__:    List[Any]        =      prepare_layoutlm_batch_inputs()

										# forward pass
										lowercase__:    Any        =      model(
										    input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_			,				labels=UpperCAmelCase_)

										# 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
										lowercase__:    int        =      outputs.logits
										lowercase__:    int        =      torch.Size((2, 25, 13))
										self.assertEqual(logits.shape			,				UpperCAmelCase_)





			@slow
			def __lowercase    (   self)	-> Tuple:




										'''simple docstring'''




										lowercase__:    Tuple        =      LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(UpperCAmelCase_)

										lowercase__:    Tuple        =      prepare_layoutlm_batch_inputs()

										# forward pass
										lowercase__:    str        =      model(input_ids=UpperCAmelCase_			,				bbox=UpperCAmelCase_			,				attention_mask=UpperCAmelCase_			,				token_type_ids=UpperCAmelCase_)

										# test the shape of the logits
										lowercase__:    str        =      torch.Size((2, 25))
										self.assertEqual(outputs.start_logits.shape			,				UpperCAmelCase_)
										self.assertEqual(outputs.end_logits.shape			,				UpperCAmelCase_)














"""simple docstring"""





def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       assert isinstance(snake_case_						,       snake_case_		), F"""The input value of [set_a={set_a}] is not a set"""
       assert isinstance(snake_case_						,       snake_case_		), F"""The input value of [set_b={set_b}] is not a set"""

       return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
  from doctest import testmod

  testmod()






