



'''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 						UpperCAmelCase						:


 '''simple docstring'''




 def __init__( self				,	lowerCAmelCase_				,	lowerCAmelCase_=1_3				,	lowerCAmelCase_=7				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=True				,	lowerCAmelCase_=9_9				,	lowerCAmelCase_=3_2				,	lowerCAmelCase_=5				,	lowerCAmelCase_=4				,	lowerCAmelCase_=3_7				,	lowerCAmelCase_="gelu"				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=0.1				,	lowerCAmelCase_=5_1_2				,	lowerCAmelCase_=1_6				,	lowerCAmelCase_=2				,	lowerCAmelCase_=0.0_2				,	lowerCAmelCase_=3				,	lowerCAmelCase_=4				,	lowerCAmelCase_=None				,	lowerCAmelCase_=1_0_0_0				,	)			->   str:



      """simple docstring"""







      a_									=parent
      a_									=batch_size
      a_									=seq_length
      a_									=is_training
      a_									=use_input_mask
      a_									=use_token_type_ids
      a_									=use_labels
      a_									=vocab_size
      a_									=hidden_size
      a_									=num_hidden_layers
      a_									=num_attention_heads
      a_									=intermediate_size
      a_									=hidden_act
      a_									=hidden_dropout_prob
      a_									=attention_probs_dropout_prob
      a_									=max_position_embeddings
      a_									=type_vocab_size
      a_									=type_sequence_label_size
      a_									=initializer_range
      a_									=num_labels
      a_									=num_choices
      a_									=scope
      a_									=range_bbox




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



      """simple docstring"""







      a_									=ids_tensor([self.batch_size, self.seq_length]				,	self.vocab_size)

      a_									=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]:
                     a_									=bbox[i, j, 3]
                     a_									=bbox[i, j, 1]
                     a_									=t
                if bbox[i, j, 2] < bbox[i, j, 0]:
                     a_									=bbox[i, j, 2]
                     a_									=bbox[i, j, 0]
                     a_									=t

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

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

      a_									=None
      a_									=None
      a_									=None
      if self.use_labels:
           a_									=ids_tensor([self.batch_size]				,	self.type_sequence_label_size)
           a_									=ids_tensor([self.batch_size, self.seq_length]				,	self.num_labels)
           a_									=ids_tensor([self.batch_size]				,	self.num_choices)

      a_									=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				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_				,	lowerCAmelCase_)			->   Dict:



      """simple docstring"""







      a_									=LayoutLMModel(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      a_									=model(lowerCAmelCase_				,	lowerCAmelCase_				,	attention_mask=lowerCAmelCase_				,	token_type_ids=lowerCAmelCase_)
      a_									=model(lowerCAmelCase_				,	lowerCAmelCase_				,	token_type_ids=lowerCAmelCase_)
      a_									=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_)			->   Tuple:



      """simple docstring"""







      a_									=LayoutLMForMaskedLM(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      a_									=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"""







      a_									=self.num_labels
      a_									=LayoutLMForSequenceClassification(lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      a_									=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_)			->   Optional[int]:



      """simple docstring"""







      a_									=self.num_labels
      a_									=LayoutLMForTokenClassification(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      a_									=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_)			->   Tuple:



      """simple docstring"""







      a_									=LayoutLMForQuestionAnswering(config=lowerCAmelCase_)
      model.to(lowerCAmelCase_)
      model.eval()
      a_									=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)			->   Tuple:



      """simple docstring"""







      a_									=self.prepare_config_and_inputs()
      (
          (
          a_
      )		,    (
          a_
      )		,    (
          a_
      )		,    (
          a_
      )		,    (
          a_
      )		,    (
          a_
      )		,    (
          a_
      )		,    (
          a_
      )		,    
      )									=config_and_inputs
      a_									={
          "input_ids": input_ids,
          "bbox": bbox,
          "token_type_ids": token_type_ids,
          "attention_mask": input_mask,
      }
      return config, inputs_dict







@require_torch
class 						UpperCAmelCase						(		__a       ,						__a       ,						unittest.TestCase):


 '''simple docstring'''




 __magic_name__       :			Tuple	=   (
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 __magic_name__       :			str	=   (
     {
         "document-question-answering": LayoutLMForQuestionAnswering,
         "feature-extraction": LayoutLMModel,
         "fill-mask": LayoutLMForMaskedLM,
         "text-classification": LayoutLMForSequenceClassification,
         "token-classification": LayoutLMForTokenClassification,
         "zero-shot": LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __magic_name__       :			Optional[int]	=   True




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



      """simple docstring"""







      a_									=LayoutLMModelTester(self)
      a_									=ConfigTester(self				,	config_class=lowerCAmelCase_				,	hidden_size=3_7)




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



      """simple docstring"""







      self.config_tester.run_common_tests()




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCAmelCase_)




 def    lowercase_	( self)			->   int:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           a_									=type
           self.model_tester.create_and_check_model(*lowerCAmelCase_)




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



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase_)




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



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase_)




 def    lowercase_	( self)			->   Dict:



      """simple docstring"""







      a_									=self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase_)




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



      """simple docstring"""







      a_									=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)			->   Tuple:



      """simple docstring"""







      pass






def 			UpperCAmelCase_							(		):


     '''simple docstring'''







     a_									=torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]							,						device=lowercase__							)  # noqa: E231
     a_									=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=lowercase__							)  # noqa: E231
     a_									=torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]							,						device=lowercase__							)  # noqa: E231
     a_									=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=lowercase__							)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     a_									=torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]							,						device=lowercase__							)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels







@require_torch
class 						UpperCAmelCase						(		unittest.TestCase):


 '''simple docstring'''




 @slow
 def    lowercase_	( self)			->   int:



      """simple docstring"""







      a_									=LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased").to(lowerCAmelCase_)

      a_		,    a_		,    a_		,    a_		,    a_									=prepare_layoutlm_batch_inputs()

      # forward pass
      a_									=model(input_ids=lowerCAmelCase_				,	bbox=lowerCAmelCase_				,	attention_mask=lowerCAmelCase_				,	token_type_ids=lowerCAmelCase_)

      # test the sequence output on [0, :3, :3]
      a_									=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]
      a_									=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)			->   str:



      """simple docstring"""







      a_									=LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"				,	num_labels=2).to(
          lowerCAmelCase_)

      a_		,    a_		,    a_		,    a_		,    a_									=prepare_layoutlm_batch_inputs()

      # forward pass
      a_									=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
      a_									=outputs.loss
      a_									=torch.Size([])
      self.assertEqual(loss.shape				,	lowerCAmelCase_)

      # test the shape of the logits
      a_									=outputs.logits
      a_									=torch.Size((2, 2))
      self.assertEqual(logits.shape				,	lowerCAmelCase_)




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



      """simple docstring"""







      a_									=LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"				,	num_labels=1_3).to(
          lowerCAmelCase_)

      a_		,    a_		,    a_		,    a_		,    a_									=prepare_layoutlm_batch_inputs()

      # forward pass
      a_									=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
      a_									=outputs.logits
      a_									=torch.Size((2, 2_5, 1_3))
      self.assertEqual(logits.shape				,	lowerCAmelCase_)




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



      """simple docstring"""







      a_									=LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased").to(lowerCAmelCase_)

      a_		,    a_		,    a_		,    a_		,    a_									=prepare_layoutlm_batch_inputs()

      # forward pass
      a_									=model(input_ids=lowerCAmelCase_				,	bbox=lowerCAmelCase_				,	attention_mask=lowerCAmelCase_				,	token_type_ids=lowerCAmelCase_)

      # test the shape of the logits
      a_									=torch.Size((2, 2_5))
      self.assertEqual(outputs.start_logits.shape				,	lowerCAmelCase_)
      self.assertEqual(outputs.end_logits.shape				,	lowerCAmelCase_)







'''simple docstring'''


from dataclasses import dataclass
from typing import Callable, Optional

import datasets







@dataclass
class 						UpperCAmelCase						(		datasets.BuilderConfig):


 '''simple docstring'''




 __magic_name__       :			Optional[Callable]	=   None
 __magic_name__       :			Optional[dict]	=   None
 __magic_name__       :			Optional[datasets.Features]	=   None




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



      """simple docstring"""







      assert self.generator is not None, "generator must be specified"

      if self.gen_kwargs is None:
           a_									={}







class 						UpperCAmelCase						(		datasets.GeneratorBasedBuilder):


 '''simple docstring'''




 __magic_name__       :			Tuple	=   GeneratorConfig




 def    lowercase_	( self)			->   Tuple:



      """simple docstring"""







      return datasets.DatasetInfo(features=self.config.features)




 def    lowercase_	( self				,	lowerCAmelCase_)			->   int:



      """simple docstring"""







      return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,	gen_kwargs=self.config.gen_kwargs)]




 def    lowercase_	( self				,	**lowerCAmelCase_)			->   List[Any]:



      """simple docstring"""







      for idx, ex in enumerate(self.config.generator(**lowerCAmelCase_)):
           yield idx, ex


