


'''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  :
 def __init__(			self   ,					__magic_name__   ,					__magic_name__=13   ,					__magic_name__=7   ,					__magic_name__=True   ,					__magic_name__=True   ,					__magic_name__=True   ,					__magic_name__=True   ,					__magic_name__=99   ,					__magic_name__=32   ,					__magic_name__=5   ,					__magic_name__=4   ,					__magic_name__=37   ,					__magic_name__="gelu"   ,					__magic_name__=0.1   ,					__magic_name__=0.1   ,					__magic_name__=5_12   ,					__magic_name__=16   ,					__magic_name__=2   ,					__magic_name__=0.0_2   ,					__magic_name__=3   ,					__magic_name__=4   ,					__magic_name__=None   ,					__magic_name__=10_00   ,					)       ->				Dict:
      _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 				__UpperCAmelCase		(			self    )       ->				Any:
      _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 				__UpperCAmelCase		(			self    )       ->				Dict:
      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 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				Optional[Any]:
      _a         =				LayoutLMModel(config=__magic_name__    )
      model.to(__magic_name__    )
      model.eval()
      _a         =				model(__magic_name__   ,					__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__    )
      _a         =				model(__magic_name__   ,					__magic_name__   ,					token_type_ids=__magic_name__    )
      _a         =				model(__magic_name__   ,					__magic_name__    )
      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 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				List[str]:
      _a         =				LayoutLMForMaskedLM(config=__magic_name__    )
      model.to(__magic_name__    )
      model.eval()
      _a         =				model(__magic_name__   ,					__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__   ,					labels=__magic_name__    )
      self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.vocab_size)    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				Union[str, Any]:
      _a         =				self.num_labels
      _a         =				LayoutLMForSequenceClassification(__magic_name__    )
      model.to(__magic_name__    )
      model.eval()
      _a         =				model(
          __magic_name__   ,					__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__   ,					labels=__magic_name__    )
      self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.num_labels)    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				Union[str, Any]:
      _a         =				self.num_labels
      _a         =				LayoutLMForTokenClassification(config=__magic_name__    )
      model.to(__magic_name__    )
      model.eval()
      _a         =				model(__magic_name__   ,					__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__   ,					labels=__magic_name__    )
      self.parent.assertEqual(result.logits.shape   ,					(self.batch_size, self.seq_length, self.num_labels)    )
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__   ,					__magic_name__    )       ->				List[str]:
      _a         =				LayoutLMForQuestionAnswering(config=__magic_name__    )
      model.to(__magic_name__    )
      model.eval()
      _a         =				model(
          __magic_name__   ,					bbox=__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__   ,					start_positions=__magic_name__   ,					end_positions=__magic_name__   ,					)
      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    )       ->				Dict:
      _a         =				self.prepare_config_and_inputs()
      (
          _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 			a  (  _SCREAMING_SNAKE_CASE	,					_SCREAMING_SNAKE_CASE	,					unittest.TestCase							):
 _lowerCAmelCase			       =						(
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 _lowerCAmelCase			       =						(
     {
         """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			       =						True
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a         =				LayoutLMModelTester(self    )
      _a         =				ConfigTester(self   ,					config_class=__magic_name__   ,					hidden_size=37    )
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      self.config_tester.run_common_tests()
 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      _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(*__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      _a         =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Optional[int]:
      _a         =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Any:
      _a         =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__magic_name__    )
 def 				__UpperCAmelCase		(			self    )       ->				Any:
      _a         =				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__magic_name__    )




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






def 						_A					() ->	Dict:




     '''simple docstring'''



     _a         =				torch.tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]]					,    device=A_		)  # 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=A_		)  # noqa: E231
     _a         =				torch.tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]]					,    device=A_		)  # 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=A_		)  # noqa: E231
     # these are sequence labels (i.e. at the token level)
     _a         =				torch.tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]]					,    device=A_		)  # noqa: E231
     # fmt: on

     return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class 			a  (  unittest.TestCase							):
 @slow
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      _a         =				LayoutLMModel.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(__magic_name__    )

      _a         =				prepare_layoutlm_batch_inputs()

      # forward pass
      _a         =				model(input_ids=__magic_name__   ,					bbox=__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__    )

      # 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=__magic_name__   ,					)

      self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,					__magic_name__   ,					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=__magic_name__    )

      self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]   ,					__magic_name__   ,					atol=1e-3    )    )
 @slow
 def 				__UpperCAmelCase		(			self    )       ->				int:
      _a         =				LayoutLMForSequenceClassification.from_pretrained('microsoft/layoutlm-base-uncased'   ,					num_labels=2    ).to(
          __magic_name__    )

      _a         =				prepare_layoutlm_batch_inputs()

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

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

      # test the shape of the logits
      _a         =				outputs.logits
      _a         =				torch.Size((2, 2)    )
      self.assertEqual(logits.shape   ,					__magic_name__    )
 @slow
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a         =				LayoutLMForTokenClassification.from_pretrained('microsoft/layoutlm-base-uncased'   ,					num_labels=13    ).to(
          __magic_name__    )

      _a         =				prepare_layoutlm_batch_inputs()

      # forward pass
      _a         =				model(
          input_ids=__magic_name__   ,					bbox=__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__   ,					labels=__magic_name__    )

      # 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, 25, 13)    )
      self.assertEqual(logits.shape   ,					__magic_name__    )




 @slow
 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				LayoutLMForQuestionAnswering.from_pretrained('microsoft/layoutlm-base-uncased'    ).to(__magic_name__    )

      _a         =				prepare_layoutlm_batch_inputs()

      # forward pass
      _a         =				model(input_ids=__magic_name__   ,					bbox=__magic_name__   ,					attention_mask=__magic_name__   ,					token_type_ids=__magic_name__    )

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









"""simple docstring"""


from __future__ import annotations




def      snake_case    (A_ :list[int | float]      ,     A_ :int      ,     A_ :int ):







  '''simple docstring'''

  if len(A_ ) == 0:
    raise ValueError('find_min() arg is an empty sequence' )
  if (
      left >= len(A_ )
      or left < -len(A_ )
      or right >= len(A_ )
      or right < -len(A_ )
  ):
    raise IndexError('list index out of range' )
  if left == right:
    return nums[left]
  a	:     Optional[Any]							=  (left + right) >> 1  # the middle
  a	:     List[str]							=  find_min(A_      ,     A_      ,     A_ )  # find min in range[left, mid]
  a	:     Union[str, Any]							=  find_min(A_      ,     mid + 1      ,     A_ )  # find min in range[mid + 1, right]

  return left_min if left_min <= right_min else right_min


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)




