



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    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				List[Any]  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Dict=1_3  ,	UpperCamelCase			:				str=7  ,	UpperCamelCase			:				Tuple=True  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Union[str, Any]=True  ,	UpperCamelCase			:				Dict=True  ,	UpperCamelCase			:				Any=9_9  ,	UpperCamelCase			:				str=3_2  ,	UpperCamelCase			:				int=5  ,	UpperCamelCase			:				str=4  ,	UpperCamelCase			:				Dict=3_7  ,	UpperCamelCase			:				List[str]="gelu"  ,	UpperCamelCase			:				Optional[Any]=0.1  ,	UpperCamelCase			:				Any=0.1  ,	UpperCamelCase			:				Tuple=5_1_2  ,	UpperCamelCase			:				Any=1_6  ,	UpperCamelCase			:				str=2  ,	UpperCamelCase			:				List[Any]=0.0_2  ,	UpperCamelCase			:				List[str]=3  ,	UpperCamelCase			:				Optional[int]=4  ,	UpperCamelCase			:				Dict=None  ,	UpperCamelCase			:				List[str]=1_0_0_0  ,	)->Optional[int]:
      __SCREAMING_SNAKE_CASE							:     int							=						parent
      __SCREAMING_SNAKE_CASE							:     str							=						batch_size
      __SCREAMING_SNAKE_CASE							:     str							=						seq_length
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						is_training
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						use_input_mask
      __SCREAMING_SNAKE_CASE							:     Any							=						use_token_type_ids
      __SCREAMING_SNAKE_CASE							:     Dict							=						use_labels
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						vocab_size
      __SCREAMING_SNAKE_CASE							:     Any							=						hidden_size
      __SCREAMING_SNAKE_CASE							:     Dict							=						num_hidden_layers
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						num_attention_heads
      __SCREAMING_SNAKE_CASE							:     Dict							=						intermediate_size
      __SCREAMING_SNAKE_CASE							:     int							=						hidden_act
      __SCREAMING_SNAKE_CASE							:     int							=						hidden_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						max_position_embeddings
      __SCREAMING_SNAKE_CASE							:     int							=						type_vocab_size
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						type_sequence_label_size
      __SCREAMING_SNAKE_CASE							:     int							=						initializer_range
      __SCREAMING_SNAKE_CASE							:     Dict							=						num_labels
      __SCREAMING_SNAKE_CASE							:     Any							=						num_choices
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						scope
      __SCREAMING_SNAKE_CASE							:     Any							=						range_bbox





     def 						__snake_case     (							self			:				Any					)->str:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						ids_tensor([self.batch_size, self.seq_length]  ,	self.vocab_size					)

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

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

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

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

      __SCREAMING_SNAKE_CASE							:     str							=						self.get_config()

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





     def 						__snake_case     (							self			:				List[Any]					)->Any:
      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 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				Union[str, Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     str							=						LayoutLMModel(config=UpperCamelCase					)
      model.to(UpperCamelCase					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						model(UpperCamelCase  ,	UpperCamelCase  ,	attention_mask=UpperCamelCase  ,	token_type_ids=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						model(UpperCamelCase  ,	UpperCamelCase  ,	token_type_ids=UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[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 						__snake_case     (							self			:				List[str]  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				str  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				str					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						LayoutLMForMaskedLM(config=UpperCamelCase					)
      model.to(UpperCamelCase					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     Optional[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.vocab_size)					)





     def 						__snake_case     (							self			:				str  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				List[str]  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				int  ,	UpperCamelCase			:				Optional[int]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     int							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     Any							=						LayoutLMForSequenceClassification(UpperCamelCase					)
      model.to(UpperCamelCase					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						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 						__snake_case     (							self			:				Any  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Union[str, Any]  ,	UpperCamelCase			:				List[Any]  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Tuple  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				List[Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     Dict							=						self.num_labels
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						LayoutLMForTokenClassification(config=UpperCamelCase					)
      model.to(UpperCamelCase					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     int							=						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 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				Optional[Any]  ,	UpperCamelCase			:				Any  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Dict  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Optional[int]  ,	UpperCamelCase			:				Union[str, Any]					)->str:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						LayoutLMForQuestionAnswering(config=UpperCamelCase					)
      model.to(UpperCamelCase					)
      model.eval()
      __SCREAMING_SNAKE_CASE							:     Union[str, 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 						__snake_case     (							self			:				Tuple					)->Any:
      __SCREAMING_SNAKE_CASE							:     int							=						self.prepare_config_and_inputs()
      (
          __SCREAMING_SNAKE_CASE							
      )							:     str							=						config_and_inputs
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						{
          '''input_ids''': input_ids,
          '''bbox''': bbox,
          '''token_type_ids''': token_type_ids,
          '''attention_mask''': input_mask,
      }
      return config, inputs_dict






@require_torch
class    _SCREAMING_SNAKE_CASE						(UpperCamelCase      ,     UpperCamelCase      ,     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 						__snake_case     (							self			:				str					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Dict							=						LayoutLMModelTester(self					)
      __SCREAMING_SNAKE_CASE							:     str							=						ConfigTester(self  ,	config_class=UpperCamelCase  ,	hidden_size=3_7					)





     def 						__snake_case     (							self			:				Optional[int]					)->int:
      self.config_tester.run_common_tests()





     def 						__snake_case     (							self			:				List[Any]					)->Optional[Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*UpperCamelCase					)





     def 						__snake_case     (							self			:				str					)->int:
      __SCREAMING_SNAKE_CASE							:     int							=						self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       __SCREAMING_SNAKE_CASE							:     Any							=						type
       self.model_tester.create_and_check_model(*UpperCamelCase					)





     def 						__snake_case     (							self			:				List[Any]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase					)





     def 						__snake_case     (							self			:				Union[str, Any]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*UpperCamelCase					)





     def 						__snake_case     (							self			:				Optional[Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     str							=						self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*UpperCamelCase					)





     def 						__snake_case     (							self			:				List[str]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     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 						__snake_case     (							self			:				Dict					)->Any:
      pass






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[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=SCREAMING_SNAKE_CASE__  )  # noqa: E231
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						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=SCREAMING_SNAKE_CASE__  )  # noqa: E231
 __SCREAMING_SNAKE_CASE							:     Tuple							=						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=SCREAMING_SNAKE_CASE__  )  # noqa: E231
 __SCREAMING_SNAKE_CASE							:     int							=						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=SCREAMING_SNAKE_CASE__  )  # noqa: E231
 # these are sequence labels (i.e. at the token level)
 __SCREAMING_SNAKE_CASE							:     Dict							=						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=SCREAMING_SNAKE_CASE__  )  # noqa: E231
 # fmt: on

 return input_ids, attention_mask, bbox, token_type_ids, labels






@require_torch
class    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     @slow
     def 						__snake_case     (							self			:				Dict					)->int:
      __SCREAMING_SNAKE_CASE							:     Any							=						LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"					).to(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						prepare_layoutlm_batch_inputs()

      # forward pass
      __SCREAMING_SNAKE_CASE							:     Tuple							=						model(input_ids=UpperCamelCase  ,	bbox=UpperCamelCase  ,	attention_mask=UpperCamelCase  ,	token_type_ids=UpperCamelCase					)

      # test the sequence output on [0, :3, :3]
      __SCREAMING_SNAKE_CASE							:     int							=						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=UpperCamelCase  ,	)

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

      # test the pooled output on [1, :3]
      __SCREAMING_SNAKE_CASE							:     List[str]							=						torch.tensor([-0.6_5_8_0, -0.0_2_1_4, 0.8_5_5_2]  ,	device=UpperCamelCase					)

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





     @slow
     def 						__snake_case     (							self			:				int					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Dict							=						LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,	num_labels=2					).to(
          UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						prepare_layoutlm_batch_inputs()

      # forward pass
      __SCREAMING_SNAKE_CASE							:     Dict							=						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
      __SCREAMING_SNAKE_CASE							:     List[str]							=						outputs.loss
      __SCREAMING_SNAKE_CASE							:     Tuple							=						torch.Size([]					)
      self.assertEqual(loss.shape  ,	UpperCamelCase					)

      # test the shape of the logits
      __SCREAMING_SNAKE_CASE							:     Dict							=						outputs.logits
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						torch.Size((2, 2)					)
      self.assertEqual(logits.shape  ,	UpperCamelCase					)





     @slow
     def 						__snake_case     (							self			:				str					)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"  ,	num_labels=1_3					).to(
          UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						prepare_layoutlm_batch_inputs()

      # forward pass
      __SCREAMING_SNAKE_CASE							:     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
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						outputs.logits
      __SCREAMING_SNAKE_CASE							:     Dict							=						torch.Size((2, 2_5, 1_3)					)
      self.assertEqual(logits.shape  ,	UpperCamelCase					)






     @slow
     def 						__snake_case     (							self			:				Optional[int]					)->Dict:
      __SCREAMING_SNAKE_CASE							:     Any							=						LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"					).to(UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						prepare_layoutlm_batch_inputs()

      # forward pass
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						model(input_ids=UpperCamelCase  ,	bbox=UpperCamelCase  ,	attention_mask=UpperCamelCase  ,	token_type_ids=UpperCamelCase					)

      # test the shape of the logits
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						torch.Size((2, 2_5)					)
      self.assertEqual(outputs.start_logits.shape  ,	UpperCamelCase					)
      self.assertEqual(outputs.end_logits.shape  ,	UpperCamelCase					)













'''simple docstring'''







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
					snake_case_  :							Union[str, Any]            =	[]

					snake_case_  :							List[Any]            =	os.path.join("tests", "models", "**", "test_modeling_*.py")
					snake_case_  :							int            =	glob.glob(pattern)
					# TODO: deal with TF/Flax too
					snake_case_  :							Optional[int]            =	[
					    x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
					]

					for test_file in test_files:
										snake_case_  :							Any            =	get_tester_classes(test_file)
										for tester_class in tester_classes:
															# A few tester classes don't have `parent` parameter in `__init__`.
															# TODO: deal this better
															try:
																				snake_case_  :							Tuple            =	tester_class(parent=None)
															except Exception:
																				continue
															if hasattr(tester, "get_config"):
																				snake_case_  :							Any            =	tester.get_config()
																				for k, v in config.to_dict().items():
																									if isinstance(v, int):
																														snake_case_  :							int            =	None
																														if k in ["vocab_size"]:
																																			snake_case_  :							Optional[Any]            =	1_00
																														elif k in ["max_position_embeddings"]:
																																			snake_case_  :							Any            =	1_28
																														elif k in ["hidden_size", "d_model"]:
																																			snake_case_  :							str            =	40
																														elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																			snake_case_  :							str            =	5
																														if target is not None and v > target:
																																			failures.append(
																																			    f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
																																			    f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
																																			    f''' than {target}.'''
																																			)

					if len(failures) > 0:
										raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))





