

'''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 		lowerCAmelCase__     :



 """simple docstring"""



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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   parent
      __SCREAMING_SNAKE_CASE						     =   batch_size
      __SCREAMING_SNAKE_CASE						     =   seq_length
      __SCREAMING_SNAKE_CASE						     =   is_training
      __SCREAMING_SNAKE_CASE						     =   use_input_mask
      __SCREAMING_SNAKE_CASE						     =   use_token_type_ids
      __SCREAMING_SNAKE_CASE						     =   use_labels
      __SCREAMING_SNAKE_CASE						     =   vocab_size
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   num_hidden_layers
      __SCREAMING_SNAKE_CASE						     =   num_attention_heads
      __SCREAMING_SNAKE_CASE						     =   intermediate_size
      __SCREAMING_SNAKE_CASE						     =   hidden_act
      __SCREAMING_SNAKE_CASE						     =   hidden_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE						     =   max_position_embeddings
      __SCREAMING_SNAKE_CASE						     =   type_vocab_size
      __SCREAMING_SNAKE_CASE						     =   type_sequence_label_size
      __SCREAMING_SNAKE_CASE						     =   initializer_range
      __SCREAMING_SNAKE_CASE						     =   num_labels
      __SCREAMING_SNAKE_CASE						     =   num_choices
      __SCREAMING_SNAKE_CASE						     =   scope
      __SCREAMING_SNAKE_CASE						     =   range_bbox
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				Optional[int]:


      """simple docstring"""




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

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

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

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

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

      __SCREAMING_SNAKE_CASE						     =   self.get_config()

      return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
 def    UpperCAmelCase__	(	self      : Any    )						->				Dict:


      """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    UpperCAmelCase__	(	self      : str      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   LayoutLMModel(config=__SCREAMING_SNAKE_CASE    )
      model.to(__SCREAMING_SNAKE_CASE    )
      model.eval()
      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							attention_mask=__SCREAMING_SNAKE_CASE      ,							token_type_ids=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							token_type_ids=__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )
      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      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : int      ,							__SCREAMING_SNAKE_CASE      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   LayoutLMForMaskedLM(config=__SCREAMING_SNAKE_CASE    )
      model.to(__SCREAMING_SNAKE_CASE    )
      model.eval()
      __SCREAMING_SNAKE_CASE						     =   model(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							attention_mask=__SCREAMING_SNAKE_CASE      ,							token_type_ids=__SCREAMING_SNAKE_CASE      ,							labels=__SCREAMING_SNAKE_CASE    )
      self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)    )
 def    UpperCAmelCase__	(	self      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Dict      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : Any      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : str      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.num_labels
      __SCREAMING_SNAKE_CASE						     =   LayoutLMForSequenceClassification(__SCREAMING_SNAKE_CASE    )
      model.to(__SCREAMING_SNAKE_CASE    )
      model.eval()
      __SCREAMING_SNAKE_CASE						     =   model(
          __SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE      ,							attention_mask=__SCREAMING_SNAKE_CASE      ,							token_type_ids=__SCREAMING_SNAKE_CASE      ,							labels=__SCREAMING_SNAKE_CASE    )
      self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.num_labels)    )
 def    UpperCAmelCase__	(	self      : int      ,							__SCREAMING_SNAKE_CASE      : Tuple      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]      ,							__SCREAMING_SNAKE_CASE      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]    )						->				Union[str, Any]:


      """simple docstring"""




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


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   LayoutLMForQuestionAnswering(config=__SCREAMING_SNAKE_CASE    )
      model.to(__SCREAMING_SNAKE_CASE    )
      model.eval()
      __SCREAMING_SNAKE_CASE						     =   model(
          __SCREAMING_SNAKE_CASE      ,							bbox=__SCREAMING_SNAKE_CASE      ,							attention_mask=__SCREAMING_SNAKE_CASE      ,							token_type_ids=__SCREAMING_SNAKE_CASE      ,							start_positions=__SCREAMING_SNAKE_CASE      ,							end_positions=__SCREAMING_SNAKE_CASE      ,							)
      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      : int    )						->				List[str]:


      """simple docstring"""




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







@require_torch
class 		lowerCAmelCase__     (   a				,      a				,      unittest.TestCase      ):



 """simple docstring"""



 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]    )						->				int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   LayoutLMModelTester(self    )
      __SCREAMING_SNAKE_CASE						     =   ConfigTester(self      ,							config_class=__SCREAMING_SNAKE_CASE      ,							hidden_size=37    )
 def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


      """simple docstring"""




      self.config_tester.run_common_tests()
 def    UpperCAmelCase__	(	self      : Dict    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
           __SCREAMING_SNAKE_CASE						     =   type
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : int    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE    )


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


      """simple docstring"""




      pass







def      a__ (						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE						     =   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
     __SCREAMING_SNAKE_CASE						     =   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
     __SCREAMING_SNAKE_CASE						     =   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
     __SCREAMING_SNAKE_CASE						     =   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)
     __SCREAMING_SNAKE_CASE						     =   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 		lowerCAmelCase__     (   unittest.TestCase      ):



 """simple docstring"""



 @slow
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				List[Any]:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   prepare_layoutlm_batch_inputs()

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

      # test the sequence output on [0, :3, :3]
      __SCREAMING_SNAKE_CASE						     =   torch.tensor(
          [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]]      ,							device=__SCREAMING_SNAKE_CASE      ,							)

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

      # test the pooled output on [1, :3]
      __SCREAMING_SNAKE_CASE						     =   torch.tensor([-0.6580, -0.0214, 0.8552]      ,							device=__SCREAMING_SNAKE_CASE    )

      self.assertTrue(torch.allclose(outputs.pooler_output[1, :3]      ,							__SCREAMING_SNAKE_CASE      ,							atol=1E-3    )    )
 @slow
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Any:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   prepare_layoutlm_batch_inputs()

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

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

      # test the shape of the logits
      __SCREAMING_SNAKE_CASE						     =   outputs.logits
      __SCREAMING_SNAKE_CASE						     =   torch.Size((2, 2)    )
      self.assertEqual(logits.shape      ,							__SCREAMING_SNAKE_CASE    )
 @slow
 def    UpperCAmelCase__	(	self      : List[str]    )						->				Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   LayoutLMForTokenClassification.from_pretrained("""microsoft/layoutlm-base-uncased"""      ,							num_labels=13    ).to(
          __SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   prepare_layoutlm_batch_inputs()

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

      # 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						     =   outputs.logits
      __SCREAMING_SNAKE_CASE						     =   torch.Size((2, 25, 13)    )
      self.assertEqual(logits.shape      ,							__SCREAMING_SNAKE_CASE    )


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


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   prepare_layoutlm_batch_inputs()

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

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









'''simple docstring'''



import inspect
import warnings

import torch

from .state import AcceleratorState, GradientState
from .utils import DistributedType, honor_type, is_tpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm







def      a__ (						a__   ,					a__		):




     """simple docstring"""


     if isinstance(a__   ,					(list, tuple)		):
          return honor_type(a__   ,					(move_to_device(a__   ,					a__		) for t in state)		)
     elif isinstance(a__   ,					a__		):
          return type(a__		)({k: move_to_device(a__   ,					a__		) for k, v in state.items()}		)
     elif isinstance(a__   ,					torch.Tensor		):
          return state.to(a__		)
     return state







class 		lowerCAmelCase__     (   torch.optim.Optimizer      ):



 """simple docstring"""



 def __init__(	self      : Tuple      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Dict=True      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   optimizer
      __SCREAMING_SNAKE_CASE						     =   scaler
      __SCREAMING_SNAKE_CASE						     =   AcceleratorState()
      __SCREAMING_SNAKE_CASE						     =   GradientState()
      __SCREAMING_SNAKE_CASE						     =   device_placement
      __SCREAMING_SNAKE_CASE						     =   False
      __SCREAMING_SNAKE_CASE						     =   None

      # Handle device placement
      if device_placement:
           __SCREAMING_SNAKE_CASE						     =   self.optimizer.state_dict()
           if self.accelerator_state.distributed_type == DistributedType.TPU:
                xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
           else:
                __SCREAMING_SNAKE_CASE						     =   move_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
           self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
 @property
 def    UpperCAmelCase__	(	self      : str    )						->				Union[str, Any]:


      """simple docstring"""




      return self.optimizer.state
 @state.setter
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   state
 @property
 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				str:


      """simple docstring"""




      return self.optimizer.param_groups
 @param_groups.setter
 def    UpperCAmelCase__	(	self      : Dict      ,							__SCREAMING_SNAKE_CASE      : str    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   param_groups
 @property
 def    UpperCAmelCase__	(	self      : List[Any]    )						->				Any:


      """simple docstring"""




      return self.optimizer.defaults
 @defaults.setter
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Dict    )						->				Optional[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   defaults
 def    UpperCAmelCase__	(	self      : Any      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				List[str]:


      """simple docstring"""




      self.optimizer.add_param_group(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]    )						->				Dict:


      """simple docstring"""




      if self.accelerator_state.distributed_type == DistributedType.TPU and self.device_placement:
           xm.send_cpu_data_to_device(__SCREAMING_SNAKE_CASE      ,							self.accelerator_state.device    )
      self.optimizer.load_state_dict(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Union[str, Any]:


      """simple docstring"""




      return self.optimizer.state_dict()
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[str]=None    )						->				Any:


      """simple docstring"""




      if self.gradient_state.sync_gradients:
           __SCREAMING_SNAKE_CASE						     =   """set_to_none""" in inspect.signature(self.optimizer.zero_grad    ).parameters
           if accept_arg:
                if set_to_none is None:
                     __SCREAMING_SNAKE_CASE						     =   False
                self.optimizer.zero_grad(set_to_none=__SCREAMING_SNAKE_CASE    )
           else:
                if set_to_none is not None:
                     raise ValueError("""`set_to_none` for Optimizer.zero_grad` is not supported by this optimizer."""    )
                self.optimizer.zero_grad()
 def    UpperCAmelCase__	(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=None    )						->				Dict:


      """simple docstring"""




      if self.gradient_state.sync_gradients:
           if self.accelerator_state.distributed_type == DistributedType.TPU:
                __SCREAMING_SNAKE_CASE						     =   {"""closure""": closure} if closure is not None else {}
                xm.optimizer_step(self.optimizer      ,							optimizer_args=__SCREAMING_SNAKE_CASE    )
           elif self.scaler is not None:
                __SCREAMING_SNAKE_CASE						     =   False
                if self._last_scale is None:
                     # `get_scale` is an async operation requiring full synchronization
                     # on CPU and GPUs before finishing. As a result, we store away
                     # the prior one to reduce the call overhead
                     __SCREAMING_SNAKE_CASE						     =   self.scaler.get_scale()
                     __SCREAMING_SNAKE_CASE						     =   True
                self.scaler.step(self.optimizer      ,							__SCREAMING_SNAKE_CASE    )
                self.scaler.update()
                __SCREAMING_SNAKE_CASE						     =   self.scaler.get_scale()
                if not new_scale:
                     # If we reduced the loss scale, it means the optimizer step was skipped because of gradient overflow.
                     __SCREAMING_SNAKE_CASE						     =   scale_after < self._last_scale
                __SCREAMING_SNAKE_CASE						     =   scale_after

           else:
                self.optimizer.step(__SCREAMING_SNAKE_CASE    )
 def    UpperCAmelCase__	(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : Any    )						->				Any:


      """simple docstring"""




      for param_group in self.optimizer.param_groups:
           __SCREAMING_SNAKE_CASE						     =   [parameters_map.get(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    ) for p in param_group["""params"""]]
 @property
 def    UpperCAmelCase__	(	self      : int    )						->				Any:


      """simple docstring"""




      warnings.warn(
          """The `is_overflow` property is deprecated and will be removed in version 1.0 of Accelerate use """
          """`optimizer.step_was_skipped` instead."""      ,							__SCREAMING_SNAKE_CASE      ,							)
      return self._is_overflow
 @property
 def    UpperCAmelCase__	(	self      : Union[str, Any]    )						->				Optional[int]:


      """simple docstring"""




      return self._is_overflow
 def __getstate__(	self      : Tuple    )						->				str:


      """simple docstring"""




      return self.__dict__.copy()


 def __setstate__(	self      : str      ,							__SCREAMING_SNAKE_CASE      : Optional[int]    )						->				Any:


      """simple docstring"""




      self.__dict__.update(__SCREAMING_SNAKE_CASE    )






