

'''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 copy
import os
from typing import Dict, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings, logging
from ..auto import AutoConfig


UpperCAmelCase				: List[Any]         =  logging.get_logger(__name__)


UpperCAmelCase				: int         =  {
    'suno/bark-small': 'https://huggingface.co/suno/bark-small/resolve/main/config.json',
    'suno/bark': 'https://huggingface.co/suno/bark/resolve/main/config.json',
}

UpperCAmelCase				: Optional[Any]         =  '\n    This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model\n    according to the specified arguments, defining the model architecture. Instantiating a configuration with the\n    defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark)\n    architecture.\n\n    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the\n    documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        block_size (`int`, *optional*, defaults to 1024):\n            The maximum sequence length that this model might ever be used with. Typically set this to something large\n            just in case (e.g., 512 or 1024 or 2048).\n        input_vocab_size (`int`, *optional*, defaults to 10_048):\n            Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the\n            `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with\n            regards to the chosen sub-model.\n        output_vocab_size (`int`, *optional*, defaults to 10_048):\n            Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented\n            by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought\n            with regards to the chosen sub-model.\n        num_layers (`int`, *optional*, defaults to 12):\n            Number of hidden layers in the given sub-model.\n        num_heads (`int`, *optional*, defaults to 12):\n            Number of attention heads for each attention layer in the Transformer architecture.\n        hidden_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture.\n        dropout (`float`, *optional*, defaults to 0.0):\n            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.\n        bias (`bool`, *optional*, defaults to `True`):\n            Whether or not to use bias in the linear layers and layer norm layers.\n        initializer_range (`float`, *optional*, defaults to 0.02):\n            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n'







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark_module"
 lowerCAmelCase__						      =   ["past_key_values"]

 lowerCAmelCase__						      =   {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
     "vocab_size": "input_vocab_size",
     "window_size": "block_size",
 }
 def __init__(	self      : Union[str, Any]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=1_024      ,							__SCREAMING_SNAKE_CASE      : Dict=10_048      ,							__SCREAMING_SNAKE_CASE      : Union[str, Any]=10_048      ,							__SCREAMING_SNAKE_CASE      : Tuple=12      ,							__SCREAMING_SNAKE_CASE      : Dict=12      ,							__SCREAMING_SNAKE_CASE      : List[str]=768      ,							__SCREAMING_SNAKE_CASE      : Tuple=0.0      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Dict=0.02      ,							__SCREAMING_SNAKE_CASE      : Optional[Any]=True      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   block_size
      __SCREAMING_SNAKE_CASE						     =   input_vocab_size
      __SCREAMING_SNAKE_CASE						     =   output_vocab_size
      __SCREAMING_SNAKE_CASE						     =   num_layers
      __SCREAMING_SNAKE_CASE						     =   num_heads
      __SCREAMING_SNAKE_CASE						     =   hidden_size
      __SCREAMING_SNAKE_CASE						     =   dropout
      __SCREAMING_SNAKE_CASE						     =   bias
      __SCREAMING_SNAKE_CASE						     =   use_cache
      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )


 @classmethod
 def    UpperCAmelCase__	(	cls      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Union[str, os.PathLike]      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, os.PathLike]] = None      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : bool = False      ,							__SCREAMING_SNAKE_CASE      : Optional[Union[str, bool]] = None      ,							__SCREAMING_SNAKE_CASE      : str = "main"      ,							**__SCREAMING_SNAKE_CASE      : Tuple      ,							)						->				"PretrainedConfig":


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   cache_dir
      __SCREAMING_SNAKE_CASE						     =   force_download
      __SCREAMING_SNAKE_CASE						     =   local_files_only
      __SCREAMING_SNAKE_CASE						     =   revision

      cls._set_token_in_kwargs(__SCREAMING_SNAKE_CASE      ,							__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   cls.get_config_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )

      # get the config dict if we are loading from Bark
      if config_dict.get("""model_type"""    ) == "bark":
           __SCREAMING_SNAKE_CASE						     =   config_dict[f'{cls.model_type}_config']

      if "model_type" in config_dict and hasattr(cls      ,							"""model_type"""    ) and config_dict["model_type"] != cls.model_type:
           logger.warning(
               f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
               f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

      return cls.from_dict(__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkSemanticConfig"				,      model="BarkSemanticModel"      )				,      "\n    Example:\n\n    ```python\n    >>> from transformers import BarkSemanticConfig, BarkSemanticModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkSemanticConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkSemanticModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "semantic"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkCoarseConfig"				,      model="BarkCoarseModel"      )				,      "\n    Example:\n\n    ```python\n    >>> from transformers import BarkCoarseConfig, BarkCoarseModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkCoarseConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkCoarseModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "coarse_acoustics"







@add_start_docstrings(
    BARK_SUBMODELCONFIG_START_DOCSTRING.format(config="BarkFineConfig"				,      model="BarkFineModel"      )				,      "\n        n_codes_total (`int`, *optional*, defaults to 8):\n            The total number of audio codebooks predicted. Used in the fine acoustics sub-model.\n        n_codes_given (`int`, *optional*, defaults to 1):\n            The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics\n            sub-models.\n    Example:\n\n    ```python\n    >>> from transformers import BarkFineConfig, BarkFineModel\n\n    >>> # Initializing a Bark sub-module style configuration\n    >>> configuration = BarkFineConfig()\n\n    >>> # Initializing a model (with random weights) from the suno/bark style configuration\n    >>> model = BarkFineModel(configuration)\n\n    >>> # Accessing the model configuration\n    >>> configuration = model.config\n    ```"				,      )
class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "fine_acoustics"
 def __init__(	self      : List[str]      ,							__SCREAMING_SNAKE_CASE      : List[Any]=True      ,							__SCREAMING_SNAKE_CASE      : Tuple=8      ,							__SCREAMING_SNAKE_CASE      : Tuple=1      ,							**__SCREAMING_SNAKE_CASE      : Any    )						->				List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   n_codes_total
      __SCREAMING_SNAKE_CASE						     =   n_codes_given

      super().__init__(tie_word_embeddings=__SCREAMING_SNAKE_CASE      ,							**__SCREAMING_SNAKE_CASE    )







class 		lowerCAmelCase__     (   a      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "bark"
 lowerCAmelCase__						      =   True
 def __init__(	self      : Optional[int]      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : Dict = None      ,							__SCREAMING_SNAKE_CASE      : List[Any]=0.02      ,							**__SCREAMING_SNAKE_CASE      : List[str]      ,							)						->				Union[str, Any]:


      """simple docstring"""




      if semantic_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""semantic_config is None. initializing the semantic model with default values."""    )

      if coarse_acoustics_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""    )

      if fine_acoustics_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""    )

      if codec_config is None:
           __SCREAMING_SNAKE_CASE						     =   {}
           logger.info("""codec_config is None. initializing the codec model with default values."""    )

      __SCREAMING_SNAKE_CASE						     =   BarkSemanticConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkCoarseConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   BarkFineConfig(**__SCREAMING_SNAKE_CASE    )
      __SCREAMING_SNAKE_CASE						     =   AutoConfig.for_model(**__SCREAMING_SNAKE_CASE    )

      __SCREAMING_SNAKE_CASE						     =   initializer_range

      super().__init__(**__SCREAMING_SNAKE_CASE    )
 @classmethod
 def    UpperCAmelCase__	(	cls      : str      ,							__SCREAMING_SNAKE_CASE      : BarkSemanticConfig      ,							__SCREAMING_SNAKE_CASE      : BarkCoarseConfig      ,							__SCREAMING_SNAKE_CASE      : BarkFineConfig      ,							__SCREAMING_SNAKE_CASE      : AutoConfig      ,							**__SCREAMING_SNAKE_CASE      : int      ,							)						->				Optional[int]:


      """simple docstring"""




      return cls(
          semantic_config=semantic_config.to_dict()      ,							coarse_acoustics_config=coarse_acoustics_config.to_dict()      ,							fine_acoustics_config=fine_acoustics_config.to_dict()      ,							codec_config=codec_config.to_dict()      ,							**__SCREAMING_SNAKE_CASE      ,							)


 def    UpperCAmelCase__	(	self      : Optional[Any]    )						->				List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   copy.deepcopy(self.__dict__    )

      __SCREAMING_SNAKE_CASE						     =   self.semantic_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.coarse_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.fine_acoustics_config.to_dict()
      __SCREAMING_SNAKE_CASE						     =   self.codec_config.to_dict()

      __SCREAMING_SNAKE_CASE						     =   self.__class__.model_type
      return output






