



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 		_lowercase		:

   def __init__(   self      :							Any				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							str=13				, __lowerCAmelCase      :							List[Any]=7				, __lowerCAmelCase      :							Tuple=True				, __lowerCAmelCase      :							Optional[Any]=True				, __lowerCAmelCase      :							str=True				, __lowerCAmelCase      :							Optional[Any]=True				, __lowerCAmelCase      :							Tuple=99				, __lowerCAmelCase      :							Optional[int]=32				, __lowerCAmelCase      :							List[str]=5				, __lowerCAmelCase      :							Tuple=4				, __lowerCAmelCase      :							Any=37				, __lowerCAmelCase      :							Optional[int]="gelu"				, __lowerCAmelCase      :							Optional[Any]=0.1				, __lowerCAmelCase      :							Tuple=0.1				, __lowerCAmelCase      :							Tuple=512				, __lowerCAmelCase      :							int=16				, __lowerCAmelCase      :							Union[str, Any]=2				, __lowerCAmelCase      :							Tuple=0.0_2				, __lowerCAmelCase      :							int=3				, __lowerCAmelCase      :							Any=4				, __lowerCAmelCase      :							Tuple=None				, __lowerCAmelCase      :							Tuple=1000				, )							->					Tuple:


    """simple docstring"""






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

   def 		A				(   self      :							Tuple				)							->					Optional[int]:


    """simple docstring"""






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

    a							=		ids_tensor([self.batch_size, self.seq_length, 4]				, self.range_bbox				)
    # Ensure that bbox is legal
    for i in range(bbox.shape[0]				):
     for j in range(bbox.shape[1]				):
      if bbox[i, j, 3] < bbox[i, j, 1]:
       a							=		bbox[i, j, 3]
       a							=		bbox[i, j, 1]
       a							=		t
      if bbox[i, j, 2] < bbox[i, j, 0]:
       a							=		bbox[i, j, 2]
       a							=		bbox[i, j, 0]
       a							=		t

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

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

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

    a							=		self.get_config()

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

   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """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 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							Any				, __lowerCAmelCase      :							List[Any]				)							->					List[Any]:


    """simple docstring"""






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


    """simple docstring"""






    a							=		LayoutLMForMaskedLM(config=__a				)
    model.to(__a				)
    model.eval()
    a							=		model(__a				, __a				, attention_mask=__a				, token_type_ids=__a				, labels=__a				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.vocab_size)				)

   def 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							Union[str, Any]				)							->					int:


    """simple docstring"""






    a							=		self.num_labels
    a							=		LayoutLMForSequenceClassification(__a				)
    model.to(__a				)
    model.eval()
    a							=		model(
        __a				, __a				, attention_mask=__a				, token_type_ids=__a				, labels=__a				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.num_labels)				)

   def 		A				(   self      :							Any				, __lowerCAmelCase      :							Optional[int]				, __lowerCAmelCase      :							Union[str, Any]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							int				, __lowerCAmelCase      :							List[Any]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Dict				)							->					Dict:


    """simple docstring"""






    a							=		self.num_labels
    a							=		LayoutLMForTokenClassification(config=__a				)
    model.to(__a				)
    model.eval()
    a							=		model(__a				, __a				, attention_mask=__a				, token_type_ids=__a				, labels=__a				)
    self.parent.assertEqual(result.logits.shape				, (self.batch_size, self.seq_length, self.num_labels)				)

   def 		A				(   self      :							Tuple				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							Dict				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							List[str]				, __lowerCAmelCase      :							str				, __lowerCAmelCase      :							Tuple				)							->					Dict:


    """simple docstring"""






    a							=		LayoutLMForQuestionAnswering(config=__a				)
    model.to(__a				)
    model.eval()
    a							=		model(
        __a				, bbox=__a				, attention_mask=__a				, token_type_ids=__a				, start_positions=__a				, end_positions=__a				, )
    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 		A				(   self      :							Any				)							->					Any:


    """simple docstring"""






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



@require_torch
class 		_lowercase		(							UpperCamelCase_, UpperCamelCase_, unittest.TestCase						):
   _UpperCAmelCase  						=      (
       (
           LayoutLMModel,
           LayoutLMForMaskedLM,
           LayoutLMForSequenceClassification,
           LayoutLMForTokenClassification,
           LayoutLMForQuestionAnswering,
       )
       if is_torch_available()
       else None
   )
   _UpperCAmelCase  						=      (
       {
           '''document-question-answering''': LayoutLMForQuestionAnswering,
           '''feature-extraction''': LayoutLMModel,
           '''fill-mask''': LayoutLMForMaskedLM,
           '''text-classification''': LayoutLMForSequenceClassification,
           '''token-classification''': LayoutLMForTokenClassification,
           '''zero-shot''': LayoutLMForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   _UpperCAmelCase  						=      True

   def 		A				(   self      :							Any				)							->					str:


    """simple docstring"""






    a							=		LayoutLMModelTester(self				)
    a							=		ConfigTester(self				, config_class=__a				, hidden_size=37				)

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


    """simple docstring"""






    self.config_tester.run_common_tests()

   def 		A				(   self      :							List[str]				)							->					Optional[int]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__a				)

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


    """simple docstring"""






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

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


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*__a				)

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


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*__a				)

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


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*__a				)

   def 		A				(   self      :							str				)							->					Optional[int]:


    """simple docstring"""






    a							=		self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*__a				)




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


    """simple docstring"""






    pass


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

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

 return input_ids, attention_mask, bbox, token_type_ids, labels




@require_torch
class 		_lowercase		(							unittest.TestCase						):

   @slow
   def 		A				(   self      :							Optional[int]				)							->					Dict:


    """simple docstring"""






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

    a							=		prepare_layoutlm_batch_inputs()

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

    # test the sequence output on [0, :3, :3]
    a							=		torch.tensor(
        [[0.1_7_8_5, -0.1_9_4_7, -0.0_4_2_5], [-0.3_2_5_4, -0.2_8_0_7, 0.2_5_5_3], [-0.5_3_9_1, -0.3_3_2_2, 0.3_3_6_4]]				, device=__a				, )

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

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

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

   @slow
   def 		A				(   self      :							List[Any]				)							->					Tuple:


    """simple docstring"""






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

    a							=		prepare_layoutlm_batch_inputs()

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

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

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

   @slow
   def 		A				(   self      :							Dict				)							->					List[Any]:


    """simple docstring"""






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

    a							=		prepare_layoutlm_batch_inputs()

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

    # test the loss calculation to be around 2.65
    # expected_loss = torch.tensor(2.65, device=torch_device)

    # The loss is currently somewhat random and can vary between 0.1-0.3 atol.
    # self.assertTrue(torch.allclose(outputs.loss, expected_loss, atol=0.1))

    # test the shape of the logits
    a							=		outputs.logits
    a							=		torch.Size((2, 25, 13)				)
    self.assertEqual(logits.shape				, __a				)




   @slow
   def 		A				(   self      :							Optional[int]				)							->					str:


    """simple docstring"""






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

    a							=		prepare_layoutlm_batch_inputs()

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

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









'''simple docstring'''






import unittest

from transformers import YosoConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          YosoForMaskedLM,
          YosoForMultipleChoice,
          YosoForQuestionAnswering,
          YosoForSequenceClassification,
          YosoForTokenClassification,
          YosoModel,
      )
      from transformers.models.yoso.modeling_yoso import YOSO_PRETRAINED_MODEL_ARCHIVE_LIST




class a_     :







   def __init__(self       , __a       , __a=1_3       , __a=7       , __a=True       , __a=True       , __a=True       , __a=True       , __a=9_9       , __a=3_2       , __a=5       , __a=4       , __a=3_7       , __a="gelu"       , __a=0.1       , __a=0.1       , __a=5_1_2       , __a=1_6       , __a=2       , __a=0.02       , __a=3       , __a=4       , __a=None       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			parent
      __snake_case					:   Dict  				=			batch_size
      __snake_case					:   Union[str, Any]  				=			seq_length
      __snake_case					:   str  				=			is_training
      __snake_case					:   Optional[Any]  				=			use_input_mask
      __snake_case					:   Dict  				=			use_token_type_ids
      __snake_case					:   Union[str, Any]  				=			use_labels
      __snake_case					:   Optional[Any]  				=			vocab_size
      __snake_case					:   Optional[Any]  				=			hidden_size
      __snake_case					:   int  				=			num_hidden_layers
      __snake_case					:   Any  				=			num_attention_heads
      __snake_case					:   Optional[Any]  				=			intermediate_size
      __snake_case					:   List[str]  				=			hidden_act
      __snake_case					:   List[Any]  				=			hidden_dropout_prob
      __snake_case					:   Optional[Any]  				=			attention_probs_dropout_prob
      __snake_case					:   str  				=			max_position_embeddings
      __snake_case					:   str  				=			type_vocab_size
      __snake_case					:   Dict  				=			type_sequence_label_size
      __snake_case					:   int  				=			initializer_range
      __snake_case					:   Dict  				=			num_labels
      __snake_case					:   Optional[int]  				=			num_choices
      __snake_case					:   Dict  				=			scope







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size)

      __snake_case					:   Optional[Any]  				=			None
      if self.use_input_mask:
         __snake_case					:   Union[str, Any]  				=			random_attention_mask([self.batch_size, self.seq_length])

      __snake_case					:   Tuple  				=			None
      if self.use_token_type_ids:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size, self.seq_length]       , self.type_vocab_size)

      __snake_case					:   Dict  				=			None
      __snake_case					:   List[str]  				=			None
      __snake_case					:   List[str]  				=			None
      if self.use_labels:
         __snake_case					:   Union[str, Any]  				=			ids_tensor([self.batch_size]       , self.type_sequence_label_size)
         __snake_case					:   Tuple  				=			ids_tensor([self.batch_size, self.seq_length]       , self.num_labels)
         __snake_case					:   str  				=			ids_tensor([self.batch_size]       , self.num_choices)

      __snake_case					:   List[str]  				=			self.get_config()

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      return YosoConfig(
          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       , is_decoder=__a       , initializer_range=self.initializer_range       , )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.get_config()
      __snake_case					:   Tuple  				=			3_0_0
      return config







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Optional[Any]  				=			self.prepare_config_and_inputs()

      __snake_case					:   Optional[int]  				=			True
      __snake_case					:   Optional[int]  				=			floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
      __snake_case					:   Dict  				=			ids_tensor([self.batch_size, self.seq_length]       , vocab_size=2)

      return (
          config,
          input_ids,
          token_type_ids,
          input_mask,
          sequence_labels,
          token_labels,
          choice_labels,
          encoder_hidden_states,
          encoder_attention_mask,
      )







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModel(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      __snake_case					:   int  				=			model(__a       , token_type_ids=__a)
      __snake_case					:   Optional[Any]  				=			model(__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , __a       , )      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			True
      __snake_case					:   Dict  				=			YosoModel(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   List[str]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , encoder_attention_mask=__a       , )
      __snake_case					:   List[Any]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , encoder_hidden_states=__a       , )
      __snake_case					:   List[Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a)
      self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			YosoForMaskedLM(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Union[str, Any]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.vocab_size))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			YosoForQuestionAnswering(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   str  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , start_positions=__a       , end_positions=__a       , )
      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      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   int:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForSequenceClassification(__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Optional[int]  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   Dict:
      """simple docstring"""






      __snake_case					:   str  				=			self.num_labels
      __snake_case					:   Dict  				=			YosoForTokenClassification(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Tuple  				=			model(__a       , attention_mask=__a       , token_type_ids=__a       , labels=__a)
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.seq_length, self.num_labels))







   def      SCREAMING_SNAKE_CASE__       (self       , __a       , __a       , __a       , __a       , __a       , __a       , __a)      ->   str:
      """simple docstring"""






      __snake_case					:   int  				=			self.num_choices
      __snake_case					:   Tuple  				=			YosoForMultipleChoice(config=__a)
      model.to(__a)
      model.eval()
      __snake_case					:   Any  				=			input_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Tuple  				=			token_type_ids.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   List[str]  				=			input_mask.unsqueeze(1).expand(-1       , self.num_choices       , -1).contiguous()
      __snake_case					:   Optional[int]  				=			model(
          __a       , attention_mask=__a       , token_type_ids=__a       , labels=__a       , )
      self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.num_choices))







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      __snake_case					:   int  				=			self.prepare_config_and_inputs()
      (
          (
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,(
          __snake_case
      )				,
      )					:   Union[str, Any]  				=			config_and_inputs
      __snake_case					:   Optional[int]  				=			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
      return config, inputs_dict




@require_torch
class a_     (     UpperCamelCase_		,   UpperCamelCase_		,   unittest.TestCase				):
   _snake_case	   =				(
       (
           YosoModel,
           YosoForMaskedLM,
           YosoForMultipleChoice,
           YosoForQuestionAnswering,
           YosoForSequenceClassification,
           YosoForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   _snake_case	   =				False
   _snake_case	   =				False
   _snake_case	   =				False

   _snake_case	   =				()
   _snake_case	   =				(
       {
           """feature-extraction""": YosoModel,
           """fill-mask""": YosoForMaskedLM,
           """question-answering""": YosoForQuestionAnswering,
           """text-classification""": YosoForSequenceClassification,
           """token-classification""": YosoForTokenClassification,
           """zero-shot""": YosoForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoModelTester(self)
      __snake_case					:   List[Any]  				=			ConfigTester(self       , config_class=__a       , hidden_size=3_7)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Dict:
      """simple docstring"""






      self.config_tester.run_common_tests()







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
         __snake_case					:   List[Any]  				=			type
         self.model_tester.create_and_check_model(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Union[str, Any]:
      """simple docstring"""






      __snake_case					:   str  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[int]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__a)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   List[str]:
      """simple docstring"""






      for model_name in YOSO_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __snake_case					:   Tuple  				=			YosoModel.from_pretrained(__a)
         self.assertIsNotNone(__a)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Any:
      """simple docstring"""






      return




@require_torch
class a_     (     unittest.TestCase				):







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			YosoModel.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   str  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   str  				=			model(__a)[0]

      __snake_case					:   Union[str, Any]  				=			torch.Size((1, 6, 7_6_8))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Dict  				=			torch.tensor(
          [[[-0.0_611, 0.1_242, 0.0_840], [0.0_280, -0.0_048, 0.1_125], [0.0_106, 0.0_226, 0.0_751]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   int:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Tuple  				=			torch.tensor([[0, 1, 2, 3, 4, 5]])

      with torch.no_grad():
         __snake_case					:   Any  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[str]  				=			torch.Size((1, 6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   str  				=			torch.tensor(
          [[[-2.1_313, -3.7_285, -2.2_407], [-2.7_047, -3.3_314, -2.6_408], [0.0_629, -2.5_166, -0.3_356]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   Dict  				=			YosoForMaskedLM.from_pretrained('uw-madison/yoso-4096')
      __snake_case					:   Optional[Any]  				=			torch.arange(4_0_9_6).unsqueeze(0)

      with torch.no_grad():
         __snake_case					:   Dict  				=			model(__a)[0]

      __snake_case					:   List[str]  				=			5_0_2_6_5

      __snake_case					:   List[Any]  				=			torch.Size((1, 4_0_9_6, vocab_size))
      self.assertEqual(output.shape       , __a)

      __snake_case					:   Tuple  				=			torch.tensor(
          [[[-2.3_914, -4.3_742, -5.0_956], [-4.0_988, -4.2_384, -7.0_406], [-3.1_427, -3.7_192, -6.6_800]]])

      self.assertTrue(torch.allclose(output[:, :3, :3]       , __a       , atol=1E-4))