
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 							UpperCAmelCase   :




 '''simple docstring'''





 def __init__( self      :				List[Any]							,A      :				Tuple							,A      :				str=13							,A      :				Dict=7							,A      :				Any=True							,A      :				Dict=True							,A      :				str=True							,A      :				str=True							,A      :				List[str]=99							,A      :				Dict=32							,A      :				Optional[int]=5							,A      :				Optional[Any]=4							,A      :				List[str]=37							,A      :				List[str]="gelu"							,A      :				Optional[int]=0.1							,A      :				Dict=0.1							,A      :				Optional[Any]=5_12							,A      :				str=16							,A      :				Dict=2							,A      :				Dict=0.02							,A      :				List[str]=3							,A      :				Dict=4							,A      :				Any=None							,A      :				Union[str, Any]=10_00							,):
     __A         =				parent
     __A         =				batch_size
     __A         =				seq_length
     __A         =				is_training
     __A         =				use_input_mask
     __A         =				use_token_type_ids
     __A         =				use_labels
     __A         =				vocab_size
     __A         =				hidden_size
     __A         =				num_hidden_layers
     __A         =				num_attention_heads
     __A         =				intermediate_size
     __A         =				hidden_act
     __A         =				hidden_dropout_prob
     __A         =				attention_probs_dropout_prob
     __A         =				max_position_embeddings
     __A         =				type_vocab_size
     __A         =				type_sequence_label_size
     __A         =				initializer_range
     __A         =				num_labels
     __A         =				num_choices
     __A         =				scope
     __A         =				range_bbox






 def        UpperCamelCase_						( self      :				Tuple				):
     __A         =				ids_tensor([self.batch_size, self.seq_length]							,self.vocab_size				)

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

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

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

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

     __A         =				self.get_config()

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






 def        UpperCamelCase_						( self      :				Tuple				):
     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      :				int							,A      :				Optional[int]							,A      :				List[str]							,A      :				int							,A      :				Union[str, Any]							,A      :				str							,A      :				Tuple							,A      :				int							,A      :				Dict				):
     __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        UpperCamelCase_						( self      :				Dict							,A      :				Tuple							,A      :				List[Any]							,A      :				Optional[Any]							,A      :				Any							,A      :				Dict							,A      :				Tuple							,A      :				Dict							,A      :				Union[str, Any]				):
     __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        UpperCamelCase_						( self      :				Any							,A      :				str							,A      :				Optional[int]							,A      :				int							,A      :				Union[str, Any]							,A      :				List[str]							,A      :				Dict							,A      :				Optional[int]							,A      :				Tuple				):
     __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        UpperCamelCase_						( self      :				Dict							,A      :				str							,A      :				List[str]							,A      :				str							,A      :				List[str]							,A      :				str							,A      :				int							,A      :				Dict							,A      :				Union[str, Any]				):
     __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        UpperCamelCase_						( self      :				Optional[Any]							,A      :				List[Any]							,A      :				Optional[Any]							,A      :				int							,A      :				Dict							,A      :				List[str]							,A      :				List[Any]							,A      :				Optional[int]							,A      :				str				):
     __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        UpperCamelCase_						( self      :				Optional[Any]				):
     __A         =				self.prepare_config_and_inputs()
     (
         (
         __A							
     )    ,							(
         __A							
     )    ,							(
         __A							
     )    ,							(
         __A							
     )    ,							(
         __A							
     )    ,							(
         __A							
     )    ,							(
         __A							
     )    ,							(
         __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 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    unittest.TestCase						):




 '''simple docstring'''





 snake_case_      					=  (
     (
         LayoutLMModel,
         LayoutLMForMaskedLM,
         LayoutLMForSequenceClassification,
         LayoutLMForTokenClassification,
         LayoutLMForQuestionAnswering,
     )
     if is_torch_available()
     else None
 )
 snake_case_      					=  (
     {
         "document-question-answering": LayoutLMForQuestionAnswering,
         "feature-extraction": LayoutLMModel,
         "fill-mask": LayoutLMForMaskedLM,
         "text-classification": LayoutLMForSequenceClassification,
         "token-classification": LayoutLMForTokenClassification,
         "zero-shot": LayoutLMForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 snake_case_      					=  True






 def        UpperCamelCase_						( self      :				List[Any]				):
     __A         =				LayoutLMModelTester(self				)
     __A         =				ConfigTester(self							,config_class=A							,hidden_size=37				)






 def        UpperCamelCase_						( self      :				Union[str, Any]				):
     self.config_tester.run_common_tests()






 def        UpperCamelCase_						( self      :				Union[str, Any]				):
     __A         =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*A				)






 def        UpperCamelCase_						( self      :				Any				):
     __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        UpperCamelCase_						( self      :				Any				):
     __A         =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_masked_lm(*A				)






 def        UpperCamelCase_						( self      :				Optional[Any]				):
     __A         =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*A				)






 def        UpperCamelCase_						( self      :				Optional[int]				):
     __A         =				self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*A				)






 def        UpperCamelCase_						( self      :				int				):
     __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        UpperCamelCase_						( self      :				Union[str, Any]				):
     pass





def 		UpperCAmelCase    (			)	->			str:







    """simple docstring"""



    __A         =				torch.tensor([[1_0_1,1_0_1_9,1_0_1_4,1_0_1_6,1_0_3_7,1_2_8_4_9,4_7_4_7,1_0_0_4,1_4_2_4_6,2_2_7_8,5_4_3_9,4_5_2_4,5_0_0_2,2_9_3_0,2_1_9_3,2_9_3_0,4_3_4_1,3_2_0_8,1_0_0_5,1_0_5_5,2_1_7_1,2_8_4_8,1_1_3_0_0,3_5_3_1,1_0_2],[1_0_1,4_0_7_0,4_0_3_4,7_0_2_0,1_0_2_4,3_0_5_8,1_0_1_5,1_0_1_3,2_8_6_1,1_0_1_3,6_0_7_0,1_9_2_7_4,2_7_7_2,6_2_0_5,2_7_8_1_4,1_6_1_4_7,1_6_1_4_7,4_3_4_3,2_0_4_7,1_0_2_8_3,1_0_9_6_9,1_4_3_8_9,1_0_1_2,2_3_3_8,1_0_2]]	,     device=a_		)  # noqa: E231
    __A         =				torch.tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]	,     device=a_		)  # noqa: E231
    __A         =				torch.tensor([[[0,0,0,0],[4_2_3,2_3_7,4_4_0,2_5_1],[4_2_7,2_7_2,4_4_1,2_8_7],[4_1_9,1_1_5,4_3_7,1_2_9],[9_6_1,8_8_5,9_9_2,9_1_2],[2_5_6,3_8,3_3_0,5_8],[2_5_6,3_8,3_3_0,5_8],[3_3_6,4_2,3_5_3,5_7],[3_6_0,3_9,4_0_1,5_6],[3_6_0,3_9,4_0_1,5_6],[4_1_1,3_9,4_7_1,5_9],[4_7_9,4_1,5_2_8,5_9],[5_3_3,3_9,6_3_0,6_0],[6_7,1_1_3,1_3_4,1_3_1],[1_4_1,1_1_5,2_0_9,1_3_2],[6_8,1_4_9,1_3_3,1_6_6],[1_4_1,1_4_9,1_8_7,1_6_4],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[1_9_5,1_4_8,2_8_7,1_6_5],[2_9_5,1_4_8,3_4_9,1_6_5],[4_4_1,1_4_9,4_9_2,1_6_6],[4_9_7,1_4_9,5_4_6,1_6_4],[6_4,2_0_1,1_2_5,2_1_8],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]],[[0,0,0,0],[6_6_2,1_5_0,7_5_4,1_6_6],[6_6_5,1_9_9,7_4_2,2_1_1],[5_1_9,2_1_3,5_5_4,2_2_8],[5_1_9,2_1_3,5_5_4,2_2_8],[1_3_4,4_3_3,1_8_7,4_5_4],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[1_3_0,4_6_7,2_0_4,4_8_0],[3_1_4,4_6_9,3_7_6,4_8_2],[5_0_4,6_8_4,5_8_2,7_0_6],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[9_4_1,8_2_5,9_7_3,9_0_0],[6_1_0,7_4_9,6_5_2,7_6_5],[1_3_0,6_5_9,1_6_8,6_7_2],[1_7_6,6_5_7,2_3_7,6_7_2],[2_3_8,6_5_7,3_1_2,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[4_4_3,6_5_3,6_2_8,6_7_2],[7_1_6,3_0_1,8_2_5,3_1_7],[1_0_0_0,1_0_0_0,1_0_0_0,1_0_0_0]]]	,     device=a_		)  # noqa: E231
    __A         =				torch.tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]	,     device=a_		)  # noqa: E231
    # these are sequence labels (i.e. at the token level)
    __A         =				torch.tensor([[-1_0_0,1_0,1_0,1_0,9,1,-1_0_0,7,7,-1_0_0,7,7,4,2,5,2,8,8,-1_0_0,-1_0_0,5,0,3,2,-1_0_0],[-1_0_0,1_2,1_2,1_2,-1_0_0,1_2,1_0,-1_0_0,-1_0_0,-1_0_0,-1_0_0,1_0,1_2,9,-1_0_0,-1_0_0,-1_0_0,1_0,1_0,1_0,9,1_2,-1_0_0,1_0,-1_0_0]]	,     device=a_		)  # noqa: E231
    # fmt: on

    return input_ids, attention_mask, bbox, token_type_ids, labels





@require_torch
class 							UpperCAmelCase   (						unittest.TestCase						):




 '''simple docstring'''





 @slow
 def        UpperCamelCase_						( self      :				Optional[int]				):
     __A         =				LayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased"				).to(A				)

     __A    ,							__A    ,							__A    ,							__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(
         [[0.17_85, -0.19_47, -0.04_25], [-0.32_54, -0.28_07, 0.25_53], [-0.53_91, -0.33_22, 0.33_64]]							,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([-0.65_80, -0.02_14, 0.85_52]							,device=A				)

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






 @slow
 def        UpperCamelCase_						( self      :				Union[str, Any]				):
     # initialize model with randomly initialized sequence classification head
     __A         =				LayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased"							,num_labels=2				).to(
         A				)

     __A    ,							__A    ,							__A    ,							__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        UpperCamelCase_						( self      :				Optional[Any]				):
     # initialize model with randomly initialized token classification head
     __A         =				LayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased"							,num_labels=13				).to(
         A				)

     __A    ,							__A    ,							__A    ,							__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        UpperCamelCase_						( self      :				Union[str, Any]				):
     # initialize model with randomly initialized token classification head
     __A         =				LayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased"				).to(A				)

     __A    ,							__A    ,							__A    ,							__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				)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE							:str											=       {
    'configuration_deberta_v2': ['DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaV2Config', 'DebertaV2OnnxConfig'],
    'tokenization_deberta_v2': ['DebertaV2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int											=       ['DebertaV2TokenizerFast']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFDebertaV2ForMaskedLM',
					    'TFDebertaV2ForQuestionAnswering',
					    'TFDebertaV2ForSequenceClassification',
					    'TFDebertaV2ForTokenClassification',
					    'TFDebertaV2Model',
					    'TFDebertaV2PreTrainedModel',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any											=       [
					    'DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaV2ForMaskedLM',
					    'DebertaV2ForMultipleChoice',
					    'DebertaV2ForQuestionAnswering',
					    'DebertaV2ForSequenceClassification',
					    'DebertaV2ForTokenClassification',
					    'DebertaV2Model',
					    'DebertaV2PreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_deberta_va import (
					    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    DebertaVaConfig,
					    DebertaVaOnnxConfig,
					)
					from .tokenization_deberta_va import DebertaVaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta_va import (
										    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaVaForMaskedLM,
										    TFDebertaVaForQuestionAnswering,
										    TFDebertaVaForSequenceClassification,
										    TFDebertaVaForTokenClassification,
										    TFDebertaVaModel,
										    TFDebertaVaPreTrainedModel,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta_va import (
										    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaVaForMaskedLM,
										    DebertaVaForMultipleChoice,
										    DebertaVaForQuestionAnswering,
										    DebertaVaForSequenceClassification,
										    DebertaVaForTokenClassification,
										    DebertaVaModel,
										    DebertaVaPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:List[Any]											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
