





import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
        DebertaForMaskedLM,
        DebertaForQuestionAnswering,
        DebertaForSequenceClassification,
        DebertaForTokenClassification,
        DebertaModel,
    )
    from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST

class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase  ):


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





         """simple docstring"""



         __snake_case							       =   parent
         __snake_case							       =   batch_size
         __snake_case							       =   seq_length
         __snake_case							       =   is_training
         __snake_case							       =   use_input_mask
         __snake_case							       =   use_token_type_ids
         __snake_case							       =   use_labels
         __snake_case							       =   vocab_size
         __snake_case							       =   hidden_size
         __snake_case							       =   num_hidden_layers
         __snake_case							       =   num_attention_heads
         __snake_case							       =   intermediate_size
         __snake_case							       =   hidden_act
         __snake_case							       =   hidden_dropout_prob
         __snake_case							       =   attention_probs_dropout_prob
         __snake_case							       =   max_position_embeddings
         __snake_case							       =   type_vocab_size
         __snake_case							       =   type_sequence_label_size
         __snake_case							       =   initializer_range
         __snake_case							       =   num_labels
         __snake_case							       =   num_choices
         __snake_case							       =   relative_attention
         __snake_case							       =   position_biased_input
         __snake_case							       =   pos_att_type
         __snake_case							       =   scope


      def    a (self       :      List[str]      ):





         """simple docstring"""



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

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

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

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

         __snake_case							       =   self.get_config()

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


      def    a (self       :      str      ):





         """simple docstring"""



         return DebertaConfig(
             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				,			relative_attention=self.relative_attention				,			position_biased_input=self.position_biased_input				,			pos_att_type=self.pos_att_type				,			)


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.get_config()
         __snake_case							       =   300
         return config


      def    a (self       :      str				,			a__       :      Optional[Any]      ):





         """simple docstring"""



         self.parent.assertListEqual(list(result.loss.size()      )				,			[]      )


      def    a (self       :      Optional[Any]				,			a__       :      Tuple				,			a__       :      Optional[int]				,			a__       :      List[Any]				,			a__       :      List[str]				,			a__       :      List[Any]				,			a__       :      int				,			a__       :      Any      ):





         """simple docstring"""



         __snake_case							       =   DebertaModel(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			attention_mask=a__				,			token_type_ids=a__      )[0]
         __snake_case							       =   model(a__				,			token_type_ids=a__      )[0]
         __snake_case							       =   model(a__      )[0]

         self.parent.assertListEqual(list(sequence_output.size()      )				,			[self.batch_size, self.seq_length, self.hidden_size]      )


      def    a (self       :      Tuple				,			a__       :      List[str]				,			a__       :      Optional[Any]				,			a__       :      Any				,			a__       :      int				,			a__       :      List[str]				,			a__       :      Tuple				,			a__       :      List[str]      ):





         """simple docstring"""



         __snake_case							       =   DebertaForMaskedLM(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      Optional[int]				,			a__       :      Optional[Any]				,			a__       :      List[str]				,			a__       :      Any				,			a__       :      int				,			a__       :      Optional[Any]				,			a__       :      List[Any]				,			a__       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   DebertaForSequenceClassification(a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   model(a__				,			attention_mask=a__				,			token_type_ids=a__				,			labels=a__      )
         self.parent.assertListEqual(list(result.logits.size()      )				,			[self.batch_size, self.num_labels]      )
         self.check_loss_output(a__      )


      def    a (self       :      List[str]				,			a__       :      Any				,			a__       :      Union[str, Any]				,			a__       :      List[Any]				,			a__       :      Tuple				,			a__       :      str				,			a__       :      Any				,			a__       :      List[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.num_labels
         __snake_case							       =   DebertaForTokenClassification(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      Tuple				,			a__       :      Dict				,			a__       :      Optional[int]				,			a__       :      int				,			a__       :      List[str]				,			a__       :      List[Any]				,			a__       :      Optional[Any]				,			a__       :      str      ):





         """simple docstring"""



         __snake_case							       =   DebertaForQuestionAnswering(config=a__      )
         model.to(a__      )
         model.eval()
         __snake_case							       =   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    a (self       :      Union[str, Any]      ):





         """simple docstring"""



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






@require_torch
class 		SCREAMING_SNAKE_CASE__				(       _UpperCAmelCase						,      _UpperCAmelCase						,      unittest.TestCase  ):
      A_						:				Optional[Any]							=    (
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      A_						:				Union[str, Any]							=    (
          {
              'feature-extraction': DebertaModel,
              'fill-mask': DebertaForMaskedLM,
              'question-answering': DebertaForQuestionAnswering,
              'text-classification': DebertaForSequenceClassification,
              'token-classification': DebertaForTokenClassification,
              'zero-shot': DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      A_						:				Tuple							=    True
      A_						:				str							=    False
      A_						:				Optional[Any]							=    False
      A_						:				List[str]							=    False
      A_						:				Optional[int]							=    False


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   DebertaModelTester(self      )
         __snake_case							       =   ConfigTester(self				,			config_class=a__				,			hidden_size=37      )


      def    a (self       :      List[str]      ):





         """simple docstring"""



         self.config_tester.run_common_tests()


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_model(*a__      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_sequence_classification(*a__      )


      def    a (self       :      Optional[Any]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_masked_lm(*a__      )


      def    a (self       :      Optional[int]      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_question_answering(*a__      )


      def    a (self       :      Dict      ):





         """simple docstring"""



         __snake_case							       =   self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_deberta_for_token_classification(*a__      )


      @slow
      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            __snake_case							       =   DebertaModel.from_pretrained(a__      )
            self.assertIsNotNone(a__      )






@require_torch
@require_sentencepiece
@require_tokenizers
class 		SCREAMING_SNAKE_CASE__				(       unittest.TestCase  ):


      @unittest.skip(reason='''Model not available yet'''      )
      def    a (self       :      int      ):





         """simple docstring"""



         pass


      @slow
      def    a (self       :      Union[str, Any]      ):





         """simple docstring"""



         __snake_case							       =   DebertaModel.from_pretrained('''microsoft/deberta-base'''      )

         __snake_case							       =   torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
         __snake_case							       =   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
         with torch.no_grad():
            __snake_case							       =   model(a__				,			attention_mask=a__      )[0]
         # compare the actual values for a slice.
         __snake_case							       =   torch.tensor(
             [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]      )
         self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,			a__				,			atol=1E-4      )				,			f"""{output[:, 1:4, 1:4]}"""      )



"""simple docstring"""





from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						SCREAMING_SNAKE_CASE_ (						__a ):




		"""simple docstring"""





		__lowercase							:  List[Any]						   =		['''image_processor''', '''tokenizer''']
		__lowercase							:  List[Any]						   =		'''BridgeTowerImageProcessor'''
		__lowercase							:  Tuple						   =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')




		def __init__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__):
				super().__init__(lowerCAmelCase__							,  lowerCAmelCase__)




		def __call__(     self							,  lowerCAmelCase__							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = 0							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = None							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = False							,  lowerCAmelCase__ = True							,  lowerCAmelCase__ = None							,  **lowerCAmelCase__							,  ):
				__SCREAMING_SNAKE_CASE            = self.tokenizer(
				    text=lowerCAmelCase__							,  add_special_tokens=lowerCAmelCase__							,  padding=lowerCAmelCase__							,  truncation=lowerCAmelCase__							,  max_length=lowerCAmelCase__							,  stride=lowerCAmelCase__							,  pad_to_multiple_of=lowerCAmelCase__							,  return_token_type_ids=lowerCAmelCase__							,  return_attention_mask=lowerCAmelCase__							,  return_overflowing_tokens=lowerCAmelCase__							,  return_special_tokens_mask=lowerCAmelCase__							,  return_offsets_mapping=lowerCAmelCase__							,  return_length=lowerCAmelCase__							,  verbose=lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  **lowerCAmelCase__							,  )
				# add pixel_values + pixel_mask
				__SCREAMING_SNAKE_CASE            = self.image_processor(
				    lowerCAmelCase__							,  return_tensors=lowerCAmelCase__							,  do_normalize=lowerCAmelCase__							,  do_center_crop=lowerCAmelCase__							,  **lowerCAmelCase__)
				encoding.update(lowerCAmelCase__)

				return encoding




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.batch_decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		def 	snake_case_     (     self							,  *lowerCAmelCase__							,  **lowerCAmelCase__):
				return self.tokenizer.decode(*lowerCAmelCase__							,  **lowerCAmelCase__)




		@property
		def 	snake_case_     (     self):
				__SCREAMING_SNAKE_CASE            = self.tokenizer.model_input_names
				__SCREAMING_SNAKE_CASE            = self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))




