code: 
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  UpperCamelCase_	:    Optional[Any]  					=       {"""configuration_vilt""": ["""VILT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViltConfig"""]}
  
  try:
  	if not is_vision_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	UpperCamelCase_	:    Union[str, Any]  					=       ["""ViltFeatureExtractor"""]
  	UpperCamelCase_	:    List[Any]  					=       ["""ViltImageProcessor"""]
  	UpperCamelCase_	:    Optional[int]  					=       ["""ViltProcessor"""]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	UpperCamelCase_	:    Optional[int]  					=       [
  	    """VILT_PRETRAINED_MODEL_ARCHIVE_LIST""",
  	    """ViltForImageAndTextRetrieval""",
  	    """ViltForImagesAndTextClassification""",
  	    """ViltForTokenClassification""",
  	    """ViltForMaskedLM""",
  	    """ViltForQuestionAnswering""",
  	    """ViltLayer""",
  	    """ViltModel""",
  	    """ViltPreTrainedModel""",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig
  
  	try:
  		if not is_vision_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .feature_extraction_vilt import ViltFeatureExtractor
  		from .image_processing_vilt import ViltImageProcessor
  		from .processing_vilt import ViltProcessor
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_vilt import (
  		    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    ViltForImageAndTextRetrieval,
  		    ViltForImagesAndTextClassification,
  		    ViltForMaskedLM,
  		    ViltForQuestionAnswering,
  		    ViltForTokenClassification,
  		    ViltLayer,
  		    ViltModel,
  		    ViltPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	UpperCamelCase_	:    Optional[int]  					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure)
  
  
  
  
  
style_context: 
  from __future__ import annotations
  
  import unittest
  
  from transformers import DebertaConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  							import tensorflow as tf
  
  							from transformers import (
  							    TFDebertaForMaskedLM,
  							    TFDebertaForQuestionAnswering,
  							    TFDebertaForSequenceClassification,
  							    TFDebertaForTokenClassification,
  							    TFDebertaModel,
  							)
  
  
  
  
  class      __lowercase	:
  
  
  
  
  
  
  	def __init__(self							:   Tuple			,  snake_case							:   Union[str, Any]			,  snake_case							:   List[Any]=13			,  snake_case							:   Union[str, Any]=7			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   int=True			,  snake_case							:   Dict=True			,  snake_case							:   Union[str, Any]=True			,  snake_case							:   str=99			,  snake_case							:   str=32			,  snake_case							:   List[Any]=2			,  snake_case							:   Optional[int]=4			,  snake_case							:   Union[str, Any]=37			,  snake_case							:   int="gelu"			,  snake_case							:   Any=0.1			,  snake_case							:   str=0.1			,  snake_case							:   Dict=512			,  snake_case							:   Any=16			,  snake_case							:   Tuple=2			,  snake_case							:   Dict=0.02			,  snake_case							:   Tuple=3			,  snake_case							:   Dict=4			,  snake_case							:   Optional[Any]=None			,  )   -> str:
  							_lowercase					:  Optional[int]    			=      parent
  							_lowercase					:  Union[str, Any]    			=      13
  							_lowercase					:  Dict    			=      7
  							_lowercase					:  Any    			=      True
  							_lowercase					:  Tuple    			=      True
  							_lowercase					:  Optional[Any]    			=      True
  							_lowercase					:  Optional[Any]    			=      True
  							_lowercase					:  str    			=      99
  							_lowercase					:  Optional[int]    			=      32
  							_lowercase					:  int    			=      2
  							_lowercase					:  Optional[int]    			=      4
  							_lowercase					:  List[str]    			=      37
  							_lowercase					:  Dict    			=      "gelu"
  							_lowercase					:  str    			=      0.1
  							_lowercase					:  Optional[int]    			=      0.1
  							_lowercase					:  int    			=      512
  							_lowercase					:  str    			=      16
  							_lowercase					:  List[Any]    			=      False
  							_lowercase					:  Dict    			=      -1
  							_lowercase					:  Optional[Any]    			=      True
  							_lowercase					:  Dict    			=      2
  							_lowercase					:  Union[str, Any]    			=      0.02
  							_lowercase					:  int    			=      3
  							_lowercase					:  Tuple    			=      4
  							_lowercase					:  Optional[Any]    			=      None
  
  
  
  
  
  
  	def 			_a(self							:   List[str]						)   -> List[Any]:
  							_lowercase					:  Optional[Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size						)
  
  							_lowercase					:  Optional[Any]    			=      None
  							if self.use_input_mask:
  													_lowercase					:  int    			=      random_attention_mask([self.batch_size, self.seq_length]						)
  
  							_lowercase					:  Dict    			=      None
  							if self.use_token_type_ids:
  													_lowercase					:  Any    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.type_vocab_size						)
  
  							_lowercase					:  Optional[Any]    			=      None
  							_lowercase					:  Tuple    			=      None
  							_lowercase					:  List[str]    			=      None
  							if self.use_labels:
  													_lowercase					:  List[Any]    			=      ids_tensor([self.batch_size]			,  self.type_sequence_label_size						)
  													_lowercase					:  Union[str, Any]    			=      ids_tensor([self.batch_size, self.seq_length]			,  self.num_labels						)
  
  							_lowercase					:  Union[str, Any]    			=      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			,  relative_attention=self.relative_attention			,  max_relative_positions=self.max_relative_positions			,  position_biased_input=self.position_biased_input			,  initializer_range=self.initializer_range			,  return_dict=snake_case			,  )
  
  							return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   List[Any]			,  snake_case							:   str			,  snake_case							:   Optional[int]			,  snake_case							:   List[Any]			,  snake_case							:   Union[str, Any]						)   -> str:
  							_lowercase					:  Tuple    			=      TFDebertaModel(config=snake_case						)
  							_lowercase					:  Any    			=      {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
  
  							_lowercase					:  Tuple    			=      [input_ids, input_mask]
  							_lowercase					:  Any    			=      model(snake_case						)
  
  							_lowercase					:  Any    			=      model(snake_case						)
  
  							self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   int			,  snake_case							:   int			,  snake_case							:   Union[str, Any]			,  snake_case							:   Tuple			,  snake_case							:   List[Any]			,  snake_case							:   Any			,  snake_case							:   Union[str, Any]			,  snake_case							:   int						)   -> int:
  							_lowercase					:  int    			=      TFDebertaForMaskedLM(config=snake_case						)
  							_lowercase					:  List[Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  							_lowercase					:  int    			=      model(snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.vocab_size)						)
  
  
  
  
  
  
  	def 			_a(self							:   str			,  snake_case							:   Dict			,  snake_case							:   Optional[int]			,  snake_case							:   int			,  snake_case							:   Tuple			,  snake_case							:   int			,  snake_case							:   Any			,  snake_case							:   Any						)   -> List[str]:
  							_lowercase					:  Dict    			=      self.num_labels
  							_lowercase					:  Tuple    			=      TFDebertaForSequenceClassification(config=snake_case						)
  							_lowercase					:  Optional[Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  
  							_lowercase					:  Dict    			=      model(snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]			,  snake_case							:   int			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   str			,  snake_case							:   Optional[int]			,  snake_case							:   int			,  snake_case							:   List[str]						)   -> Dict:
  							_lowercase					:  Union[str, Any]    			=      self.num_labels
  							_lowercase					:  List[Any]    			=      TFDebertaForTokenClassification(config=snake_case						)
  							_lowercase					:  Union[str, Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  							_lowercase					:  str    			=      model(snake_case						)
  							self.parent.assertEqual(result.logits.shape			,  (self.batch_size, self.seq_length, self.num_labels)						)
  
  
  
  
  
  
  	def 			_a(self							:   Dict			,  snake_case							:   List[Any]			,  snake_case							:   Optional[Any]			,  snake_case							:   Optional[int]			,  snake_case							:   Union[str, Any]			,  snake_case							:   Dict			,  snake_case							:   str			,  snake_case							:   Optional[Any]						)   -> Optional[Any]:
  							_lowercase					:  str    			=      TFDebertaForQuestionAnswering(config=snake_case						)
  							_lowercase					:  Optional[Any]    			=      {
  							    "input_ids": input_ids,
  							    "attention_mask": input_mask,
  							    "token_type_ids": token_type_ids,
  							}
  
  							_lowercase					:  Tuple    			=      model(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 			_a(self							:   List[Any]						)   -> Optional[Any]:
  							_lowercase					:  int    			=      self.prepare_config_and_inputs()
  							(
  							    (
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						(
  							    _lowercase
  							)			,						
  							)					:  Optional[Any]    			=      config_and_inputs
  							_lowercase					:  str    			=      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
  							return config, inputs_dict
  
  
  
  
  @require_tf
  class      __lowercase	(	__snake_case      ,   __snake_case      ,   unittest.TestCase					):
  	_A   			=      (
  	    (
  	        TFDebertaModel,
  	        TFDebertaForMaskedLM,
  	        TFDebertaForQuestionAnswering,
  	        TFDebertaForSequenceClassification,
  	        TFDebertaForTokenClassification,
  	    )
  	    if is_tf_available()
  	    else ()
  	)
  	_A   			=      (
  	    {
  	        "feature-extraction": TFDebertaModel,
  	        "fill-mask": TFDebertaForMaskedLM,
  	        "question-answering": TFDebertaForQuestionAnswering,
  	        "text-classification": TFDebertaForSequenceClassification,
  	        "token-classification": TFDebertaForTokenClassification,
  	        "zero-shot": TFDebertaForSequenceClassification,
  	    }
  	    if is_tf_available()
  	    else {}
  	)
  
  	_A   			=      False
  	_A   			=      False
  
  
  
  
  
  
  	def 			_a(self							:   int						)   -> List[Any]:
  							_lowercase					:  List[str]    			=      TFDebertaModelTester(self						)
  							_lowercase					:  Optional[int]    			=      ConfigTester(self			,  config_class=snake_case			,  hidden_size=37						)
  
  
  
  
  
  
  	def 			_a(self							:   Any						)   -> Tuple:
  							self.config_tester.run_common_tests()
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> Dict:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_model(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Optional[Any]						)   -> Any:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_masked_lm(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   str						)   -> int:
  							_lowercase					:  int    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_question_answering(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Any						)   -> List[Any]:
  							_lowercase					:  Any    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_sequence_classification(*snake_case						)
  
  
  
  
  
  
  	def 			_a(self							:   Tuple						)   -> str:
  							_lowercase					:  Union[str, Any]    			=      self.model_tester.prepare_config_and_inputs()
  							self.model_tester.create_and_check_for_token_classification(*snake_case						)
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Optional[Any]						)   -> int:
  							_lowercase					:  Optional[int]    			=      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"						)
  							self.assertIsNotNone(snake_case						)
  
  
  
  
  @require_tf
  class      __lowercase	(	unittest.TestCase					):
  
  
  
  
  
  
  	@unittest.skip(reason="Model not available yet"						)
  	def 			_a(self							:   Optional[int]						)   -> int:
  							pass
  
  
  
  
  
  
  	@slow
  	def 			_a(self							:   Optional[int]						)   -> Optional[int]:
  							_lowercase					:  Optional[Any]    			=      TFDebertaModel.from_pretrained("kamalkraj/deberta-base"						)
  							_lowercase					:  List[Any]    			=      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]						)
  							_lowercase					:  Union[str, Any]    			=      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
  							_lowercase					:  List[str]    			=      model(snake_case			,  attention_mask=snake_case						)[0]
  
  							_lowercase					:  List[str]    			=      tf.constant(
  							    [
  							        [
  							            [-0.59_85_58_96, -0.80_55_25_66, -0.8_46_21_35],
  							            [1.4_48_40_25, -0.93_48_37_94, -0.80_59_30_85],
  							            [0.3_12_27_41, 0.00_31_60_59, -1.4_13_13_77],
  							        ]
  							    ]						)
  							tf.debugging.assert_near(output[:, 1:4, 1:4]			,  snake_case			,  atol=1e-4						)
  
  
  
  
  