






from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
     TFDebertaVaForMaskedLM,
     TFDebertaVaForQuestionAnswering,
     TFDebertaVaForSequenceClassification,
     TFDebertaVaForTokenClassification,
     TFDebertaVaModel,
 )







class      UpperCAmelCase_		:



      """simple docstring"""
      def __init__(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_=13       ,							SCREAMING_SNAKE_CASE_=7       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_=99       ,							SCREAMING_SNAKE_CASE_=32       ,							SCREAMING_SNAKE_CASE_=2       ,							SCREAMING_SNAKE_CASE_=4       ,							SCREAMING_SNAKE_CASE_=37       ,							SCREAMING_SNAKE_CASE_="gelu"       ,							SCREAMING_SNAKE_CASE_=0.1       ,							SCREAMING_SNAKE_CASE_=0.1       ,							SCREAMING_SNAKE_CASE_=512       ,							SCREAMING_SNAKE_CASE_=16       ,							SCREAMING_SNAKE_CASE_=2       ,							SCREAMING_SNAKE_CASE_=0.02       ,							SCREAMING_SNAKE_CASE_=False       ,							SCREAMING_SNAKE_CASE_=True       ,							SCREAMING_SNAKE_CASE_="None"       ,							SCREAMING_SNAKE_CASE_=3       ,							SCREAMING_SNAKE_CASE_=4       ,							SCREAMING_SNAKE_CASE_=None       ,							)							-> int:
          UpperCamelCase			:Union[str, Any]      				=  parent
          UpperCamelCase			:Dict      				=  batch_size
          UpperCamelCase			:List[Any]      				=  seq_length
          UpperCamelCase			:List[str]      				=  is_training
          UpperCamelCase			:str      				=  use_input_mask
          UpperCamelCase			:Union[str, Any]      				=  use_token_type_ids
          UpperCamelCase			:Optional[int]      				=  use_labels
          UpperCamelCase			:List[Any]      				=  vocab_size
          UpperCamelCase			:Optional[Any]      				=  hidden_size
          UpperCamelCase			:Union[str, Any]      				=  num_hidden_layers
          UpperCamelCase			:Optional[int]      				=  num_attention_heads
          UpperCamelCase			:Dict      				=  intermediate_size
          UpperCamelCase			:Union[str, Any]      				=  hidden_act
          UpperCamelCase			:str      				=  hidden_dropout_prob
          UpperCamelCase			:Dict      				=  attention_probs_dropout_prob
          UpperCamelCase			:str      				=  max_position_embeddings
          UpperCamelCase			:int      				=  type_vocab_size
          UpperCamelCase			:Any      				=  type_sequence_label_size
          UpperCamelCase			:Optional[Any]      				=  initializer_range
          UpperCamelCase			:List[Any]      				=  num_labels
          UpperCamelCase			:Optional[int]      				=  num_choices
          UpperCamelCase			:int      				=  relative_attention
          UpperCamelCase			:str      				=  position_biased_input
          UpperCamelCase			:List[Any]      				=  pos_att_type
          UpperCamelCase			:Union[str, Any]      				=  scope
      def        UpperCAmelCase		(							self					)							-> Tuple:
          UpperCamelCase			:Union[str, Any]      				=  ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size					)

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

          UpperCamelCase			:Optional[Any]      				=  None
          if self.use_token_type_ids:
              UpperCamelCase			:str      				=  ids_tensor([self.batch_size, self.seq_length]       ,							self.type_vocab_size					)

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

          UpperCamelCase			:List[str]      				=  DebertaVaConfig(
              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       ,							position_biased_input=self.position_biased_input       ,							initializer_range=self.initializer_range       ,							return_dict=SCREAMING_SNAKE_CASE_       ,							)

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> List[Any]:
          UpperCamelCase			:Dict      				=  TFDebertaVaModel(config=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Union[str, Any]      				=  {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

          UpperCamelCase			:Union[str, Any]      				=  [input_ids, input_mask]
          UpperCamelCase			:Any      				=  model(SCREAMING_SNAKE_CASE_					)

          UpperCamelCase			:Optional[Any]      				=  model(SCREAMING_SNAKE_CASE_					)

          self.parent.assertEqual(result.last_hidden_state.shape       ,							(self.batch_size, self.seq_length, self.hidden_size)					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> Any:
          UpperCamelCase			:Optional[Any]      				=  TFDebertaVaForMaskedLM(config=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Optional[Any]      				=  {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          UpperCamelCase			:List[str]      				=  model(SCREAMING_SNAKE_CASE_					)
          self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.vocab_size)					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> Optional[Any]:
          UpperCamelCase			:List[Any]      				=  self.num_labels
          UpperCamelCase			:int      				=  TFDebertaVaForSequenceClassification(config=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Optional[Any]      				=  {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          UpperCamelCase			:int      				=  model(SCREAMING_SNAKE_CASE_					)
          self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.num_labels)					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> Dict:
          UpperCamelCase			:Dict      				=  self.num_labels
          UpperCamelCase			:str      				=  TFDebertaVaForTokenClassification(config=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Dict      				=  {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }
          UpperCamelCase			:Tuple      				=  model(SCREAMING_SNAKE_CASE_					)
          self.parent.assertEqual(result.logits.shape       ,							(self.batch_size, self.seq_length, self.num_labels)					)
      def        UpperCAmelCase		(							self       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_					)							-> List[str]:
          UpperCamelCase			:Union[str, Any]      				=  TFDebertaVaForQuestionAnswering(config=SCREAMING_SNAKE_CASE_					)
          UpperCamelCase			:Optional[int]      				=  {
              "input_ids": input_ids,
              "attention_mask": input_mask,
              "token_type_ids": token_type_ids,
          }

          UpperCamelCase			:List[Any]      				=  model(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					)							-> List[str]:
          UpperCamelCase			:List[str]      				=  self.prepare_config_and_inputs()
          (
              UpperCamelCase  
          )			:int      				=  config_and_inputs
          UpperCamelCase			:Any      				=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict







@require_tf
class      UpperCAmelCase_		(   _UpperCamelCase,      _UpperCamelCase,      unittest.TestCase					):



      """simple docstring"""
      UpperCamelCase_							:   int 	=(
          (
              TFDebertaVaModel,
              TFDebertaVaForMaskedLM,
              TFDebertaVaForQuestionAnswering,
              TFDebertaVaForSequenceClassification,
              TFDebertaVaForTokenClassification,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_							:   List[str] 	=(
          {
              """feature-extraction""": TFDebertaVaModel,
              """fill-mask""": TFDebertaVaForMaskedLM,
              """question-answering""": TFDebertaVaForQuestionAnswering,
              """text-classification""": TFDebertaVaForSequenceClassification,
              """token-classification""": TFDebertaVaForTokenClassification,
              """zero-shot""": TFDebertaVaForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      UpperCamelCase_							:   str 	=False
      UpperCamelCase_							:   List[str] 	=False
      def        UpperCAmelCase		(							self					)							-> Dict:
          UpperCamelCase			:Any      				=  TFDebertaVaModelTester(self					)
          UpperCamelCase			:int      				=  ConfigTester(self       ,							config_class=SCREAMING_SNAKE_CASE_       ,							hidden_size=37					)
      def        UpperCAmelCase		(							self					)							-> Union[str, Any]:
          self.config_tester.run_common_tests()
      def        UpperCAmelCase		(							self					)							-> Any:
          UpperCamelCase			:Dict      				=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self					)							-> str:
          UpperCamelCase			:List[str]      				=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self					)							-> List[Any]:
          UpperCamelCase			:Tuple      				=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self					)							-> List[Any]:
          UpperCamelCase			:Any      				=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_					)
      def        UpperCAmelCase		(							self					)							-> List[Any]:
          UpperCamelCase			:int      				=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_					)




      @slow
      def        UpperCAmelCase		(							self					)							-> List[str]:
          UpperCamelCase			:Tuple      				=  TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
          self.assertIsNotNone(SCREAMING_SNAKE_CASE_					)







@require_tf
class      UpperCAmelCase_		(   unittest.TestCase					):



      """simple docstring"""
      @unittest.skip(reason='''Model not available yet'''					)
      def        UpperCAmelCase		(							self					)							-> Dict:
          pass




      @slow
      def        UpperCAmelCase		(							self					)							-> List[Any]:
          UpperCamelCase			:Optional[Any]      				=  TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''					)
          UpperCamelCase			:Any      				=  tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
          UpperCamelCase			:List[str]      				=  tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
          UpperCamelCase			:Tuple      				=  model(SCREAMING_SNAKE_CASE_       ,							attention_mask=SCREAMING_SNAKE_CASE_					)[0]

          UpperCamelCase			:List[Any]      				=  tf.constant(
              [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]					)
          tf.debugging.assert_near(output[:, 1:4, 1:4]       ,							SCREAMING_SNAKE_CASE_       ,							atol=1e-4					)












def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	[]
   a__						:    str   		=	set({"(", "[", "{"} )
   a__						:    List[str]   		=	set({")", "]", "}"} )
   a__						:    int   		=	{"{": "}", "[": "]", "(": ")"}

   for i in range(len(lowerCamelCase ) ):
      if s[i] in open_brackets:
         stack.append(s[i] )

      elif s[i] in closed_brackets and (
          len(lowerCamelCase ) == 0 or (len(lowerCamelCase ) > 0 and open_to_closed[stack.pop()] != s[i])
      ):
         return False

   return len(lowerCamelCase ) == 0






def    _A	(   ):
   a__						:    Optional[int]   		=	input("Enter sequence of brackets: " )
   if is_balanced(lowerCamelCase ):
      print(lowerCamelCase				,			"is balanced" )
   else:
      print(lowerCamelCase				,			"is not balanced" )


if __name__ == "__main__":
  main()

