from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
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 (
          TF_MODEL_FOR_PRETRAINING_MAPPING,
          TFMobileBertForMaskedLM,
          TFMobileBertForMultipleChoice,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertModel,
      )

@require_tf
class       snake_case					(      UpperCamelCase_   ,						UpperCamelCase_   ,						unittest.TestCase						):
  lowercase_			=     (
      (
          TFMobileBertModel,
          TFMobileBertForMaskedLM,
          TFMobileBertForNextSentencePrediction,
          TFMobileBertForPreTraining,
          TFMobileBertForQuestionAnswering,
          TFMobileBertForSequenceClassification,
          TFMobileBertForTokenClassification,
          TFMobileBertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  lowercase_			=     (
      {
          'feature-extraction': TFMobileBertModel,
          'fill-mask': TFMobileBertForMaskedLM,
          'question-answering': TFMobileBertForQuestionAnswering,
          'text-classification': TFMobileBertForSequenceClassification,
          'token-classification': TFMobileBertForTokenClassification,
          'zero-shot': TFMobileBertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  lowercase_			=     False
  lowercase_			=     False




  def 	__lowercase(      self     :	List[Any]     , a_     :	int     , a_     :	str     , a_     :	Optional[int]=False				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Any			=		super()._prepare_for_class(a_     , a_     , return_labels=a_				)

       if return_labels:
            if model_class in get_values(a_				):
                 SCREAMING_SNAKE_CASE__				:				List[Any]			=		tf.zeros(self.model_tester.batch_size     , dtype=tf.intaa				)

       return inputs_dict

  class       snake_case					(      UpperCamelCase_						):




    def __init__(      self     :	Optional[Any]     , a_     :	str     , a_     :	str=13     , a_     :	Dict=7     , a_     :	Any=True     , a_     :	List[Any]=True     , a_     :	Any=True     , a_     :	List[str]=True     , a_     :	Optional[Any]=99     , a_     :	Optional[int]=32     , a_     :	int=32     , a_     :	List[str]=2     , a_     :	Dict=4     , a_     :	List[str]=37     , a_     :	Optional[int]="gelu"     , a_     :	List[str]=0.1     , a_     :	str=0.1     , a_     :	Any=512     , a_     :	Optional[Any]=16     , a_     :	Union[str, Any]=2     , a_     :	Tuple=0.02     , a_     :	str=3     , a_     :	Optional[Any]=4     , a_     :	Optional[int]=None     , )->       List[str]:




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




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




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				int			=		ids_tensor([self.batch_size, self.seq_length]     , self.vocab_size				)

         SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		None
         if self.use_input_mask:
              SCREAMING_SNAKE_CASE__				:				List[Any]			=		random_attention_mask([self.batch_size, self.seq_length]				)

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

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

         SCREAMING_SNAKE_CASE__				:				Tuple			=		MobileBertConfig(
             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     , embedding_size=self.embedding_size     , )

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




    def 	__lowercase(      self     :	Optional[int]     , a_     :	int     , a_     :	int     , a_     :	str     , a_     :	Dict     , a_     :	str     , a_     :	Optional[int]     , a_     :	int				)->       int:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				int			=		TFMobileBertModel(config=a_				)
         SCREAMING_SNAKE_CASE__				:				int			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				Tuple			=		model(a_				)

         SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[input_ids, input_mask]
         SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_				)

         SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		model(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 	__lowercase(      self     :	str     , a_     :	List[Any]     , a_     :	Optional[Any]     , a_     :	Optional[Any]     , a_     :	Tuple     , a_     :	int     , a_     :	Optional[Any]     , a_     :	List[str]				)->       str:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				Optional[int]			=		TFMobileBertForMaskedLM(config=a_				)
         SCREAMING_SNAKE_CASE__				:				Optional[int]			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				Any			=		model(a_				)
         self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)




    def 	__lowercase(      self     :	Tuple     , a_     :	List[str]     , a_     :	Dict     , a_     :	List[str]     , a_     :	Tuple     , a_     :	int     , a_     :	Union[str, Any]     , a_     :	List[Any]				)->       Dict:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		TFMobileBertForNextSentencePrediction(config=a_				)
         SCREAMING_SNAKE_CASE__				:				Dict			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				Dict			=		model(a_				)
         self.parent.assertEqual(result.logits.shape     , (self.batch_size, 2)				)




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




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				str			=		TFMobileBertForPreTraining(config=a_				)
         SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				Optional[int]			=		model(a_				)
         self.parent.assertEqual(
             result.prediction_logits.shape     , (self.batch_size, self.seq_length, self.vocab_size)				)
         self.parent.assertEqual(result.seq_relationship_logits.shape     , (self.batch_size, 2)				)




    def 	__lowercase(      self     :	List[Any]     , a_     :	Any     , a_     :	Union[str, Any]     , a_     :	Tuple     , a_     :	Dict     , a_     :	Optional[int]     , a_     :	Optional[int]     , a_     :	List[Any]				)->       int:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				int			=		self.num_labels
         SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		TFMobileBertForSequenceClassification(config=a_				)
         SCREAMING_SNAKE_CASE__				:				str			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				int			=		model(a_				)
         self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_labels)				)




    def 	__lowercase(      self     :	List[Any]     , a_     :	Any     , a_     :	Dict     , a_     :	Tuple     , a_     :	Tuple     , a_     :	Union[str, Any]     , a_     :	str     , a_     :	Optional[Any]				)->       Optional[Any]:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				List[str]			=		self.num_choices
         SCREAMING_SNAKE_CASE__				:				str			=		TFMobileBertForMultipleChoice(config=a_				)
         SCREAMING_SNAKE_CASE__				:				List[Any]			=		tf.tile(tf.expand_dims(a_     , 1				)     , (1, self.num_choices, 1)				)
         SCREAMING_SNAKE_CASE__				:				List[str]			=		tf.tile(tf.expand_dims(a_     , 1				)     , (1, self.num_choices, 1)				)
         SCREAMING_SNAKE_CASE__				:				List[str]			=		tf.tile(tf.expand_dims(a_     , 1				)     , (1, self.num_choices, 1)				)
         SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{
             'input_ids': multiple_choice_inputs_ids,
             'attention_mask': multiple_choice_input_mask,
             'token_type_ids': multiple_choice_token_type_ids,
         }
         SCREAMING_SNAKE_CASE__				:				List[str]			=		model(a_				)
         self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.num_choices)				)




    def 	__lowercase(      self     :	Dict     , a_     :	Any     , a_     :	List[Any]     , a_     :	Union[str, Any]     , a_     :	List[Any]     , a_     :	Tuple     , a_     :	Tuple     , a_     :	List[Any]				)->       Any:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				str			=		self.num_labels
         SCREAMING_SNAKE_CASE__				:				Any			=		TFMobileBertForTokenClassification(config=a_				)
         SCREAMING_SNAKE_CASE__				:				Tuple			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		model(a_				)
         self.parent.assertEqual(result.logits.shape     , (self.batch_size, self.seq_length, self.num_labels)				)




    def 	__lowercase(      self     :	Union[str, Any]     , a_     :	Tuple     , a_     :	Tuple     , a_     :	List[str]     , a_     :	List[str]     , a_     :	Union[str, Any]     , a_     :	Optional[Any]     , a_     :	Tuple				)->       Tuple:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				List[str]			=		TFMobileBertForQuestionAnswering(config=a_				)
         SCREAMING_SNAKE_CASE__				:				Dict			=		{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
         SCREAMING_SNAKE_CASE__				:				str			=		model(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 	__lowercase(      self     :	List[str]				)->       Optional[int]:




         """simple docstring"""
         SCREAMING_SNAKE_CASE__				:				List[Any]			=		self.prepare_config_and_inputs()
         (
             (
             SCREAMING_SNAKE_CASE__
         )			,							(
             SCREAMING_SNAKE_CASE__
         )			,							(
             SCREAMING_SNAKE_CASE__
         )			,							(
             SCREAMING_SNAKE_CASE__
         )			,							(
             SCREAMING_SNAKE_CASE__
         )			,							(
             SCREAMING_SNAKE_CASE__
         )			,							(
             SCREAMING_SNAKE_CASE__
         )			,							
         )				:				List[Any]			=		config_and_inputs
         SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
         return config, inputs_dict




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		TFMobileBertModelTest.TFMobileBertModelTester(self				)
       SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		ConfigTester(self     , config_class=a_     , hidden_size=37				)




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




       """simple docstring"""
       self.config_tester.run_common_tests()




  def 	__lowercase(      self     :	List[Any]				)->       Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_model(*a_				)




  def 	__lowercase(      self     :	str				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_masked_lm(*a_				)




  def 	__lowercase(      self     :	Dict				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_multiple_choice(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				int			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*a_				)




  def 	__lowercase(      self     :	Optional[Any]				)->       Any:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Tuple			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_pretraining(*a_				)




  def 	__lowercase(      self     :	Dict				)->       int:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_question_answering(*a_				)




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




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_sequence_classification(*a_				)




  def 	__lowercase(      self     :	Any				)->       str:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Optional[int]			=		self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_mobilebert_for_token_classification(*a_				)




  @slow
  def 	__lowercase(      self     :	Dict				)->       List[str]:




       """simple docstring"""
       # for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       for model_name in ["google/mobilebert-uncased"]:
            SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		TFMobileBertModel.from_pretrained(a_				)
            self.assertIsNotNone(a_				)




@require_tf
class       snake_case					(      unittest.TestCase						):




  @slow
  def 	__lowercase(      self     :	Optional[Any]				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				Dict			=		TFMobileBertForPreTraining.from_pretrained('google/mobilebert-uncased'				)
       SCREAMING_SNAKE_CASE__				:				List[Any]			=		tf.constant([[0, 1, 2, 3, 4, 5]]				)
       SCREAMING_SNAKE_CASE__				:				Any			=		model(a_				)[0]

       SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		[1, 6, 3_0522]
       self.assertEqual(output.shape     , a_				)

       SCREAMING_SNAKE_CASE__				:				List[str]			=		tf.constant(
           [
               [
                   [-4.591_9547, -9.24_8295, -9.64_5256],
                   [-6.730_6175, -6.44_0284, -6.605_2837],
                   [-7.274_3506, -6.784_7915, -6.02_4673],
               ]
           ]				)
       tf.debugging.assert_near(output[:, :3, :3]     , a_     , atol=1e-4				)




def     _a				(      lowercase__  :			Optional[int] ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		[]
     SCREAMING_SNAKE_CASE__				:				List[Any]			=		set({'(', '[', '{'} )
     SCREAMING_SNAKE_CASE__				:				Optional[int]			=		set({')', ']', '}'} )
     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		{'{': '}', '[': ']', '(': ')'}

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

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

     return len(lowercase__ ) == 0







def     _a				(      ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Tuple			=		input('Enter sequence of brackets: ' )
     if is_balanced(lowercase__ ):
          print(lowercase__     ,  'is balanced' )
     else:
          print(lowercase__     ,  'is not balanced' )


if __name__ == "__main__":
      main()



