'''simple docstring'''






from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, 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 (
        TFConvBertForMaskedLM,
        TFConvBertForMultipleChoice,
        TFConvBertForQuestionAnswering,
        TFConvBertForSequenceClassification,
        TFConvBertForTokenClassification,
        TFConvBertModel,
    )




class    a :



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=13   ,    snake_case_=7   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=True   ,    snake_case_=99   ,    snake_case_=32   ,    snake_case_=2   ,    snake_case_=4   ,    snake_case_=37   ,    snake_case_="gelu"   ,    snake_case_=0.1   ,    snake_case_=0.1   ,    snake_case_=512   ,    snake_case_=16   ,    snake_case_=2   ,    snake_case_=0.0_2   ,    snake_case_=3   ,    snake_case_=4   ,    snake_case_=None   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= parent
    __UpperCAmelCase:					str		= 13
    __UpperCAmelCase:					List[str]		= 7
    __UpperCAmelCase:					List[str]		= True
    __UpperCAmelCase:					str		= True
    __UpperCAmelCase:					Optional[int]		= True
    __UpperCAmelCase:					int		= True
    __UpperCAmelCase:					List[str]		= 99
    __UpperCAmelCase:					Any		= 384
    __UpperCAmelCase:					List[str]		= 2
    __UpperCAmelCase:					Optional[Any]		= 4
    __UpperCAmelCase:					str		= 37
    __UpperCAmelCase:					List[str]		= """gelu"""
    __UpperCAmelCase:					Optional[Any]		= 0.1
    __UpperCAmelCase:					Dict		= 0.1
    __UpperCAmelCase:					Optional[Any]		= 512
    __UpperCAmelCase:					int		= 16
    __UpperCAmelCase:					List[Any]		= 2
    __UpperCAmelCase:					Optional[Any]		= 0.0_2
    __UpperCAmelCase:					Dict		= 3
    __UpperCAmelCase:					Any		= 4
    __UpperCAmelCase:					Union[str, Any]		= 128
    __UpperCAmelCase:					Tuple		= 2
    __UpperCAmelCase:					Optional[Any]		= 9
    __UpperCAmelCase:					Any		= 1
    __UpperCAmelCase:					Tuple		= None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size					)

    __UpperCAmelCase:					Tuple		= None
    if self.use_input_mask:
      __UpperCAmelCase:					Any		= random_attention_mask([self.batch_size, self.seq_length]					)

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

    __UpperCAmelCase:					Optional[Any]		= None
    __UpperCAmelCase:					Optional[int]		= None
    __UpperCAmelCase:					int		= None
    if self.use_labels:
      __UpperCAmelCase:					Optional[int]		= ids_tensor([self.batch_size]   ,    self.type_sequence_label_size					)
      __UpperCAmelCase:					int		= ids_tensor([self.batch_size, self.seq_length]   ,    self.num_labels					)
      __UpperCAmelCase:					List[str]		= ids_tensor([self.batch_size]   ,    self.num_choices					)

    __UpperCAmelCase:					List[Any]		= ConvBertConfig(
        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   ,    return_dict=snake_case_   ,    )

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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= TFConvBertModel(config=snake_case_					)
    __UpperCAmelCase:					Any		= {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

    __UpperCAmelCase:					int		= [input_ids, input_mask]
    __UpperCAmelCase:					List[Any]		= model(snake_case_					)

    __UpperCAmelCase:					str		= model(snake_case_					)

    self.parent.assertEqual(result.last_hidden_state.shape   ,    (self.batch_size, self.seq_length, self.hidden_size)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= TFConvBertForMaskedLM(config=snake_case_					)
    __UpperCAmelCase:					List[str]		= {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    __UpperCAmelCase:					str		= model(snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.vocab_size)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.num_labels
    __UpperCAmelCase:					str		= TFConvBertForSequenceClassification(config=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    __UpperCAmelCase:					List[Any]		= model(snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_labels)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Union[str, Any]		= self.num_choices
    __UpperCAmelCase:					Dict		= TFConvBertForMultipleChoice(config=snake_case_					)
    __UpperCAmelCase:					Optional[int]		= tf.tile(tf.expand_dims(snake_case_   ,    1					)   ,    (1, self.num_choices, 1)					)
    __UpperCAmelCase:					Dict		= tf.tile(tf.expand_dims(snake_case_   ,    1					)   ,    (1, self.num_choices, 1)					)
    __UpperCAmelCase:					str		= tf.tile(tf.expand_dims(snake_case_   ,    1					)   ,    (1, self.num_choices, 1)					)
    __UpperCAmelCase:					str		= {
        """input_ids""": multiple_choice_inputs_ids,
        """attention_mask""": multiple_choice_input_mask,
        """token_type_ids""": multiple_choice_token_type_ids,
    }
    __UpperCAmelCase:					Optional[Any]		= model(snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.num_choices)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.num_labels
    __UpperCAmelCase:					Optional[int]		= TFConvBertForTokenClassification(config=snake_case_					)
    __UpperCAmelCase:					List[Any]		= {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }
    __UpperCAmelCase:					Optional[int]		= model(snake_case_					)
    self.parent.assertEqual(result.logits.shape   ,    (self.batch_size, self.seq_length, self.num_labels)					)







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= TFConvBertForQuestionAnswering(config=snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= {
        """input_ids""": input_ids,
        """attention_mask""": input_mask,
        """token_type_ids""": token_type_ids,
    }

    __UpperCAmelCase:					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    lowercase_  (							self					):

    '''simple docstring'''







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




@require_tf
class    a (       __lowerCAmelCase						,  __lowerCAmelCase						,  unittest.TestCase				):



  """simple docstring"""
  __lowerCAmelCase				      =					(
      (
          TFConvBertModel,
          TFConvBertForMaskedLM,
          TFConvBertForQuestionAnswering,
          TFConvBertForSequenceClassification,
          TFConvBertForTokenClassification,
          TFConvBertForMultipleChoice,
      )
      if is_tf_available()
      else ()
  )
  __lowerCAmelCase				      =					(
      {
          """feature-extraction""": TFConvBertModel,
          """fill-mask""": TFConvBertForMaskedLM,
          """question-answering""": TFConvBertForQuestionAnswering,
          """text-classification""": TFConvBertForSequenceClassification,
          """token-classification""": TFConvBertForTokenClassification,
          """zero-shot""": TFConvBertForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False
  __lowerCAmelCase				      =					False







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= TFConvBertModelTester(self					)
    __UpperCAmelCase:					Union[str, Any]		= ConfigTester(self   ,    config_class=snake_case_   ,    hidden_size=37					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    self.config_tester.run_common_tests()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_masked_lm(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_multiple_choice(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_question_answering(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					int		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_sequence_classification(*snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_token_classification(*snake_case_					)







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCAmelCase:					Any		= True
    __UpperCAmelCase:					Tuple		= True

    if hasattr(snake_case_   ,    """use_cache"""					):
      __UpperCAmelCase:					Dict		= True

    __UpperCAmelCase:					List[Any]		= getattr(self.model_tester   ,    """encoder_seq_length"""   ,    self.model_tester.seq_length					)
    __UpperCAmelCase:					str		= getattr(self.model_tester   ,    """key_length"""   ,    snake_case_					)

    for model_class in self.all_model_classes:
      __UpperCAmelCase:					str		= self._prepare_for_class(snake_case_   ,    snake_case_					)
      __UpperCAmelCase:					Optional[Any]		= model_class(snake_case_					)
      __UpperCAmelCase:					Tuple		= len(model(snake_case_					)					)

      with tempfile.TemporaryDirectory() as tmpdirname:
        model.save_pretrained(snake_case_   ,    saved_model=snake_case_					)
        __UpperCAmelCase:					Tuple		= os.path.join(snake_case_   ,    """saved_model"""   ,    """1"""					)
        __UpperCAmelCase:					List[Any]		= tf.keras.models.load_model(snake_case_					)
        __UpperCAmelCase:					Optional[int]		= model(snake_case_					)

        if self.is_encoder_decoder:
          __UpperCAmelCase:					List[Any]		= outputs["""encoder_hidden_states"""]
          __UpperCAmelCase:					Optional[Any]		= outputs["""encoder_attentions"""]
        else:
          __UpperCAmelCase:					List[str]		= outputs["""hidden_states"""]
          __UpperCAmelCase:					Union[str, Any]		= outputs["""attentions"""]

        self.assertEqual(len(snake_case_					)   ,    snake_case_					)

        __UpperCAmelCase:					Optional[int]		= getattr(
            self.model_tester   ,    """expected_num_hidden_layers"""   ,    self.model_tester.num_hidden_layers + 1					)

        self.assertEqual(len(snake_case_					)   ,    snake_case_					)
        self.assertListEqual(
            list(output_hidden_states[0].shape[-2:]					)   ,    [self.model_tester.seq_length, self.model_tester.hidden_size]   ,    )

        self.assertEqual(len(snake_case_					)   ,    self.model_tester.num_hidden_layers					)
        self.assertListEqual(
            list(output_attentions[0].shape[-3:]					)   ,    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]   ,    )







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""					)
    self.assertIsNotNone(snake_case_					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					str		= self.model_tester.prepare_config_and_inputs_for_common()
    __UpperCAmelCase:					Optional[int]		= True
    __UpperCAmelCase:					Union[str, Any]		= getattr(self.model_tester   ,    """decoder_seq_length"""   ,    self.model_tester.seq_length					)
    __UpperCAmelCase:					Any		= getattr(self.model_tester   ,    """encoder_seq_length"""   ,    self.model_tester.seq_length					)
    __UpperCAmelCase:					List[Any]		= getattr(self.model_tester   ,    """key_length"""   ,    snake_case_					)
    __UpperCAmelCase:					Union[str, Any]		= getattr(self.model_tester   ,    """key_length"""   ,    snake_case_					)

    def check_decoder_attentions_output(snake_case_					):
      __UpperCAmelCase:					Dict		= len(snake_case_					)
      self.assertEqual(out_len % 2   ,    0					)
      __UpperCAmelCase:					Tuple		= outputs.decoder_attentions
      self.assertEqual(len(snake_case_					)   ,    self.model_tester.num_hidden_layers					)
      self.assertListEqual(
          list(decoder_attentions[0].shape[-3:]					)   ,    [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]   ,    )

    def check_encoder_attentions_output(snake_case_					):
      __UpperCAmelCase:					Union[str, Any]		= [
          t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
      ]
      self.assertEqual(len(snake_case_					)   ,    self.model_tester.num_hidden_layers					)
      self.assertListEqual(
          list(attentions[0].shape[-3:]					)   ,    [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]   ,    )

    for model_class in self.all_model_classes:
      __UpperCAmelCase:					Union[str, Any]		= True
      __UpperCAmelCase:					Optional[Any]		= False
      __UpperCAmelCase:					str		= model_class(snake_case_					)
      __UpperCAmelCase:					int		= model(self._prepare_for_class(snake_case_   ,    snake_case_					)					)
      __UpperCAmelCase:					Optional[Any]		= len(snake_case_					)
      self.assertEqual(config.output_hidden_states   ,    snake_case_					)
      check_encoder_attentions_output(snake_case_					)

      if self.is_encoder_decoder:
        __UpperCAmelCase:					Optional[Any]		= model_class(snake_case_					)
        __UpperCAmelCase:					List[str]		= model(self._prepare_for_class(snake_case_   ,    snake_case_					)					)
        self.assertEqual(config.output_hidden_states   ,    snake_case_					)
        check_decoder_attentions_output(snake_case_					)

      # Check that output attentions can also be changed via the config
      del inputs_dict["output_attentions"]
      __UpperCAmelCase:					List[Any]		= True
      __UpperCAmelCase:					Optional[Any]		= model_class(snake_case_					)
      __UpperCAmelCase:					Any		= model(self._prepare_for_class(snake_case_   ,    snake_case_					)					)
      self.assertEqual(config.output_hidden_states   ,    snake_case_					)
      check_encoder_attentions_output(snake_case_					)

      # Check attention is always last and order is fine
      __UpperCAmelCase:					List[Any]		= True
      __UpperCAmelCase:					Optional[int]		= True
      __UpperCAmelCase:					Tuple		= model_class(snake_case_					)
      __UpperCAmelCase:					Tuple		= model(self._prepare_for_class(snake_case_   ,    snake_case_					)					)

      self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)   ,    len(snake_case_					)					)
      self.assertEqual(model.config.output_hidden_states   ,    snake_case_					)
      check_encoder_attentions_output(snake_case_					)




@require_tf
class    a (       unittest.TestCase				):



  """simple docstring"""







  @slow
  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Tuple		= TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""					)
    __UpperCAmelCase:					Tuple		= tf.constant([[0, 1, 2, 3, 4, 5]]					)
    __UpperCAmelCase:					List[Any]		= model(snake_case_					)[0]

    __UpperCAmelCase:					int		= [1, 6, 768]
    self.assertEqual(output.shape   ,    snake_case_					)

    __UpperCAmelCase:					Union[str, Any]		= tf.constant(
        [
            [
                [-0.0_3_4_7_5_4_9_3, -0.4_6_8_6_0_3_4, -0.3_0_6_3_8_8_3_2],
                [0.2_2_6_3_7_2_4_8, -0.2_6_9_8_8_6_4_6, -0.7_4_2_3_4_2_4],
                [0.1_0_3_2_4_8_6_8, -0.4_5_0_1_3_5_0_8, -0.5_8_2_8_0_7_8_4],
            ]
        ]					)
    tf.debugging.assert_near(output[:, :3, :3]   ,    snake_case_   ,    atol=1e-4					)
'''simple docstring'''






def  UpperCamelCase__					(					_lowercase    :				List[str]     )							->	Union[str, Any]:
  __UpperCAmelCase:					int		= []
  __UpperCAmelCase:					List[Any]		= []
  __UpperCAmelCase:					List[Any]		= {
      """^""": 3,
      """*""": 2,
      """/""": 2,
      """%""": 2,
      """+""": 1,
      """-""": 1,
  }  # Priority of each operator
  __UpperCAmelCase:					int		= len(_lowercase     ) if (len(_lowercase     ) > 7) else 7

  # Print table header for output
  print(
      """Symbol""".center(8     )   ,    """Stack""".center(_lowercase     )   ,    """Postfix""".center(_lowercase     )   ,    sep=""" | """   ,    )
  print("""-""" * (print_width * 3 + 7)     )

  for x in infix:
    if x.isalpha() or x.isdigit():
      post_fix.append(_lowercase     )  # if x is Alphabet / Digit, add it to Postfix
    elif x == "(":
      stack.append(_lowercase     )  # if x is "(" push to Stack
    elif x == ")":  # if x is ")" pop stack until "(" is encountered
      while stack[-1] != "(":
        post_fix.append(stack.pop()     )  # Pop stack & add the content to Postfix
      stack.pop()
    else:
      if len(_lowercase     ) == 0:
        stack.append(_lowercase     )  # If stack is empty, push x to stack
      else:  # while priority of x is not > priority of element in the stack
        while len(_lowercase     ) > 0 and priority[x] <= priority[stack[-1]]:
          post_fix.append(stack.pop()     )  # pop stack & add to Postfix
        stack.append(_lowercase     )  # push x to stack

    print(
        x.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  while len(_lowercase     ) > 0:  # while stack is not empty
    post_fix.append(stack.pop()     )  # pop stack & add to Postfix
    print(
        """ """.center(8     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    ("""""".join(_lowercase     )).ljust(_lowercase     )   ,    sep=""" | """   ,    )  # Output in tabular format

  return "".join(_lowercase     )  # return Postfix as str






def  UpperCamelCase__					(					_lowercase    :				str     )							->	Tuple:
  __UpperCAmelCase:					Union[str, Any]		= list(infix[::-1]     )  # reverse the infix equation

  for i in range(len(_lowercase     )     ):
    if infix[i] == "(":
      __UpperCAmelCase:					Tuple		= """)"""  # change "(" to ")"
    elif infix[i] == ")":
      __UpperCAmelCase:					Union[str, Any]		= """("""  # change ")" to "("

  return (infix_2_postfix("""""".join(_lowercase     )     ))[
      ::-1
  ]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_	   =			input('\nEnter an Infix Equation = ')  # Input an Infix equation
    SCREAMING_SNAKE_CASE_	   =			''.join(Infix.split())  # Remove spaces from the input
    print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')