


import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


UpperCAmelCase__   	=							['''bert-base-uncased''', '''bert-base-cased''']
UpperCAmelCase__   	=							'''hf-internal-testing/tiny-bert-tf-only'''

if is_tf_available():







  class       lowerCamelCase__   (       tf.keras.Model):





       def __init__(self		,     UpperCAmelCase       )     ->   List[Any]:
        super().__init__()
        _lowercase							      =tokenizer
        _lowercase							      =AutoConfig.from_pretrained(UpperCAmelCase       )
        _lowercase							      =TFAutoModel.from_config(UpperCAmelCase       )





       def      __A					(self		,     UpperCAmelCase       )     ->   Union[str, Any]:
        _lowercase							      =self.tokenizer(UpperCAmelCase       )
        _lowercase							      =self.bert(**UpperCAmelCase       )
        return out["pooler_output"]







@require_tf
@require_tensorflow_text
class       lowerCamelCase__   (       unittest.TestCase):





     def      __A					(self       )     ->   int:
      super().setUp()

      _lowercase							      =[
          BertTokenizer.from_pretrained(UpperCAmelCase       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
      ]  # repeat for when fast_bert_tokenizer=false
      _lowercase							      =[TFBertTokenizer.from_pretrained(UpperCAmelCase       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
          TFBertTokenizer.from_pretrained(UpperCAmelCase		,     use_fast_bert_tokenizer=UpperCAmelCase       )
          for checkpoint in TOKENIZER_CHECKPOINTS
      ]
      assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

      _lowercase							      =[
          '''This is a straightforward English test sentence.''',
          '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
          '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
          '''And some much more rare Chinese: 齉 堃 齉堃''',
          '''Je vais aussi écrire en français pour tester les accents''',
          '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
      ]
      _lowercase							      =list(zip(self.test_sentences		,     self.test_sentences[::-1]       )       )





     def      __A					(self       )     ->   int:

      for tokenizer, tf_tokenizer in zip(self.tokenizers		,     self.tf_tokenizers       ):
       for test_inputs in (self.test_sentences, self.paired_sentences):
        _lowercase							      =tokenizer(UpperCAmelCase		,     return_tensors='''tf'''		,     padding='''longest'''       )
        _lowercase							      =tf_tokenizer(UpperCAmelCase       )

        for key in python_outputs.keys():
         self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
         self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]		,     tf.intaa       ) == tf_outputs[key]       )       )





     @slow
     def      __A					(self       )     ->   Union[str, Any]:

      for tf_tokenizer in self.tf_tokenizers:
       _lowercase							      =tf_tokenizer(self.paired_sentences       )
       _lowercase							      =tf_tokenizer(
           text=[sentence[0] for sentence in self.paired_sentences]		,     text_pair=[sentence[1] for sentence in self.paired_sentences]		,     )
       for key in merged_outputs.keys():
        self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]		,     tf.intaa       ) == separated_outputs[key]       )       )





     @slow
     def      __A					(self       )     ->   Optional[int]:

      for tf_tokenizer in self.tf_tokenizers:
       _lowercase							      =tf.function(UpperCAmelCase       )
       for test_inputs in (self.test_sentences, self.paired_sentences):
        _lowercase							      =tf.constant(UpperCAmelCase       )
        _lowercase							      =compiled_tokenizer(UpperCAmelCase       )
        _lowercase							      =tf_tokenizer(UpperCAmelCase       )

        for key in eager_outputs.keys():
         self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )





     @slow
     def      __A					(self       )     ->   str:

      for tf_tokenizer in self.tf_tokenizers:
       _lowercase							      =ModelToSave(tokenizer=UpperCAmelCase       )
       _lowercase							      =tf.convert_to_tensor(self.test_sentences       )
       _lowercase							      =model(UpperCAmelCase       )  # Build model with some sample inputs
       with TemporaryDirectory() as tempdir:
        _lowercase							      =Path(UpperCAmelCase       ) / '''saved.model'''
        model.save(UpperCAmelCase       )
        _lowercase							      =tf.keras.models.load_model(UpperCAmelCase       )
       _lowercase							      =loaded_model(UpperCAmelCase       )
       # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
       self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )		,     1e-5       )







'''simple docstring'''







def 						snake_case_							(_a	:							str    ,   _a	:							str     ):
    UpperCAmelCase			       =		len(_a     ) + 1
    UpperCAmelCase			       =		len(_a     ) + 1

    # dp is a 2d matrix where dp[i][j] denotes whether prefix string of
    # length i of input_string matches with prefix string of length j of
    # given pattern.
    # "dp" stands for dynamic programming.
    UpperCAmelCase			       =		[[0 for i in range(_a     )] for j in range(_a     )]

    # since string of zero length match pattern of zero length
    UpperCAmelCase			       =		1

    # since pattern of zero length will never match with string of non-zero length
    for i in range(1    ,   _a     ):
        UpperCAmelCase			       =		0

    # since string of zero length will match with pattern where there
    # is at least one * alternatively
    for j in range(1    ,   _a     ):
        UpperCAmelCase			       =		dp[0][j - 2] if pattern[j - 1] == '''*''' else 0

    # now using bottom-up approach to find for all remaining lengths
    for i in range(1    ,   _a     ):
        for j in range(1    ,   _a     ):
            if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
                UpperCAmelCase			       =		dp[i - 1][j - 1]

            elif pattern[j - 1] == "*":
                if dp[i][j - 2] == 1:
                    UpperCAmelCase			       =		1
                elif pattern[j - 2] in (input_string[i - 1], "."):
                    UpperCAmelCase			       =		dp[i - 1][j]
                else:
                    UpperCAmelCase			       =		0
            else:
                UpperCAmelCase			       =		0

    return bool(dp[-1][-1]     )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   # inputing the strings
   # input_string = input("input a string :")
   # pattern = input("input a pattern :")

   A     ='aab'
   A     ='c*a*b'

   # using function to check whether given string matches the given pattern
   if match_pattern(input_string, pattern):
      print(f"""{input_string} matches the given pattern {pattern}""")
   else:
      print(f"""{input_string} does not match with the given pattern {pattern}""")




