



'''simple docstring'''





A__    :   Union[str, Any]           =					2_5_6
# Modulus to hash a string
A__    :   List[str]           =					1_0_0_0_0_0_3





def   a_   (		_UpperCAmelCase    :      str ,_UpperCAmelCase    :      str				)    ->  bool:
      __snake_case :   Optional[Any]													=    len(_UpperCAmelCase				)
      __snake_case :   Any													=    len(_UpperCAmelCase				)
      if p_len > t_len:
            return False

      __snake_case :   str													=    0
      __snake_case :   List[str]													=    0
      __snake_case :   Dict													=    1

      # Calculating the hash of pattern and substring of text
      for i in range(_UpperCAmelCase				):
            __snake_case :   int													=    (ord(pattern[i]				) + p_hash * alphabet_size) % modulus
            __snake_case :   List[str]													=    (ord(text[i]				) + text_hash * alphabet_size) % modulus
            if i == p_len - 1:
                  continue
            __snake_case :   List[str]													=    (modulus_power * alphabet_size) % modulus

      for i in range(0 ,t_len - p_len + 1				):
            if text_hash == p_hash and text[i : i + p_len] == pattern:
                  return True
            if i == t_len - p_len:
                  continue
            # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
            __snake_case :   List[str]													=    (
                (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
                + ord(text[i + p_len]				)
            ) % modulus
      return False





def   a_   (		)    ->  None:
      __snake_case :   Optional[Any]													=    'abc1abc12'
      __snake_case :   Any													=    'alskfjaldsabc1abc1abc12k23adsfabcabc'
      __snake_case :   List[str]													=    'alskfjaldsk23adsfabcabc'
      assert rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				) and not rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				)

      # Test 2)
      __snake_case :   List[str]													=    'ABABX'
      __snake_case :   Optional[Any]													=    'ABABZABABYABABX'
      assert rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				)

      # Test 3)
      __snake_case :   List[Any]													=    'AAAB'
      __snake_case :   int													=    'ABAAAAAB'
      assert rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				)

      # Test 4)
      __snake_case :   Optional[int]													=    'abcdabcy'
      __snake_case :   str													=    'abcxabcdabxabcdabcdabcy'
      assert rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				)

      # Test 5)
      __snake_case :   Any													=    'Lü'
      __snake_case :   Optional[int]													=    'Lüsai'
      assert rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				)
      __snake_case :   Dict													=    'Lue'
      assert not rabin_karp(_UpperCAmelCase ,_UpperCAmelCase				)
      print('Success.'				)


if __name__ == "__main__":
    test_rabin_karp()








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


A__    :   Optional[Any]           =					{
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   Tuple           =					['''SqueezeBertTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A__    :   str           =					[
        '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''SqueezeBertForMaskedLM''',
        '''SqueezeBertForMultipleChoice''',
        '''SqueezeBertForQuestionAnswering''',
        '''SqueezeBertForSequenceClassification''',
        '''SqueezeBertForTokenClassification''',
        '''SqueezeBertModel''',
        '''SqueezeBertModule''',
        '''SqueezeBertPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_squeezebert import (
        SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SqueezeBertConfig,
        SqueezeBertOnnxConfig,
    )
    from .tokenization_squeezebert import SqueezeBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_squeezebert import (
            SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            SqueezeBertForMaskedLM,
            SqueezeBertForMultipleChoice,
            SqueezeBertForQuestionAnswering,
            SqueezeBertForSequenceClassification,
            SqueezeBertForTokenClassification,
            SqueezeBertModel,
            SqueezeBertModule,
            SqueezeBertPreTrainedModel,
        )

else:
    import sys

    A__    :   List[Any]           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



