





"""simple docstring"""



a_			=      256
# Modulus to hash a string
a_			=      1000003







def 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  str						)       ->bool:



    '''simple docstring'''







    __A   :						Optional[int]									=					len(snake_case_						)
    __A   :						Union[str, Any]									=					len(snake_case_						)
    if p_len > t_len:
        return False

    __A   :						Tuple									=					0
    __A   :						List[Any]									=					0
    __A   :						Any									=					1

    # Calculating the hash of pattern and substring of text
    for i in range(snake_case_						):
        __A   :						List[Any]									=					(ord(pattern[i]						) + p_hash * alphabet_size) % modulus
        __A   :						Tuple									=					(ord(text[i]						) + text_hash * alphabet_size) % modulus
        if i == p_len - 1:
            continue
        __A   :						Optional[Any]									=					(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
        __A   :						Dict									=					(
            (text_hash - ord(text[i]						) * modulus_power) * alphabet_size
            + ord(text[i + p_len]						)
        ) % modulus
    return False







def 		__lowercase					(       )       ->None:



    '''simple docstring'''







    __A   :						Union[str, Any]									=					'''abc1abc12'''
    __A   :						Dict									=					'''alskfjaldsabc1abc1abc12k23adsfabcabc'''
    __A   :						Optional[int]									=					'''alskfjaldsk23adsfabcabc'''
    assert rabin_karp(snake_case_    ,snake_case_						) and not rabin_karp(snake_case_    ,snake_case_						)

    # Test 2)
    __A   :						Dict									=					'''ABABX'''
    __A   :						Union[str, Any]									=					'''ABABZABABYABABX'''
    assert rabin_karp(snake_case_    ,snake_case_						)

    # Test 3)
    __A   :						int									=					'''AAAB'''
    __A   :						Dict									=					'''ABAAAAAB'''
    assert rabin_karp(snake_case_    ,snake_case_						)

    # Test 4)
    __A   :						List[str]									=					'''abcdabcy'''
    __A   :						Tuple									=					'''abcxabcdabxabcdabcdabcy'''
    assert rabin_karp(snake_case_    ,snake_case_						)

    # Test 5)
    __A   :						Optional[int]									=					'''Lü'''
    __A   :						Union[str, Any]									=					'''Lüsai'''
    assert rabin_karp(snake_case_    ,snake_case_						)
    __A   :						List[Any]									=					'''Lue'''
    assert not rabin_karp(snake_case_    ,snake_case_						)
    print('''Success.'''						)


if __name__ == "__main__":
       test_rabin_karp()











"""simple docstring"""



from typing import TYPE_CHECKING

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


a_			=      {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""GPTNeoXTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXForCausalLM""",
           """GPTNeoXForQuestionAnswering""",
           """GPTNeoXForSequenceClassification""",
           """GPTNeoXForTokenClassification""",
           """GPTNeoXLayer""",
           """GPTNeoXModel""",
           """GPTNeoXPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox import (
                  GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXForCausalLM,
                  GPTNeoXForQuestionAnswering,
                  GPTNeoXForSequenceClassification,
                  GPTNeoXForTokenClassification,
                  GPTNeoXLayer,
                  GPTNeoXModel,
                  GPTNeoXPreTrainedModel,
              )


else:
       import sys

       a_			=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




