





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__A	: Union[str, Any]              =       {
    """configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
    """tokenization_roberta""": ["""RobertaTokenizer"""],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	: Tuple              =       ["""RobertaTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	: Optional[Any]              =       [
		    """ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """RobertaForCausalLM""",
		    """RobertaForMaskedLM""",
		    """RobertaForMultipleChoice""",
		    """RobertaForQuestionAnswering""",
		    """RobertaForSequenceClassification""",
		    """RobertaForTokenClassification""",
		    """RobertaModel""",
		    """RobertaPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	: List[Any]              =       [
		    """TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFRobertaForCausalLM""",
		    """TFRobertaForMaskedLM""",
		    """TFRobertaForMultipleChoice""",
		    """TFRobertaForQuestionAnswering""",
		    """TFRobertaForSequenceClassification""",
		    """TFRobertaForTokenClassification""",
		    """TFRobertaMainLayer""",
		    """TFRobertaModel""",
		    """TFRobertaPreTrainedModel""",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	: int              =       [
		    """FlaxRobertaForCausalLM""",
		    """FlaxRobertaForMaskedLM""",
		    """FlaxRobertaForMultipleChoice""",
		    """FlaxRobertaForQuestionAnswering""",
		    """FlaxRobertaForSequenceClassification""",
		    """FlaxRobertaForTokenClassification""",
		    """FlaxRobertaModel""",
		    """FlaxRobertaPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
		from .tokenization_roberta import RobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roberta_fast import RobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roberta import (
				    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RobertaForCausalLM,
				    RobertaForMaskedLM,
				    RobertaForMultipleChoice,
				    RobertaForQuestionAnswering,
				    RobertaForSequenceClassification,
				    RobertaForTokenClassification,
				    RobertaModel,
				    RobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roberta import (
				    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRobertaForCausalLM,
				    TFRobertaForMaskedLM,
				    TFRobertaForMultipleChoice,
				    TFRobertaForQuestionAnswering,
				    TFRobertaForSequenceClassification,
				    TFRobertaForTokenClassification,
				    TFRobertaMainLayer,
				    TFRobertaModel,
				    TFRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roberta import (
				    FlaxRobertaForCausalLM,
				    FlaxRobertaForMaskedLM,
				    FlaxRobertaForMultipleChoice,
				    FlaxRobertaForQuestionAnswering,
				    FlaxRobertaForSequenceClassification,
				    FlaxRobertaForTokenClassification,
				    FlaxRobertaModel,
				    FlaxRobertaPreTrainedModel,
				)

else:
		import sys

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










_lowerCamelCase    :		Optional[Any]		      =    256
# Modulus to hash a string
_lowerCamelCase    :		Optional[Any]		      =    1_000_003

def        __a			(   __lowerCAmelCase      ,	__lowerCAmelCase   )     ->     bool:
					SCREAMING_SNAKE_CASE							:     Tuple						=				len(__lowerCAmelCase   )
					SCREAMING_SNAKE_CASE							:     Tuple						=				len(__lowerCAmelCase   )
					if p_len > t_len:
										return False

					SCREAMING_SNAKE_CASE							:     Dict						=				0
					SCREAMING_SNAKE_CASE							:     Tuple						=				0
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				1

					# Calculating the hash of pattern and substring of text
					for i in range(__lowerCAmelCase   ):
										SCREAMING_SNAKE_CASE							:     Tuple						=				(ord(pattern[i]   ) + p_hash * alphabet_size) % modulus
										SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				(ord(text[i]   ) + text_hash * alphabet_size) % modulus
										if i == p_len - 1:
															continue
										SCREAMING_SNAKE_CASE							:     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
										SCREAMING_SNAKE_CASE							:     int						=				(
										    (text_hash - ord(text[i]   ) * modulus_power) * alphabet_size
										    + ord(text[i + p_len]   )
										) % modulus
					return False

def        __a			(   )     ->     None:
					SCREAMING_SNAKE_CASE							:     Any						=				'abc1abc12'
					SCREAMING_SNAKE_CASE							:     str						=				'alskfjaldsabc1abc1abc12k23adsfabcabc'
					SCREAMING_SNAKE_CASE							:     List[str]						=				'alskfjaldsk23adsfabcabc'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   ) and not rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 2)
					SCREAMING_SNAKE_CASE							:     List[str]						=				'ABABX'
					SCREAMING_SNAKE_CASE							:     int						=				'ABABZABABYABABX'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 3)
					SCREAMING_SNAKE_CASE							:     int						=				'AAAB'
					SCREAMING_SNAKE_CASE							:     Tuple						=				'ABAAAAAB'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

					# Test 4)
					SCREAMING_SNAKE_CASE							:     Tuple						=				'abcdabcy'
					SCREAMING_SNAKE_CASE							:     Optional[Any]						=				'abcxabcdabxabcdabcdabcy'
					assert rabin_karp(__lowerCAmelCase      ,	__lowerCAmelCase   )

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


if __name__ == "__main__":
			test_rabin_karp()