
from __future__ import annotations







def        A__					(						lowerCamelCase     ,    lowerCamelCase						)  ->	bool:
				UpperCamelCase_:							Union[str, Any]	=						get_failure_array(lowerCamelCase						)

				# 2) Step through text searching for pattern
				UpperCamelCase_:							Union[str, Any]	=						0, 0  # index into text, pattern
				while i < len(lowerCamelCase						):
								if pattern[j] == text[i]:
												if j == (len(lowerCamelCase						) - 1):
																return True
												j += 1

								# if this is a prefix in our pattern
								# just go back far enough to continue
								elif j > 0:
												UpperCamelCase_:							str	=						failure[j - 1]
												continue
								i += 1
				return False







def        A__					(						lowerCamelCase						)  ->	list[int]:
				UpperCamelCase_:							Tuple	=						[0]
				UpperCamelCase_:							List[str]	=						0
				UpperCamelCase_:							Optional[int]	=						1
				while j < len(lowerCamelCase						):
								if pattern[i] == pattern[j]:
												i += 1
								elif i > 0:
												UpperCamelCase_:							Dict	=						failure[i - 1]
												continue
								j += 1
								failure.append(lowerCamelCase						)
				return failure


if __name__ == "__main__":
							# Test 1)
							lowerCamelCase_						: Any         =  """abc1abc12"""
							lowerCamelCase_						: int         =  """alskfjaldsabc1abc1abc12k23adsfabcabc"""
							lowerCamelCase_						: Dict         =  """alskfjaldsk23adsfabcabc"""
							assert kmp(pattern, texta) and not kmp(pattern, texta)

							# Test 2)
							lowerCamelCase_						: str         =  """ABABX"""
							lowerCamelCase_						: List[str]         =  """ABABZABABYABABX"""
							assert kmp(pattern, text)

							# Test 3)
							lowerCamelCase_						: Dict         =  """AAAB"""
							lowerCamelCase_						: int         =  """ABAAAAAB"""
							assert kmp(pattern, text)

							# Test 4)
							lowerCamelCase_						: Optional[int]         =  """abcdabcy"""
							lowerCamelCase_						: str         =  """abcxabcdabxabcdabcdabcy"""
							assert kmp(pattern, text)

							# Test 5)
							lowerCamelCase_						: Tuple         =  """aabaabaaa"""
							assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]








from typing import TYPE_CHECKING

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


lowerCamelCase_						: Optional[Any]         =  {
    """configuration_distilbert""": [
        """DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """DistilBertConfig""",
        """DistilBertOnnxConfig""",
    ],
    """tokenization_distilbert""": ["""DistilBertTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[Any]         =  ["""DistilBertTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Optional[int]         =  [
							    """DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DistilBertForMaskedLM""",
							    """DistilBertForMultipleChoice""",
							    """DistilBertForQuestionAnswering""",
							    """DistilBertForSequenceClassification""",
							    """DistilBertForTokenClassification""",
							    """DistilBertModel""",
							    """DistilBertPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: List[str]         =  [
							    """TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDistilBertForMaskedLM""",
							    """TFDistilBertForMultipleChoice""",
							    """TFDistilBertForQuestionAnswering""",
							    """TFDistilBertForSequenceClassification""",
							    """TFDistilBertForTokenClassification""",
							    """TFDistilBertMainLayer""",
							    """TFDistilBertModel""",
							    """TFDistilBertPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCamelCase_						: Dict         =  [
							    """FlaxDistilBertForMaskedLM""",
							    """FlaxDistilBertForMultipleChoice""",
							    """FlaxDistilBertForQuestionAnswering""",
							    """FlaxDistilBertForSequenceClassification""",
							    """FlaxDistilBertForTokenClassification""",
							    """FlaxDistilBertModel""",
							    """FlaxDistilBertPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_distilbert import (
							    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DistilBertConfig,
							    DistilBertOnnxConfig,
							)
							from .tokenization_distilbert import DistilBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_distilbert_fast import DistilBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_distilbert import (
														    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DistilBertForMaskedLM,
														    DistilBertForMultipleChoice,
														    DistilBertForQuestionAnswering,
														    DistilBertForSequenceClassification,
														    DistilBertForTokenClassification,
														    DistilBertModel,
														    DistilBertPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_distilbert import (
														    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDistilBertForMaskedLM,
														    TFDistilBertForMultipleChoice,
														    TFDistilBertForQuestionAnswering,
														    TFDistilBertForSequenceClassification,
														    TFDistilBertForTokenClassification,
														    TFDistilBertMainLayer,
														    TFDistilBertModel,
														    TFDistilBertPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_distilbert import (
														    FlaxDistilBertForMaskedLM,
														    FlaxDistilBertForMultipleChoice,
														    FlaxDistilBertForQuestionAnswering,
														    FlaxDistilBertForSequenceClassification,
														    FlaxDistilBertForTokenClassification,
														    FlaxDistilBertModel,
														    FlaxDistilBertPreTrainedModel,
														)

else:
							import sys

							lowerCamelCase_						: Optional[Any]         =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






