"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__     						=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__     						=  ["""XLNetTokenizer"""]

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

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__     						=  [
        """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """XLNetForMultipleChoice""",
        """XLNetForQuestionAnswering""",
        """XLNetForQuestionAnsweringSimple""",
        """XLNetForSequenceClassification""",
        """XLNetForTokenClassification""",
        """XLNetLMHeadModel""",
        """XLNetModel""",
        """XLNetPreTrainedModel""",
        """load_tf_weights_in_xlnet""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__     						=  [
        """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFXLNetForMultipleChoice""",
        """TFXLNetForQuestionAnsweringSimple""",
        """TFXLNetForSequenceClassification""",
        """TFXLNetForTokenClassification""",
        """TFXLNetLMHeadModel""",
        """TFXLNetMainLayer""",
        """TFXLNetModel""",
        """TFXLNetPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlnet import XLNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_xlnet_fast import XLNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xlnet import (
            XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            XLNetForMultipleChoice,
            XLNetForQuestionAnswering,
            XLNetForQuestionAnsweringSimple,
            XLNetForSequenceClassification,
            XLNetForTokenClassification,
            XLNetLMHeadModel,
            XLNetModel,
            XLNetPreTrainedModel,
            load_tf_weights_in_xlnet,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_xlnet import (
            TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFXLNetForMultipleChoice,
            TFXLNetForQuestionAnsweringSimple,
            TFXLNetForSequenceClassification,
            TFXLNetForTokenClassification,
            TFXLNetLMHeadModel,
            TFXLNetMainLayer,
            TFXLNetModel,
            TFXLNetPreTrainedModel,
        )

else:
    import sys

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

"""simple docstring"""


from __future__ import annotations






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_failure_array(lowercase						)

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

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






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[0]
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					1
  while j < len(lowercase						):
    if pattern[i] == pattern[j]:
      i += 1
    elif i > 0:
      _UpperCAmelCase         =					failure[i - 1]
      continue
    j += 1
    failure.append(lowercase						)
  return failure


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

    # Test 2)
    UpperCAmelCase__     						=  """ABABX"""
    UpperCAmelCase__     						=  """ABABZABABYABABX"""
    assert kmp(pattern, text)

    # Test 3)
    UpperCAmelCase__     						=  """AAAB"""
    UpperCAmelCase__     						=  """ABAAAAAB"""
    assert kmp(pattern, text)

    # Test 4)
    UpperCAmelCase__     						=  """abcdabcy"""
    UpperCAmelCase__     						=  """abcxabcdabxabcdabcdabcy"""
    assert kmp(pattern, text)

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