

from typing import TYPE_CHECKING

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


__a    :Optional[Any]      =			{'configuration_xlnet': ['XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLNetConfig']}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Tuple      =			['XLNetTokenizer']

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Optional[int]      =			['XLNetTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __a    :Optional[Any]      =			[
          '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:
      __a    :int      =			[
          '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

      __a    :int      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


"""simple docstring"""


def   _A					(	_a	:						Optional[int]     ,   _a	:						str				):



      """simple docstring"""






      A				  =		0
      A				  =		len(_a				) - 1

      while left <= right:
            # avoid divided by 0 during interpolation
            if sorted_collection[left] == sorted_collection[right]:
                  if sorted_collection[left] == item:
                        return left
                  else:
                        return None

            A				  =		left + ((item - sorted_collection[left]) * (right - left)) // (
                sorted_collection[right] - sorted_collection[left]
            )

            # out of range check
            if point < 0 or point >= len(_a				):
                  return None

            A				  =		sorted_collection[point]
            if current_item == item:
                  return point
            else:
                  if point < left:
                        A				  =		left
                        A				  =		point
                  elif point > right:
                        A				  =		right
                        A				  =		point
                  else:
                        if item < current_item:
                              A				  =		point - 1
                        else:
                              A				  =		point + 1
      return None



def   _A					(	_a	:						Any     ,   _a	:						List[str]     ,   _a	:						Dict     ,   _a	:						List[str]				):



      """simple docstring"""






      if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                  return left
            else:
                  return None

      A				  =		left + ((item - sorted_collection[left]) * (right - left)) // (
          sorted_collection[right] - sorted_collection[left]
      )

      # out of range check
      if point < 0 or point >= len(_a				):
            return None

      if sorted_collection[point] == item:
            return point
      elif point < left:
            return interpolation_search_by_recursion(_a     ,   _a     ,   _a     ,   _a				)
      elif point > right:
            return interpolation_search_by_recursion(_a     ,   _a     ,   _a     ,   _a				)
      else:
            if sorted_collection[point] > item:
                  return interpolation_search_by_recursion(
                      _a     ,   _a     ,   _a     ,   point - 1				)
            else:
                  return interpolation_search_by_recursion(
                      _a     ,   _a     ,   point + 1     ,   _a				)



def   _A					(	_a	:						List[Any]				):



      """simple docstring"""






      if collection != sorted(_a				):
            raise ValueError("""Collection must be ascending sorted"""				)
      return True


if __name__ == "__main__":
  import sys
  UpperCAmelCase													=0
  if debug == 1:
    UpperCAmelCase													=[10, 30, 40, 45, 50, 66, 77, 93]
    try:
      __assert_sorted(collection)
    except ValueError:
      sys.exit("Sequence must be ascending sorted to apply interpolation search")
    UpperCAmelCase													=67

  UpperCAmelCase													=interpolation_search(collection, target)
  if result is not None:
    print(f"""{target} found at positions: {result}""")
  else:
    print("Not found")




