

def      A__					(			__A  :						Optional[Any]     ,				__A  :						str			)					->Tuple:
      __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  :						str     ,				__A  :						str     ,				__A  :						Optional[Any]     ,				__A  :						Tuple			)					->List[Any]:

      # 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

      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  :						Optional[Any]			)					->Dict:
      if collection != sorted(__A			):
            raise ValueError('''Collection must be ascending sorted'''			)
      return True


if __name__ == "__main__":
   import sys
   _lowerCamelCase      :					Dict					    =      0
   if debug == 1:
      _lowerCamelCase      :					Optional[Any]					    =      [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''')
      _lowerCamelCase      :					Dict					    =      67

   _lowerCamelCase      :					List[Any]					    =      interpolation_search(collection, target)
   if result is not None:
      print(F"""{target} found at positions: {result}""")
   else:
      print('''Not found''')








'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

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

