

'''simple docstring'''






from __future__ import annotations

import bisect




def        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       int  ,		A  :       Optional[int] = 0  ,		A  :       List[str] = -1  )    -> int:







   """simple docstring"""

   if hi < 0:
      __snake_case				:      str     							= len(A  )

   while lo < hi:
      __snake_case				:      Union[str, Any]     							= lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         __snake_case				:      List[Any]     							= mid + 1
      else:
         __snake_case				:      List[Any]     							= mid

   return lo




def        _SCREAMING_SNAKE_CASE  (				A  :       List[Any]  ,		A  :       List[str]  ,		A  :       Dict = 0  ,		A  :       Any = -1  )    -> int:







   """simple docstring"""

   if hi < 0:
      __snake_case				:      Any     							= len(A  )

   while lo < hi:
      __snake_case				:      Union[str, Any]     							= lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         __snake_case				:      str     							= mid + 1
      else:
         __snake_case				:      Any     							= mid

   return lo




def        _SCREAMING_SNAKE_CASE  (				A  :       str  ,		A  :       List[str]  ,		A  :       Union[str, Any] = 0  ,		A  :       List[Any] = -1  )    -> None:







   """simple docstring"""
   sorted_collection.insert(bisect_left(A  ,		A  ,		A  ,		A  )  ,		A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       Dict  ,		A  :       Union[str, Any] = 0  ,		A  :       Union[str, Any] = -1  )    -> None:







   """simple docstring"""
   sorted_collection.insert(bisect_right(A  ,		A  ,		A  ,		A  )  ,		A  )




def        _SCREAMING_SNAKE_CASE  (				A  :       Tuple  ,		A  :       List[Any]  )    -> int | None:







   """simple docstring"""
   __snake_case				:      Union[str, Any]     							= 0
   __snake_case				:      int     							= len(A  ) - 1

   while left <= right:
      __snake_case				:      List[Any]     							= left + (right - left) // 2
      __snake_case				:      Dict     							= sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         __snake_case				:      Dict     							= midpoint - 1
      else:
         __snake_case				:      List[Any]     							= midpoint + 1
   return None




def        _SCREAMING_SNAKE_CASE  (				A  :       List[str]  ,		A  :       Dict  )    -> int | None:







   """simple docstring"""
   __snake_case				:      Tuple     							= bisect.bisect_left(A  ,		A  )
   if index != len(A  ) and sorted_collection[index] == item:
      return index
   return None




def        _SCREAMING_SNAKE_CASE  (				A  :       Dict  ,		A  :       List[Any]  ,		A  :       str  ,		A  :       Optional[Any]  )    -> int | None:







   """simple docstring"""

   if right < left:
      return None

   __snake_case				:      Union[str, Any]     							= left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(A  ,		A  ,		A  ,		midpoint - 1  )
   else:
      return binary_search_by_recursion(A  ,		A  ,		midpoint + 1  ,		A  )


if __name__ == "__main__":
      __A    	=   input('''Enter numbers separated by comma:\n''').strip()
      __A    	=   sorted(int(item) for item in user_input.split(''','''))
      __A    	=   int(input('''Enter a single number to be found in the list:\n'''))
      __A    	=   binary_search(collection, target)
      if result is None:
            print(f'''{target} was not found in {collection}.''')
      else:
            print(f'''{target} was found at position {result} in {collection}.''')







'''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    	=   {
    '''configuration_distilbert''': [
        '''DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''DistilBertConfig''',
        '''DistilBertOnnxConfig''',
    ],
    '''tokenization_distilbert''': ['''DistilBertTokenizer'''],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''DistilBertTokenizerFast''']

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

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''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:
      __A    	=   [
          '''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

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