

"""simple docstring"""




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			{
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			int	  =			[
         '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Data2VecAudioForAudioFrameClassification''',
         '''Data2VecAudioForCTC''',
         '''Data2VecAudioForSequenceClassification''',
         '''Data2VecAudioForXVector''',
         '''Data2VecAudioModel''',
         '''Data2VecAudioPreTrainedModel''',
     ]
     SCREAMING_SNAKE_CASE_     :			Optional[int]	  =			[
         '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Data2VecTextForCausalLM''',
         '''Data2VecTextForMaskedLM''',
         '''Data2VecTextForMultipleChoice''',
         '''Data2VecTextForQuestionAnswering''',
         '''Data2VecTextForSequenceClassification''',
         '''Data2VecTextForTokenClassification''',
         '''Data2VecTextModel''',
         '''Data2VecTextPreTrainedModel''',
     ]
     SCREAMING_SNAKE_CASE_     :			Tuple	  =			[
         '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''Data2VecVisionForImageClassification''',
         '''Data2VecVisionForMaskedImageModeling''',
         '''Data2VecVisionForSemanticSegmentation''',
         '''Data2VecVisionModel''',
         '''Data2VecVisionPreTrainedModel''',
     ]

if is_tf_available():
     SCREAMING_SNAKE_CASE_     :			Any	  =			[
         '''TFData2VecVisionForImageClassification''',
         '''TFData2VecVisionForSemanticSegmentation''',
         '''TFData2VecVisionModel''',
         '''TFData2VecVisionPreTrainedModel''',
     ]

if TYPE_CHECKING:
     from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
     from .configuration_dataavec_text import (
         DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DataaVecTextConfig,
         DataaVecTextOnnxConfig,
     )
     from .configuration_dataavec_vision import (
         DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
         DataaVecVisionConfig,
         DataaVecVisionOnnxConfig,
     )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dataavec_audio import (
              DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecAudioForAudioFrameClassification,
              DataaVecAudioForCTC,
              DataaVecAudioForSequenceClassification,
              DataaVecAudioForXVector,
              DataaVecAudioModel,
              DataaVecAudioPreTrainedModel,
          )
          from .modeling_dataavec_text import (
              DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecTextForCausalLM,
              DataaVecTextForMaskedLM,
              DataaVecTextForMultipleChoice,
              DataaVecTextForQuestionAnswering,
              DataaVecTextForSequenceClassification,
              DataaVecTextForTokenClassification,
              DataaVecTextModel,
              DataaVecTextPreTrainedModel,
          )
          from .modeling_dataavec_vision import (
              DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
              DataaVecVisionForImageClassification,
              DataaVecVisionForMaskedImageModeling,
              DataaVecVisionForSemanticSegmentation,
              DataaVecVisionModel,
              DataaVecVisionPreTrainedModel,
          )
     if is_tf_available():
          from .modeling_tf_dataavec_vision import (
              TFDataaVecVisionForImageClassification,
              TFDataaVecVisionForSemanticSegmentation,
              TFDataaVecVisionModel,
              TFDataaVecVisionPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







"""simple docstring"""




from __future__ import annotations

import bisect



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      int:




   """simple docstring"""
   if hi < 0:
      lowerCamelCase__						=			len(A__   )

   while lo < hi:
      lowerCamelCase__						=			lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         lowerCamelCase__						=			mid + 1
      else:
         lowerCamelCase__						=			mid

   return lo



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      int:




   """simple docstring"""
   if hi < 0:
      lowerCamelCase__						=			len(A__   )

   while lo < hi:
      lowerCamelCase__						=			lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         lowerCamelCase__						=			mid + 1
      else:
         lowerCamelCase__						=			mid

   return lo



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      None:




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



def 	UpperCAmelCase__   (				A__	,    A__	,    A__ = 0	,    A__ = -1   )      ->      None:




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



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int | None:




   """simple docstring"""
   lowerCamelCase__						=			0
   lowerCamelCase__						=			len(A__   ) - 1

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



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      int | None:




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



def 	UpperCAmelCase__   (				A__	,    A__	,    A__	,    A__   )      ->      int | None:




   """simple docstring"""
   if right < left:
      return None

   lowerCamelCase__						=			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__":
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			input('''Enter numbers separated by comma:\n''').strip()
     SCREAMING_SNAKE_CASE_     :			int	  =			sorted(int(item) for item in user_input.split(''','''))
     SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			int(input('''Enter a single number to be found in the list:\n'''))
     SCREAMING_SNAKE_CASE_     :			Dict	  =			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}.''')




