
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin



class 		UpperCamelCase__  (	__lowercase   ):
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				["image_processor", "tokenizer"]
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				"AutoImageProcessor"
   _SCREAMING_SNAKE_CASE				:							str     	=				"AutoTokenizer"
   def __init__(self			: Dict ,    snake_case_			: str=None ,    snake_case_			: Union[str, Any]=None ,    **snake_case_			: List[Any]       ):
    __a			:  Optional[int]		    =  None
    if "feature_extractor" in kwargs:
     warnings.warn(
         '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
         ''' instead.''' ,    snake_case_ ,    )
     __a			:  Dict		    =  kwargs.pop('''feature_extractor'''       )

    __a			:  List[Any]		    =  image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
     raise ValueError('''You need to specify an `image_processor`.'''       )
    if tokenizer is None:
     raise ValueError('''You need to specify a `tokenizer`.'''       )

    super().__init__(snake_case_ ,    snake_case_       )
    __a			:  Dict		    =  self.image_processor
    __a			:  str		    =  False
   def __call__(self			: int ,    *snake_case_			: Any ,    **snake_case_			: List[str]       ):
    # For backward compatibility
    if self._in_target_context_manager:
     return self.current_processor(*snake_case_ ,    **snake_case_       )

    __a			:  Optional[Any]		    =  kwargs.pop('''images''' ,    snake_case_       )
    __a			:  List[str]		    =  kwargs.pop('''text''' ,    snake_case_       )
    if len(snake_case_       ) > 0:
     __a			:  Any		    =  args[0]
     __a			:  Tuple		    =  args[1:]

    if images is None and text is None:
     raise ValueError('''You need to specify either an `images` or `text` input to process.'''       )

    if images is not None:
     __a			:  int		    =  self.image_processor(snake_case_ ,    *snake_case_ ,    **snake_case_       )
    if text is not None:
     __a			:  str		    =  self.tokenizer(snake_case_ ,    **snake_case_       )

    if text is None:
     return inputs
    elif images is None:
     return encodings
    else:
     __a			:  Any		    =  encodings['''input_ids''']
     return inputs
   def 				lowerCAmelCase      (self			: Union[str, Any] ,    *snake_case_			: List[str] ,    **snake_case_			: int       ):
    return self.tokenizer.batch_decode(*snake_case_ ,    **snake_case_       )
   def 				lowerCAmelCase      (self			: Any ,    *snake_case_			: Optional[Any] ,    **snake_case_			: Any       ):
    return self.tokenizer.decode(*snake_case_ ,    **snake_case_       )
   @contextmanager
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your images inputs, or in a separate call.'''       )
    __a			:  Union[str, Any]		    =  True
    __a			:  Optional[int]		    =  self.tokenizer
    yield
    __a			:  Optional[Any]		    =  self.image_processor
    __a			:  Optional[Any]		    =  False
   def 				lowerCAmelCase      (self			: int ,    snake_case_			: Optional[int] ,    snake_case_			: Tuple=False ,    snake_case_			: List[str]=None       ):
    if added_vocab is None:
     __a			:  Any		    =  self.tokenizer.get_added_vocab()

    __a			:  Optional[Any]		    =  {}

    while tokens:
     __a			:  Any		    =  re.search(r'''<s_(.*?)>''' ,    snake_case_ ,    re.IGNORECASE       )
     if start_token is None:
      break
     __a			:  List[Any]		    =  start_token.group(1       )
     __a			:  str		    =  re.search(rf"</s_{key}>" ,    snake_case_ ,    re.IGNORECASE       )
     __a			:  Any		    =  start_token.group()
     if end_token is None:
      __a			:  Optional[int]		    =  tokens.replace(snake_case_ ,    ''''''       )
     else:
      __a			:  List[Any]		    =  end_token.group()
      __a			:  Tuple		    =  re.escape(snake_case_       )
      __a			:  Union[str, Any]		    =  re.escape(snake_case_       )
      __a			:  Optional[int]		    =  re.search(f"{start_token_escaped}(.*?){end_token_escaped}" ,    snake_case_ ,    re.IGNORECASE       )
      if content is not None:
       __a			:  Tuple		    =  content.group(1       ).strip()
       if r"<s_" in content and r"</s_" in content:  # non-leaf node
        __a			:  int		    =  self.tokenajson(snake_case_ ,    is_inner_value=snake_case_ ,    added_vocab=snake_case_       )
        if value:
         if len(snake_case_       ) == 1:
          __a			:  str		    =  value[0]
         __a			:  Any		    =  value
       else:  # leaf nodes
        __a			:  Optional[int]		    =  []
        for leaf in content.split(r'''<sep/>'''       ):
         __a			:  Optional[int]		    =  leaf.strip()
         if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
          __a			:  Dict		    =  leaf[1:-2]  # for categorical special tokens
         output[key].append(snake_case_       )
        if len(output[key]       ) == 1:
         __a			:  Tuple		    =  output[key][0]

      __a			:  Union[str, Any]		    =  tokens[tokens.find(snake_case_       ) + len(snake_case_       ) :].strip()
      if tokens[:6] == r"<sep/>":  # non-leaf nodes
       return [output] + self.tokenajson(tokens[6:] ,    is_inner_value=snake_case_ ,    added_vocab=snake_case_       )

    if len(snake_case_       ):
     return [output] if is_inner_value else output
    else:
     return [] if is_inner_value else {"text_sequence": tokens}
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' ,    snake_case_ ,    )
    return self.image_processor_class







   @property
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' ,    snake_case_ ,    )
    return self.image_processor


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
lowercase__          =10




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
  if array[i] == target:
   return i
 return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 __a			:  List[Any]		    =  0
 __a			:  Union[str, Any]		    =  len(lowerCAmelCase__  )
 while left <= right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )

  __a			:  str		    =  (left + right) // 3 + 1
  __a			:  List[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   __a			:  List[str]		    =  one_third - 1
  elif array[two_third] < target:
   __a			:  List[str]		    =  two_third + 1

  else:
   __a			:  Dict		    =  one_third + 1
   __a			:  Any		    =  two_third - 1
 else:
  return -1




def        __UpperCamelCase					(    lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    int  ,       lowerCAmelCase__							:    list[int]  ,       lowerCAmelCase__							:    int  ):
 if left < right:
  if right - left < precision:
   return lin_search(lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  __a			:  Union[str, Any]		    =  (left + right) // 3 + 1
  __a			:  Optional[Any]		    =  2 * (left + right) // 3 + 1

  if array[one_third] == target:
   return one_third
  elif array[two_third] == target:
   return two_third

  elif target < array[one_third]:
   return rec_ternary_search(lowerCAmelCase__  ,       one_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  elif array[two_third] < target:
   return rec_ternary_search(two_third + 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
  else:
   return rec_ternary_search(one_third + 1  ,       two_third - 1  ,       lowerCAmelCase__  ,       lowerCAmelCase__  )
 else:
  return -1


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      lowercase__          =input('Enter numbers separated by comma:\n').strip()
      lowercase__          =[int(item.strip()) for item in user_input.split(',')]
      assert collection == sorted(collection), F"List must be ordered.\n{collection}."
      lowercase__          =int(input('Enter the number to be found in the list:\n').strip())
      lowercase__          =ite_ternary_search(collection, target)
      lowercase__          =rec_ternary_search(0, len(collection) - 1, collection, target)
      if resulta != -1:
            print(F"""Iterative search: {target} found at positions: {resulta}""")
            print(F"""Recursive search: {target} found at positions: {resulta}""")
      else:
            print('Not found')
