import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class __snake_case    (						lowerCamelCase_		):
   lowerCAmelCase_														=							["image_processor", "tokenizer"]
   lowerCAmelCase_														=							"AutoImageProcessor"
   lowerCAmelCase_														=							"AutoTokenizer"




   def __init__(   self      :    Tuple	,     _lowercase      :    List[str]=None	,     _lowercase      :    Optional[Any]=None	,     **_lowercase      :    Dict				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				None
     if "feature_extractor" in kwargs:
       warnings.warn(
           """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
           """ instead."""	,     _lowercase	,     )
       SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""feature_extractor"""				)

     SCREAMING_SNAKE_CASE__      							=				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__(_lowercase	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				self.image_processor
     SCREAMING_SNAKE_CASE__      							=				False




   def __call__(   self      :    List[Any]	,     *_lowercase      :    List[str]	,     **_lowercase      :    List[str]				):



     """simple docstring"""






     if self._in_target_context_manager:
       return self.current_processor(*_lowercase	,     **_lowercase				)

     SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""images"""	,     _lowercase				)
     SCREAMING_SNAKE_CASE__      							=				kwargs.pop("""text"""	,     _lowercase				)
     if len(_lowercase				) > 0:
       SCREAMING_SNAKE_CASE__      							=				args[0]
       SCREAMING_SNAKE_CASE__      							=				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:
       SCREAMING_SNAKE_CASE__      							=				self.image_processor(_lowercase	,     *_lowercase	,     **_lowercase				)
     if text is not None:
       SCREAMING_SNAKE_CASE__      							=				self.tokenizer(_lowercase	,     **_lowercase				)

     if text is None:
       return inputs
     elif images is None:
       return encodings
     else:
       SCREAMING_SNAKE_CASE__      							=				encodings["""input_ids"""]
       return inputs




   def      __a						(   self      :    List[Any]	,     *_lowercase      :    Optional[Any]	,     **_lowercase      :    Union[str, Any]				):



     """simple docstring"""






     return self.tokenizer.batch_decode(*_lowercase	,     **_lowercase				)




   def      __a						(   self      :    int	,     *_lowercase      :    List[str]	,     **_lowercase      :    str				):



     """simple docstring"""






     return self.tokenizer.decode(*_lowercase	,     **_lowercase				)




   @contextmanager
   def      __a						(   self      :    Optional[int]				):



     """simple docstring"""






     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."""				)
     SCREAMING_SNAKE_CASE__      							=				True
     SCREAMING_SNAKE_CASE__      							=				self.tokenizer
     yield
     SCREAMING_SNAKE_CASE__      							=				self.image_processor
     SCREAMING_SNAKE_CASE__      							=				False




   def      __a						(   self      :    str	,     _lowercase      :    List[str]	,     _lowercase      :    List[str]=False	,     _lowercase      :    List[Any]=None				):



     """simple docstring"""






     if added_vocab is None:
       SCREAMING_SNAKE_CASE__      							=				self.tokenizer.get_added_vocab()

     SCREAMING_SNAKE_CASE__      							=				{}

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

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

     if len(_lowercase				):
       return [output] if is_inner_value else output
     else:
       return [] if is_inner_value else {"text_sequence": tokens}




   @property
   def      __a						(   self      :    Tuple				):



     """simple docstring"""






     warnings.warn(
         """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""	,     _lowercase	,     )
     return self.image_processor_class




   @property
   def      __a						(   self      :    List[Any]				):



     """simple docstring"""






     warnings.warn(
         """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""	,     _lowercase	,     )
     return self.image_processor

def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	Union[str, Any]     )							-> int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__      							=				len(__UpperCamelCase     )
  for i in range(length - 1     ):
    SCREAMING_SNAKE_CASE__      							=				i
    for k in range(i + 1			,					__UpperCamelCase     ):
      if collection[k] < collection[least]:
        SCREAMING_SNAKE_CASE__      							=				k
    if least != i:
      SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				(collection[i], collection[least])
  return collection


if __name__ == "__main__":
   __lowerCamelCase      :			Any							       =		input('''Enter numbers separated by a comma:\n''').strip()
   __lowerCamelCase      :			Dict							       =		[int(item) for item in user_input.split(''',''')]
   print(selection_sort(unsorted))
