





"""simple docstring"""





def 					__A  (_SCREAMING_SNAKE_CASE     )   ->str:





  """simple docstring"""


  lowerCAmelCase__     :List[Any]											=				len(_SCREAMING_SNAKE_CASE     )
  for i in range(length - 1     ):
    lowerCAmelCase__     :Optional[Any]											=				i
    for k in range(i + 1	,  _SCREAMING_SNAKE_CASE     ):
      if collection[k] < collection[least]:
        lowerCAmelCase__     :int											=				k
    if least != i:
      lowerCAmelCase__				,							lowerCAmelCase__     :int											=				(collection[i], collection[least])
  return collection


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


"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'				)

          if text is not None:
              UpperCAmelCase          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if text is not None and images is not None:
              UpperCAmelCase          =       image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_				) ,							tensor_type=lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



