



"""simple docstring"""




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Dict						=					['image_processor', 'tokenizer']
      __SCREAMING_SNAKE_CASE		:  int						=					'CLIPImageProcessor'
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					('CLIPTokenizer', 'CLIPTokenizerFast')



      def __init__(	self	:      str	,      A	:      Tuple=None	,      A	:      Union[str, Any]=None	,      **A	:      List[str]		):
           _UpperCAmelCase     :      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."	,      A	,      )
                _UpperCAmelCase     :      Any														=       kwargs.pop("feature_extractor"		)

           _UpperCAmelCase     :      Optional[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__(A	,      A		)



      def __call__(	self	:      Any	,      A	:      Any=None	,      A	:      List[Any]=None	,      A	:      str=None	,      **A	:      Union[str, 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     :      Any														=       self.tokenizer(A	,      return_tensors=A	,      **A		)

           if images is not None:
                _UpperCAmelCase     :      Union[str, Any]														=       self.image_processor(A	,      return_tensors=A	,      **A		)

           if text is not None and images is not None:
                _UpperCAmelCase     :      List[Any]														=       image_features.pixel_values
                return encoding
           elif text is not None:
                return encoding
           else:
                return BatchEncoding(data=dict(**A		)	,      tensor_type=A		)



      def 		snake_case_   (	self	:      Optional[Any]	,      *A	:      Union[str, Any]	,      **A	:      Optional[int]		):
           return self.tokenizer.batch_decode(*A	,      **A		)



      def 		snake_case_   (	self	:      Dict	,      *A	:      Union[str, Any]	,      **A	:      Union[str, Any]		):
           return self.tokenizer.decode(*A	,      **A		)



      @property
      def 		snake_case_   (	self	:      List[Any]		):
           _UpperCAmelCase     :      int														=       self.tokenizer.model_input_names
           _UpperCAmelCase     :      str														=       self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



      @property
      def 		snake_case_   (	self	:      Dict		):
           warnings.warn(
               "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,      A	,      )
           return self.image_processor_class



      @property
      def 		snake_case_   (	self	:      Tuple		):
           warnings.warn(
               "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,      A	,      )
           return self.image_processor





"""simple docstring"""




from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class       UpperCAmelCase_    (							_UpperCamelCase		):



      def __lt__(	self	:      int	,      A	:      Dict		):
           return self[-1] < other[-1]



      def __eq__(	self	:      Union[str, Any]	,      A	:      Union[str, Any]		):
           return self[-1] == other[-1]







def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		list							)    ->  list:





     '''simple docstring'''

     _UpperCAmelCase     :      list[Stack]														=       []
     # sort into stacks
     for element in collection:
          _UpperCAmelCase     :      Any														=       Stack([element]							)
          _UpperCAmelCase     :      str														=       bisect_left(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
          if i != len(SCREAMING_SNAKE_CASE__							):
               stacks[i].append(SCREAMING_SNAKE_CASE__							)
          else:
               stacks.append(SCREAMING_SNAKE_CASE__							)

    # use a heap-based merge to merge stack efficiently
     _UpperCAmelCase     :      Optional[Any]														=       merge(*(reversed(SCREAMING_SNAKE_CASE__							) for stack in stacks)							)
     return collection


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