





"""simple docstring"""





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
    import torch





class       snake_case		(       __UpperCAmelCase       ):
      lowerCamelCase__      =			['''image_processor''', '''tokenizer''']
      lowerCamelCase__      =			'''OneFormerImageProcessor'''
      lowerCamelCase__      =			('''CLIPTokenizer''', '''CLIPTokenizerFast''')

      def __init__(      self       :Tuple						,							_lowerCamelCase       :Tuple=None						,							_lowerCamelCase       :Tuple=None						,							_lowerCamelCase       :int = 7_7						,							_lowerCamelCase       :int = 7_7						,							**_lowerCamelCase       :Tuple						):
       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`.'''						)

       __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	max_seq_length
       __SCREAMING_SNAKE_CASE			:      Any 							=	task_seq_length

       super().__init__(_lowerCamelCase						,							_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						,							_lowerCamelCase       :Optional[int]=None						,							_lowerCamelCase       :str=7_7						):
       if text_list is None:
        raise ValueError('''tokens cannot be None.'''						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.tokenizer(_lowerCamelCase						,							padding='''max_length'''						,							max_length=_lowerCamelCase						,							truncation=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      Optional[int] 							=	tokens['''attention_mask'''], tokens['''input_ids''']

       __SCREAMING_SNAKE_CASE			:      int 							=	[]
       for attn_mask, input_id in zip(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Dict 							=	torch.tensor(_lowerCamelCase						) * torch.tensor(_lowerCamelCase						)
        token_inputs.append(token.unsqueeze(0						)						)

       __SCREAMING_SNAKE_CASE			:      List[str] 							=	torch.cat(_lowerCamelCase						,							dim=0						)
       return token_inputs

      def __call__(      self       :int						,							_lowerCamelCase       :Tuple=None						,							_lowerCamelCase       :Tuple=None						,							_lowerCamelCase       :Union[str, Any]=None						,							**_lowerCamelCase       :Any						):

       if task_inputs is None:
        raise ValueError('''You have to specify the task_input. Found None.'''						)
       elif images is None:
        raise ValueError('''You have to specify the image. Found None.'''						)

       if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs						):
        raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''						)

       __SCREAMING_SNAKE_CASE			:      Dict 							=	self.image_processor(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							**_lowerCamelCase						)

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Any 							=	[task_inputs]

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						) and all(isinstance(_lowerCamelCase						,							_lowerCamelCase						) for task_input in task_inputs						):
        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	[]
        for task in task_inputs:
         __SCREAMING_SNAKE_CASE			:      Tuple 							=	f'''the task is {task}'''
         task_token_inputs.append(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Any 							=	self._preprocess_text(_lowerCamelCase						,							max_length=self.task_seq_length						)
       else:
        raise TypeError('''Task Inputs should be a string or a list of strings.'''						)

       if hasattr(_lowerCamelCase						,							'''text_inputs'''						):
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	encoded_inputs.text_inputs

        __SCREAMING_SNAKE_CASE			:      Optional[int] 							=	[]
        for texts in texts_list:
         __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	self._preprocess_text(_lowerCamelCase						,							max_length=self.max_seq_length						)
         text_inputs.append(text_input_list.unsqueeze(0						)						)

        __SCREAMING_SNAKE_CASE			:      Any 							=	torch.cat(_lowerCamelCase						,							dim=0						)

       return encoded_inputs

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						,							_lowerCamelCase       :int=None						,							_lowerCamelCase       :Any=None						,							_lowerCamelCase       :List[str]=None						,							**_lowerCamelCase       :Any						):

       if task_inputs is None:
        raise ValueError('''You have to specify the task_input. Found None.'''						)
       elif images is None:
        raise ValueError('''You have to specify the image. Found None.'''						)

       if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs						):
        raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''						)

       __SCREAMING_SNAKE_CASE			:      List[Any] 							=	self.image_processor.encode_inputs(_lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase						,							**_lowerCamelCase						)

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	[task_inputs]

       if isinstance(_lowerCamelCase						,							_lowerCamelCase						) and all(isinstance(_lowerCamelCase						,							_lowerCamelCase						) for task_input in task_inputs						):
        __SCREAMING_SNAKE_CASE			:      List[str] 							=	[]
        for task in task_inputs:
         __SCREAMING_SNAKE_CASE			:      str 							=	f'''the task is {task}'''
         task_token_inputs.append(_lowerCamelCase						)
        __SCREAMING_SNAKE_CASE			:      Any 							=	self._preprocess_text(_lowerCamelCase						,							max_length=self.task_seq_length						)
       else:
        raise TypeError('''Task Inputs should be a string or a list of strings.'''						)

       if hasattr(_lowerCamelCase						,							'''text_inputs'''						):
        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	encoded_inputs.text_inputs

        __SCREAMING_SNAKE_CASE			:      Any 							=	[]
        for texts in texts_list:
         __SCREAMING_SNAKE_CASE			:      Dict 							=	self._preprocess_text(_lowerCamelCase						,							max_length=self.max_seq_length						)
         text_inputs.append(text_input_list.unsqueeze(0						)						)

        __SCREAMING_SNAKE_CASE			:      List[Any] 							=	torch.cat(_lowerCamelCase						,							dim=0						)

       return encoded_inputs

      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							*_lowerCamelCase       :List[str]						,							**_lowerCamelCase       :Tuple						):
       return self.image_processor.post_process_semantic_segmentation(*_lowerCamelCase						,							**_lowerCamelCase						)

      def 						SCREAMING_SNAKE_CASE_      (      self       :Optional[int]						,							*_lowerCamelCase       :Tuple						,							**_lowerCamelCase       :Optional[Any]						):
       return self.image_processor.post_process_instance_segmentation(*_lowerCamelCase						,							**_lowerCamelCase						)



      def 						SCREAMING_SNAKE_CASE_      (      self       :List[str]						,							*_lowerCamelCase       :str						,							**_lowerCamelCase       :str						):
       return self.image_processor.post_process_panoptic_segmentation(*_lowerCamelCase						,							**_lowerCamelCase						)








"""simple docstring"""





def 		lowerCAmelCase_     (  lowercase_       :							Tuple			,				lowercase_       :							Any			,				lowercase_       :							List[str]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Any 							=	index
 __SCREAMING_SNAKE_CASE			:      Union[str, Any] 							=	2 * index + 1
 __SCREAMING_SNAKE_CASE			:      Any 							=	2 * index + 2
 if left_index < heap_size and unsorted[left_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      str 							=	left_index

 if right_index < heap_size and unsorted[right_index] > unsorted[largest]:
  __SCREAMING_SNAKE_CASE			:      Optional[Any] 							=	right_index

 if largest != index:
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[str] 							=	unsorted[index], unsorted[largest]
  heapify(lowercase_			,				lowercase_			,				lowercase_		)




def 		lowerCAmelCase_     (  lowercase_       :							List[Any]		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE			:      Dict 							=	len(lowercase_		)
 for i in range(n // 2 - 1			,				-1			,				-1		):
  heapify(lowercase_			,				lowercase_			,				lowercase_		)
 for i in range(n - 1			,				0			,				-1		):
  __SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE			:      List[Any] 							=	unsorted[i], unsorted[0]
  heapify(lowercase_			,				0			,				lowercase_		)
 return unsorted


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

