




'''simple docstring'''






from typing import List

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


if is_torch_available():
      import torch






class 					lowercase_			(	__lowerCamelCase       ):
     A_       							= ['image_processor', 'tokenizer']
     A_       							= 'OneFormerImageProcessor'
     A_       							= ('CLIPTokenizer', 'CLIPTokenizerFast')





     def __init__(    self    :				int    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				List[str]=None    ,       __lowerCamelCase    :				int = 77    ,       __lowerCamelCase    :				int = 77    ,       **__lowerCamelCase    :				Optional[int] ):






           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`.' )

           snake_case__					:      Optional[int]  			=						max_seq_length
           snake_case__					:      Tuple  			=						task_seq_length

           super().__init__(__lowerCamelCase    ,       __lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[Any]    ,       __lowerCamelCase    :				Union[str, Any]=None    ,       __lowerCamelCase    :				Any=77 ):






           if text_list is None:
                 raise ValueError('tokens cannot be None.' )

           snake_case__					:      str  			=						self.tokenizer(__lowerCamelCase    ,       padding='max_length'    ,       max_length=__lowerCamelCase    ,       truncation=__lowerCamelCase )

           snake_case__			,       snake_case__					:      Dict  			=						tokens['attention_mask'], tokens['input_ids']

           snake_case__					:      List[str]  			=						[]
           for attn_mask, input_id in zip(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      Union[str, Any]  			=						torch.tensor(__lowerCamelCase ) * torch.tensor(__lowerCamelCase )
                 token_inputs.append(token.unsqueeze(0 ) )

           snake_case__					:      Dict  			=						torch.cat(__lowerCamelCase    ,       dim=0 )
           return token_inputs





     def __call__(    self    :				int    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Tuple=None    ,       **__lowerCamelCase    :				str ):






           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.' )

           snake_case__					:      List[Any]  			=						self.image_processor(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       **__lowerCamelCase )

           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      List[Any]  			=						[task_inputs]

           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ) and all(isinstance(__lowerCamelCase    ,       __lowerCamelCase ) for task_input in task_inputs ):
                 snake_case__					:      str  			=						[]
                 for task in task_inputs:
                       snake_case__					:      Any  			=						F"the task is {task}"
                       task_token_inputs.append(__lowerCamelCase )
                 snake_case__					:      Optional[int]  			=						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' ):
                 snake_case__					:      List[Any]  			=						encoded_inputs.text_inputs

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

                 snake_case__					:      Union[str, Any]  			=						torch.cat(__lowerCamelCase    ,       dim=0 )

           return encoded_inputs





     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				Optional[int]=None    ,       __lowerCamelCase    :				Tuple=None    ,       __lowerCamelCase    :				Any=None    ,       **__lowerCamelCase    :				str ):






           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.' )

           snake_case__					:      Optional[Any]  			=						self.image_processor.encode_inputs(__lowerCamelCase    ,       __lowerCamelCase    ,       __lowerCamelCase    ,       **__lowerCamelCase )

           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ):
                 snake_case__					:      Union[str, Any]  			=						[task_inputs]

           if isinstance(__lowerCamelCase    ,       __lowerCamelCase ) and all(isinstance(__lowerCamelCase    ,       __lowerCamelCase ) for task_input in task_inputs ):
                 snake_case__					:      Any  			=						[]
                 for task in task_inputs:
                       snake_case__					:      str  			=						F"the task is {task}"
                       task_token_inputs.append(__lowerCamelCase )
                 snake_case__					:      Dict  			=						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' ):
                 snake_case__					:      Tuple  			=						encoded_inputs.text_inputs

                 snake_case__					:      List[Any]  			=						[]
                 for texts in texts_list:
                       snake_case__					:      List[Any]  			=						self._preprocess_text(__lowerCamelCase    ,       max_length=self.max_seq_length )
                       text_inputs.append(text_input_list.unsqueeze(0 ) )

                 snake_case__					:      str  			=						torch.cat(__lowerCamelCase    ,       dim=0 )

           return encoded_inputs





     def  _lowerCAmelCase			(    self    :				List[str]    ,       *__lowerCamelCase    :				Any    ,       **__lowerCamelCase    :				Optional[Any] ):
           return self.image_processor.post_process_semantic_segmentation(*__lowerCamelCase    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				Optional[Any]    ,       *__lowerCamelCase    :				Union[str, Any]    ,       **__lowerCamelCase    :				List[str] ):
           return self.image_processor.post_process_instance_segmentation(*__lowerCamelCase    ,       **__lowerCamelCase )





     def  _lowerCAmelCase			(    self    :				List[str]    ,       *__lowerCamelCase    :				int    ,       **__lowerCamelCase    :				List[str] ):
           return self.image_processor.post_process_panoptic_segmentation(*__lowerCamelCase    ,       **__lowerCamelCase )






'''simple docstring'''






class 					lowercase_			:





     def __init__(    self    :				Any ):
           snake_case__					:      List[str]  			=						[0]
           snake_case__					:      Optional[Any]  			=						0





     def  _lowerCAmelCase			(    self    :				List[str]    ,       __lowerCamelCase    :				int ):
           snake_case__					:      Optional[int]  			=						self.__heap[i]
           while i // 2 > 0:
                 if self.__heap[i] > self.__heap[i // 2]:
                       snake_case__					:      Union[str, Any]  			=						self.__heap[i // 2]
                       snake_case__					:      Union[str, Any]  			=						temporary
                 i //= 2





     def  _lowerCAmelCase			(    self    :				int    ,       __lowerCamelCase    :				int ):
           self.__heap.append(__lowerCamelCase )
           self.__size += 1
           self.__swap_up(self.__size )





     def  _lowerCAmelCase			(    self    :				Dict    ,       __lowerCamelCase    :				int ):
           while self.__size >= 2 * i:
                 if 2 * i + 1 > self.__size:
                       snake_case__					:      List[str]  			=						2 * i
                 else:
                       if self.__heap[2 * i] > self.__heap[2 * i + 1]:
                             snake_case__					:      Any  			=						2 * i
                       else:
                             snake_case__					:      Tuple  			=						2 * i + 1
                 snake_case__					:      int  			=						self.__heap[i]
                 if self.__heap[i] < self.__heap[bigger_child]:
                       snake_case__					:      Tuple  			=						self.__heap[bigger_child]
                       snake_case__					:      int  			=						temporary
                 snake_case__					:      int  			=						bigger_child





     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           snake_case__					:      Any  			=						self.__heap[1]
           snake_case__					:      List[str]  			=						self.__heap[self.__size]
           self.__size -= 1
           self.__heap.pop()
           self.__swap_down(1 )
           return max_value





     @property
     def  _lowerCAmelCase			(    self    :				Union[str, Any] ):
           return self.__heap[1:]





     def __len__(    self    :				int ):
           return self.__size


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      # create an instance of BinaryHeap
      A_ 			=		BinaryHeap()
      binary_heap.insert(6)
      binary_heap.insert(10)
      binary_heap.insert(15)
      binary_heap.insert(12)
      # pop root(max-values because it is max heap)
      print(binary_heap.pop())  # 15
      print(binary_heap.pop())  # 12
      # get the list and size after operations
      print(binary_heap.get_list)
      print(len(binary_heap))
