
from typing import List

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


if is_torch_available():
    import torch




class 	UpperCAmelCase_      (			_A					):


    '''simple docstring'''







    a__   	=		["""image_processor""", """tokenizer"""]
    a__   	=		"""OneFormerImageProcessor"""
    a__   	=		("""CLIPTokenizer""", """CLIPTokenizerFast""")
    def __init__(			self      :			Optional[int]      ,							UpperCamelCase__      :			Optional[Any]=None      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			int = 77      ,							UpperCamelCase__      :			int = 77      ,							**UpperCamelCase__      :			str   )      ->     Tuple:







      """simple docstring"""






      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`."""   )

      __magic_name__    					=	max_seq_length
      __magic_name__    					=	task_seq_length

      super().__init__(UpperCamelCase__      ,							UpperCamelCase__   )
    def 							_lowercase							(			self      :			Tuple      ,							UpperCamelCase__      :			List[str]=None      ,							UpperCamelCase__      :			Any=77   )      ->     List[str]:







      """simple docstring"""






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

      __magic_name__    					=	self.tokenizer(UpperCamelCase__      ,							padding="""max_length"""      ,							max_length=UpperCamelCase__      ,							truncation=UpperCamelCase__   )

      __magic_name__					,   __magic_name__    					=	tokens["""attention_mask"""], tokens["""input_ids"""]

      __magic_name__    					=	[]
      for attn_mask, input_id in zip(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	torch.tensor(UpperCamelCase__   ) * torch.tensor(UpperCamelCase__   )
        token_inputs.append(token.unsqueeze(0   )   )

      __magic_name__    					=	torch.cat(UpperCamelCase__      ,							dim=0   )
      return token_inputs
    def __call__(			self      :			Any      ,							UpperCamelCase__      :			Tuple=None      ,							UpperCamelCase__      :			List[str]=None      ,							UpperCamelCase__      :			Union[str, Any]=None      ,							**UpperCamelCase__      :			Dict   )      ->     int:







      """simple docstring"""






      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."""   )

      __magic_name__    					=	self.image_processor(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__   )

      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[task_inputs]

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

      if hasattr(UpperCamelCase__      ,							"""text_inputs"""   ):
        __magic_name__    					=	encoded_inputs.text_inputs

        __magic_name__    					=	[]
        for texts in texts_list:
          __magic_name__    					=	self._preprocess_text(UpperCamelCase__      ,							max_length=self.max_seq_length   )
          text_inputs.append(text_input_list.unsqueeze(0   )   )

        __magic_name__    					=	torch.cat(UpperCamelCase__      ,							dim=0   )

      return encoded_inputs
    def 							_lowercase							(			self      :			Union[str, Any]      ,							UpperCamelCase__      :			int=None      ,							UpperCamelCase__      :			List[Any]=None      ,							UpperCamelCase__      :			Dict=None      ,							**UpperCamelCase__      :			Any   )      ->     Optional[int]:







      """simple docstring"""






      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."""   )

      __magic_name__    					=	self.image_processor.encode_inputs(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__      ,							**UpperCamelCase__   )

      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[task_inputs]

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

      if hasattr(UpperCamelCase__      ,							"""text_inputs"""   ):
        __magic_name__    					=	encoded_inputs.text_inputs

        __magic_name__    					=	[]
        for texts in texts_list:
          __magic_name__    					=	self._preprocess_text(UpperCamelCase__      ,							max_length=self.max_seq_length   )
          text_inputs.append(text_input_list.unsqueeze(0   )   )

        __magic_name__    					=	torch.cat(UpperCamelCase__      ,							dim=0   )

      return encoded_inputs
    def 							_lowercase							(			self      :			Union[str, Any]      ,							*UpperCamelCase__      :			Dict      ,							**UpperCamelCase__      :			Union[str, Any]   )      ->     Tuple:







      """simple docstring"""






      return self.image_processor.post_process_semantic_segmentation(*UpperCamelCase__      ,							**UpperCamelCase__   )
    def 							_lowercase							(			self      :			Any      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     List[Any]:







      """simple docstring"""






      return self.image_processor.post_process_instance_segmentation(*UpperCamelCase__      ,							**UpperCamelCase__   )





    def 							_lowercase							(			self      :			Tuple      ,							*UpperCamelCase__      :			List[str]      ,							**UpperCamelCase__      :			str   )      ->     Tuple:







      """simple docstring"""






      return self.image_processor.post_process_panoptic_segmentation(*UpperCamelCase__      ,							**UpperCamelCase__   )



class 	UpperCAmelCase_      :


  '''simple docstring'''







  def __init__(			self      :			Tuple      ,							UpperCamelCase__      :			Dict=None   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	data
    __magic_name__    					=	None





  def __repr__(			self      :			Optional[int]   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	""""""
    __magic_name__    					=	self
    while temp:
      string_rep += F'''<{temp.data}> ---> '''
      __magic_name__    					=	temp.next
    string_rep += "<END>"
    return string_rep







def a__   (	A_ ):






  '''simple docstring'''





  if not elements_list:
    raise Exception("""The Elements List is empty""" )

  # Set first element as Head
  __magic_name__    					=	Node(elements_list[0] )
  __magic_name__    					=	head
  # Loop through elements from position 1
  for data in elements_list[1:]:
    __magic_name__    					=	Node(A_ )
    __magic_name__    					=	current.next
  return head


__lowerCAmelCase      :					Union[str, Any]   	=       [1, 3, 5, 32, 44, 12, 43]
print(F'''List: {list_data}''')
print('Creating Linked List from List.')
__lowerCAmelCase      :					Optional[Any]   	=       make_linked_list(list_data)
print('Linked List:')





print(linked_list)

