




'''simple docstring'''





from typing import List

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


if is_torch_available():
  import torch




class      UpperCamelCase       (	snake_case__       ):
  a     :Tuple          =					['image_processor', 'tokenizer']
  a     :int          =					'OneFormerImageProcessor'
  a     :int          =					('CLIPTokenizer', 'CLIPTokenizerFast')



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase = 77		,     __lowerCAmelCase = 77		,     **__lowerCAmelCase ):







      '''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`.""" )

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

      super().__init__(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=77 ):







      '''simple docstring'''


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

      _a				:							Any							       =      self.tokenizer(__lowerCAmelCase		,     padding="""max_length"""		,     max_length=__lowerCAmelCase		,     truncation=__lowerCAmelCase )

      _a       , _a				:							Optional[Any]							       =      tokens["""attention_mask"""], tokens["""input_ids"""]

      _a				:							str							       =      []
      for attn_mask, input_id in zip(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Tuple							       =      torch.tensor(__lowerCAmelCase ) * torch.tensor(__lowerCAmelCase )
          token_inputs.append(token.unsqueeze(0 ) )

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



  def __call__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''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.""" )

      _a				:							int							       =      self.image_processor(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							List[str]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							List[str]							       =      []
          for task in task_inputs:
              _a				:							Dict							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							str							       =      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""" ):
          _a				:							int							       =      encoded_inputs.text_inputs

          _a				:							Tuple							       =      []
          for texts in texts_list:
              _a				:							int							       =      self._preprocess_text(__lowerCAmelCase		,     max_length=self.max_seq_length )
              text_inputs.append(text_input_list.unsqueeze(0 ) )

          _a				:							Any							       =      torch.cat(__lowerCAmelCase		,     dim=0 )

      return encoded_inputs



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''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.""" )

      _a				:							Dict							       =      self.image_processor.encode_inputs(__lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase		,     **__lowerCAmelCase )

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [task_inputs]

      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) and all(isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) for task_input in task_inputs ):
          _a				:							Dict							       =      []
          for task in task_inputs:
              _a				:							str							       =      f'''the task is {task}'''
              task_token_inputs.append(__lowerCAmelCase )
          _a				:							List[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""" ):
          _a				:							Union[str, Any]							       =      encoded_inputs.text_inputs

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

          _a				:							Dict							       =      torch.cat(__lowerCAmelCase		,     dim=0 )

      return encoded_inputs



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return self.image_processor.post_process_semantic_segmentation(*__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


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



  def     a_							(			self		,     *__lowerCAmelCase		,     **__lowerCAmelCase ):







      '''simple docstring'''


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








'''simple docstring'''





from __future__ import annotations

import sys




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							str							       =      letter
      _a				:							int							       =      freq
      _a				:							dict[str, str]							       =      {}



  def __repr__(			self ):







      '''simple docstring'''


      return f'''{self.letter}:{self.freq}'''






class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							int							       =      freq
      _a				:							Letter | TreeNode							       =      left
      _a				:							Letter | TreeNode							       =      right






def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							dict[str, int]							       =      {}
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      f.read(1						)
            if not c:
                break
            _a				:							Optional[Any]							       =      chars[c] + 1 if c in chars else 1
    return sorted((Letter(UpperCAmelCase_   ,	UpperCAmelCase_						) for c, f in chars.items())   ,	key=lambda UpperCAmelCase_						: x.freq						)





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							list[Letter | TreeNode]							       =      letters  # type: ignore
    while len(UpperCAmelCase_						) > 1:
        _a				:							List[Any]							       =      response.pop(0						)
        _a				:							List[str]							       =      response.pop(0						)
        _a				:							Dict							       =      left.freq + right.freq
        _a				:							Any							       =      TreeNode(UpperCAmelCase_   ,	UpperCAmelCase_   ,	UpperCAmelCase_						)
        response.append(UpperCAmelCase_						)
        response.sort(key=lambda UpperCAmelCase_						: x.freq						)
    return response[0]





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_						):
    if isinstance(UpperCAmelCase_   ,	UpperCAmelCase_						):
        _a				:							int							       =      bitstring
        return [root]
    _a				:							TreeNode							       =      root  # type: ignore
    _a				:							Optional[Any]							       =      []
    letters += traverse_tree(treenode.left   ,	bitstring + """0"""						)
    letters += traverse_tree(treenode.right   ,	bitstring + """1"""						)
    return letters





def UpperCAmelCase    (       UpperCAmelCase_						):
    _a				:							Optional[Any]							       =      parse_file(UpperCAmelCase_						)
    _a				:							Tuple							       =      build_tree(UpperCAmelCase_						)
    _a				:							Union[str, Any]							       =      {
        k: v for letter in traverse_tree(UpperCAmelCase_   ,	""""""						) for k, v in letter.bitstring.items()
    }
    print(F'''Huffman Coding  of {file_path}: '''						)
    with open(UpperCAmelCase_						) as f:
        while True:
            _a				:							Any							       =      f.read(1						)
            if not c:
                break
            print(letters[c]   ,	end=""" """						)
    print()


if __name__ == "__main__":
  # pass the file path to the huffman function
  huffman(sys.argv[1])


