code: 
  
  
  
  
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
     import torch
  
  if is_vision_available():
     from PIL import Image
  
     from transformers import ConvNextImageProcessor
  
  class 					snake_case_							(       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[Any]   ,       snake_case__:	Optional[int]   ,       snake_case__:	List[str]=7   ,       snake_case__:	Optional[Any]=3   ,       snake_case__:	Optional[int]=18   ,       snake_case__:	List[str]=30   ,       snake_case__:	int=400   ,       snake_case__:	str=True   ,       snake_case__:	Any=None   ,       snake_case__:	Any=0.8_75   ,       snake_case__:	str=True   ,       snake_case__:	Optional[int]=[0.5, 0.5, 0.5]   ,       snake_case__:	List[Any]=[0.5, 0.5, 0.5]   ,       )  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	size if size is not None else {'shortest_edge': 20}
         _lowercase     =	parent
         _lowercase     =	batch_size
         _lowercase     =	num_channels
         _lowercase     =	image_size
         _lowercase     =	min_resolution
         _lowercase     =	max_resolution
         _lowercase     =	do_resize
         _lowercase     =	size
         _lowercase     =	crop_pct
         _lowercase     =	do_normalize
         _lowercase     =	image_mean
         _lowercase     =	image_std
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }
  
  
  
  
  
  
  @require_torch
  @require_vision
  class 					snake_case_							(       lowercase_      ,			unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     __lowerCAmelCase      =						ConvNextImageProcessor if is_vision_available() else None
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	ConvNextImageProcessingTester(self	)
  
     @property
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         return self.image_processor_tester.prepare_image_processor_dict()
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         self.assertTrue(hasattr(snake_case__   ,       'do_resize'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'size'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'crop_pct'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'do_normalize'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'image_mean'	)	)
         self.assertTrue(hasattr(snake_case__   ,       'image_std'	)	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	int	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class.from_dict(self.image_processor_dict	)
         self.assertEqual(image_processor.size   ,       {'shortest_edge': 20}	)
  
         _lowercase     =	self.image_processing_class.from_dict(self.image_processor_dict   ,       size=42	)
         self.assertEqual(image_processor.size   ,       {'shortest_edge': 42}	)
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         pass
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random PIL images
         _lowercase     =	prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       Image.Image	)
  
         # Test not batched input
         _lowercase     =	image_processing(image_inputs[0]   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )   ,       )
  
         # Test batched
         _lowercase     =	image_processing(snake_case__   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Union[str, Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random numpy tensors
         _lowercase     =	prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case__   ,       numpify=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       np.ndarray	)
  
         # Test not batched input
         _lowercase     =	image_processing(image_inputs[0]   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )   ,       )
  
         # Test batched
         _lowercase     =	image_processing(snake_case__   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )   ,       )
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]	)  ->		List[str]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.image_processing_class(**self.image_processor_dict	)
         # create random PyTorch tensors
         _lowercase     =	prepare_image_inputs(self.image_processor_tester   ,       equal_resolution=snake_case__   ,       torchify=snake_case__	)
         for image in image_inputs:
             self.assertIsInstance(snake_case__   ,       torch.Tensor	)
  
         # Test not batched input
         _lowercase     =	image_processing(image_inputs[0]   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )   ,       )
  
         # Test batched
         _lowercase     =	image_processing(snake_case__   ,       return_tensors='pt'	).pixel_values
         self.assertEqual(
             encoded_images.shape   ,       (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.size['shortest_edge'],
                 self.image_processor_tester.size['shortest_edge'],
             )   ,       )
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  import sys
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Any   ,       snake_case__:	str   ,       snake_case__:	int	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	letter
         _lowercase     =	freq
         _lowercase     =	{}
  
     def __repr__(    self:	Optional[int]	)  ->		str:
  
  
         """simple docstring"""
  
  
  
         return f'''{self.letter}:{self.freq}'''
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	Optional[int]   ,       snake_case__:	int   ,       snake_case__:	Letter | TreeNode   ,       snake_case__:	Letter | TreeNode	)  ->		List[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	freq
         _lowercase     =	left
         _lowercase     =	right
  
  
  
  
  
  
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	{}
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	f.read(1      )
              if not c:
                  break
              _lowercase     =	chars[c] + 1 if c in chars else 1
      return sorted((Letter(_lowercase,      _lowercase      ) for c, f in chars.items()),      key=lambda _lowercase      : x.freq      )
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	letters  # type: ignore
      while len(_lowercase      ) > 1:
          _lowercase     =	response.pop(0      )
          _lowercase     =	response.pop(0      )
          _lowercase     =	left.freq + right.freq
          _lowercase     =	TreeNode(_lowercase,      _lowercase,      _lowercase      )
          response.append(_lowercase      )
          response.sort(key=lambda _lowercase      : x.freq      )
      return response[0]
  def        UpperCAmelCase      (   _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      if isinstance(_lowercase,      _lowercase      ):
          _lowercase     =	bitstring
          return [root]
      _lowercase     =	root  # type: ignore
      _lowercase     =	[]
      letters += traverse_tree(treenode.left,      bitstring + '0'      )
      letters += traverse_tree(treenode.right,      bitstring + '1'      )
      return letters
  def        UpperCAmelCase      (   _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	parse_file(_lowercase      )
      _lowercase     =	build_tree(_lowercase      )
      _lowercase     =	{
          k: v for letter in traverse_tree(_lowercase,      ''      ) for k, v in letter.bitstring.items()
      }
      print(f'''Huffman Coding  of {file_path}: '''      )
      with open(_lowercase      ) as f:
          while True:
              _lowercase     =	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])
  