




"""simple docstring"""



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


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import CLIPImageProcessor




class       UpperCAmelCase_    (		unittest.TestCase				):




    def __init__(       self		:			Optional[int]      ,					snake_case_		:			Optional[int]      ,					snake_case_		:			int=7      ,					snake_case_		:			List[Any]=3      ,					snake_case_		:			Union[str, Any]=18      ,					snake_case_		:			Dict=30      ,					snake_case_		:			Optional[int]=400      ,					snake_case_		:			str=True      ,					snake_case_		:			str=None      ,					snake_case_		:			List[str]=True      ,					snake_case_		:			Optional[Any]=None      ,					snake_case_		:			Optional[Any]=True      ,					snake_case_		:			List[str]=[0.4814_5466, 0.457_8275, 0.4082_1073]      ,					snake_case_		:			Optional[int]=[0.2686_2954, 0.2613_0258, 0.2757_7711]      ,					snake_case_		:			Any=True      ,					) ->				Optional[int]:




     '''simple docstring'''

     A__			=							size if size is not None else {"shortest_edge": 20}
     A__			=							crop_size if crop_size is not None else {"height": 18, "width": 18}
     A__			=							parent
     A__			=							batch_size
     A__			=							num_channels
     A__			=							image_size
     A__			=							min_resolution
     A__			=							max_resolution
     A__			=							do_resize
     A__			=							size
     A__			=							do_center_crop
     A__			=							crop_size
     A__			=							do_normalize
     A__			=							image_mean
     A__			=							image_std
     A__			=							do_convert_rgb




    def 				__magic_name__				(       self		:			List[Any] ) ->				List[str]:




     '''simple docstring'''

     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_center_crop": self.do_center_crop,
         "crop_size": self.crop_size,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_convert_rgb": self.do_convert_rgb,
     }




    def 				__magic_name__				(       self		:			Optional[Any]      ,					snake_case_		:			Optional[Any]=False      ,					snake_case_		:			List[Any]=False      ,					snake_case_		:			Dict=False ) ->				Tuple:




     '''simple docstring'''

     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     if equal_resolution:
      A__			=							[]
      for i in range(self.batch_size ):
       image_inputs.append(
           np.random.randint(
               255      ,					size=(self.num_channels, self.max_resolution, self.max_resolution)      ,					dtype=np.uinta ) )
     else:
      A__			=							[]
      for i in range(self.batch_size ):
       A__, A__			=							np.random.choice(np.arange(self.min_resolution      ,					self.max_resolution )      ,					2 )
       image_inputs.append(np.random.randint(255      ,					size=(self.num_channels, width, height)      ,					dtype=np.uinta ) )

     if not numpify and not torchify:
      # PIL expects the channel dimension as last dimension
      A__			=							[Image.fromarray(np.moveaxis(snake_case_      ,					0      ,					-1 ) ) for x in image_inputs]

     if torchify:
      A__			=							[torch.from_numpy(snake_case_ ) for x in image_inputs]

     return image_inputs






@require_torch
@require_vision
class       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = CLIPImageProcessor if is_vision_available() else None




    def 				__magic_name__				(       self		:			List[Any] ) ->				Union[str, Any]:




     '''simple docstring'''

     A__			=							CLIPImageProcessingTester(self )




    @property
    def 				__magic_name__				(       self		:			Dict ) ->				List[Any]:




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




    def 				__magic_name__				(       self		:			Dict ) ->				Any:




     '''simple docstring'''

     A__			=							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_      ,					"do_center_crop" ) )
     self.assertTrue(hasattr(snake_case_      ,					"center_crop" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_normalize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_mean" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_std" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_convert_rgb" ) )




    def 				__magic_name__				(       self		:			List[Any] ) ->				str:




     '''simple docstring'''

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size      ,					{"shortest_edge": 20} )
     self.assertEqual(image_processor.crop_size      ,					{"height": 18, "width": 18} )

     A__			=							self.image_processing_class.from_dict(self.image_processor_dict      ,					size=42      ,					crop_size=84 )
     self.assertEqual(image_processor.size      ,					{"shortest_edge": 42} )
     self.assertEqual(image_processor.crop_size      ,					{"height": 84, "width": 84} )




    def 				__magic_name__				(       self		:			Dict ) ->				Any:




     '''simple docstring'''

     pass




    def 				__magic_name__				(       self		:			str ) ->				Tuple:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     A__			=							self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					Image.Image )

     # Test not batched input
     A__			=							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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)




    def 				__magic_name__				(       self		:			Dict ) ->				Optional[int]:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     A__			=							self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_      ,					numpify=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					np.ndarray )

     # Test not batched input
     A__			=							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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)




    def 				__magic_name__				(       self		:			List[str] ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     A__			=							self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_      ,					torchify=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					torch.Tensor )

     # Test not batched input
     A__			=							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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							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.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)






@require_torch
@require_vision
class       UpperCAmelCase_    (		A_,  unittest.TestCase				):
    lowercase__		   = CLIPImageProcessor if is_vision_available() else None




    def 				__magic_name__				(       self		:			List[Any] ) ->				List[Any]:




     '''simple docstring'''

     A__			=							CLIPImageProcessingTester(self      ,					num_channels=4 )
     A__			=							3




    @property
    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[Any]:




     '''simple docstring'''

     return self.image_processor_tester.prepare_image_processor_dict()




    def 				__magic_name__				(       self		:			Tuple ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							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_      ,					"do_center_crop" ) )
     self.assertTrue(hasattr(snake_case_      ,					"center_crop" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_normalize" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_mean" ) )
     self.assertTrue(hasattr(snake_case_      ,					"image_std" ) )
     self.assertTrue(hasattr(snake_case_      ,					"do_convert_rgb" ) )




    def 				__magic_name__				(       self		:			List[str] ) ->				Any:




     '''simple docstring'''

     pass




    def 				__magic_name__				(       self		:			List[Any] ) ->				Dict:




     '''simple docstring'''

     A__			=							self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     A__			=							self.image_processor_tester.prepare_inputs(equal_resolution=snake_case_ )
     for image in image_inputs:
      self.assertIsInstance(snake_case_      ,					Image.Image )

     # Test not batched input
     A__			=							image_processing(image_inputs[0]      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             1,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)

     # Test batched
     A__			=							image_processing(snake_case_      ,					return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape      ,					(
             self.image_processor_tester.batch_size,
             self.expected_encoded_image_num_channels,
             self.image_processor_tester.crop_size["height"],
             self.image_processor_tester.crop_size["width"],
         )      ,					)











"""simple docstring"""



from __future__ import annotations

import sys




class       UpperCAmelCase_    :




    def __init__(       self		:			Union[str, Any]      ,					snake_case_		:			str      ,					snake_case_		:			int ) ->				int:




     '''simple docstring'''

     A__			=							letter
     A__			=							freq
     A__			=							{}




    def __repr__(       self		:			Union[str, Any] ) ->				str:




     '''simple docstring'''

     return F"""{self.letter}:{self.freq}"""






class       UpperCAmelCase_    :




    def __init__(       self		:			Dict      ,					snake_case_		:			int      ,					snake_case_		:			Letter | TreeNode      ,					snake_case_		:			Letter | TreeNode ) ->				Optional[Any]:




     '''simple docstring'''

     A__			=							freq
     A__			=							left
     A__			=							right







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						list[Letter]:
 A__			=							{}
 with open(lowercase_		) as f:
  while True:
   A__			=							f.read(1		)
   if not c:
    break
   A__			=							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     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						Letter | TreeNode:
 A__			=							letters  # type: ignore
 while len(lowercase_		) > 1:
  A__			=							response.pop(0		)
  A__			=							response.pop(0		)
  A__			=							left.freq + right.freq
  A__			=							TreeNode(lowercase_     ,					lowercase_     ,					lowercase_		)
  response.append(lowercase_		)
  response.sort(key=lambda lowercase_		: x.freq		)
 return response[0]







def     _SCREAMING_SNAKE_CASE  (				lowercase_     ,					lowercase_		)		->						list[Letter]:
 if isinstance(lowercase_     ,					lowercase_		):
  A__			=							bitstring
  return [root]
 A__			=							root  # type: ignore
 A__			=							[]
 letters += traverse_tree(treenode.left     ,					bitstring + "0"		)
 letters += traverse_tree(treenode.right     ,					bitstring + "1"		)
 return letters







def     _SCREAMING_SNAKE_CASE  (				lowercase_		)		->						None:
 A__			=							parse_file(lowercase_		)
 A__			=							build_tree(lowercase_		)
 A__			=							{
     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:
   A__			=							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])





