




'''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 TvltImageProcessor
def 							snake_case_    (  a__						:      int       ,a__						:      Optional[Any]=10       ,a__						:      List[Any]=10       ,a__						:      Any=False       ,a__						:      str=False     ):



    """simple docstring"""




    __lowercase     							=		[]
    for i in range(image_processor_tester.num_frames     ):
        video.append(np.random.randint(2_55       ,size=(image_processor_tester.num_channels, width, height)       ,dtype=np.uinta     )     )

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __lowercase     							=		[Image.fromarray(np.moveaxis(lowercase_       ,0       ,-1     )     ) for frame in video]

    if torchify:
        __lowercase     							=		[torch.from_numpy(lowercase_     ) for frame in video]

    return video
def 							snake_case_    (  a__						:      Dict       ,a__						:      Optional[Any]=False       ,a__						:      Optional[int]=False       ,a__						:      List[Any]=False     ):



    """simple docstring"""




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

    __lowercase     							=		[]
    for i in range(image_processor_tester.batch_size     ):
        if equal_resolution:
            __lowercase     							=		__lowercase     							=		image_processor_tester.max_resolution
        else:
            __lowercase							,__lowercase     							=		np.random.choice(
                np.arange(image_processor_tester.min_resolution       ,image_processor_tester.max_resolution     )       ,2     )
            __lowercase     							=		prepare_video(
                image_processor_tester=lowercase_       ,width=lowercase_       ,height=lowercase_       ,numpify=lowercase_       ,torchify=lowercase_       ,)
        video_inputs.append(lowercase_     )

    return video_inputs






class 			SCREAMING_SNAKE_CASE(							unittest.TestCase	):

       def __init__(       self   ,						lowerCamelCase__   ,						lowerCamelCase__=7   ,						lowerCamelCase__=3   ,						lowerCamelCase__=4   ,						lowerCamelCase__=18   ,						lowerCamelCase__=30   ,						lowerCamelCase__=400   ,						lowerCamelCase__=True   ,						lowerCamelCase__=None   ,						lowerCamelCase__=True   ,						lowerCamelCase__=[0.5, 0.5, 0.5]   ,						lowerCamelCase__=[0.5, 0.5, 0.5]   ,						lowerCamelCase__=True   ,						lowerCamelCase__=None   ,						)       ->   List[str]:

           """simple docstring"""



           __lowercase     							=		size if size is not None else {"""shortest_edge""": 18}
           __lowercase     							=		crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

           __lowercase     							=		parent
           __lowercase     							=		batch_size
           __lowercase     							=		num_channels
           __lowercase     							=		num_frames
           __lowercase     							=		image_size
           __lowercase     							=		min_resolution
           __lowercase     							=		max_resolution
           __lowercase     							=		do_resize
           __lowercase     							=		size
           __lowercase     							=		do_normalize
           __lowercase     							=		image_mean
           __lowercase     							=		image_std
           __lowercase     							=		do_center_crop
           __lowercase     							=		crop_size




       def        snake_case__     (       self	)       ->   int:

           """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,
               "do_center_crop": self.do_center_crop,
               "crop_size": self.crop_size,
           }






@require_torch
@require_vision
class 			SCREAMING_SNAKE_CASE(							A_  , unittest.TestCase	):
       snake_case_					:	Any       =   TvltImageProcessor if is_vision_available() else None

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

           """simple docstring"""



           __lowercase     							=		TvltImageProcessorTester(self	)

       @property
       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           self.assertTrue(hasattr(snake_case_   ,						"""image_mean"""	)	)
           self.assertTrue(hasattr(snake_case_   ,						"""image_std"""	)	)
           self.assertTrue(hasattr(snake_case_   ,						"""do_normalize"""	)	)
           self.assertTrue(hasattr(snake_case_   ,						"""do_resize"""	)	)
           self.assertTrue(hasattr(snake_case_   ,						"""do_center_crop"""	)	)
           self.assertTrue(hasattr(snake_case_   ,						"""size"""	)	)

       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           # create random PIL videos
           __lowercase     							=		prepare_video_inputs(self.image_processor_tester   ,						equal_resolution=snake_case_	)
           for video in video_inputs:
               self.assertIsInstance(snake_case_   ,						snake_case_	)
               self.assertIsInstance(video[0]   ,						Image.Image	)

           # Test not batched input
           __lowercase     							=		image_processor(video_inputs[0]   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_videos.shape   ,						(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )   ,						)

           # Test batched
           __lowercase     							=		image_processor(snake_case_   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_videos.shape   ,						(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )   ,						)

       def        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           # create random numpy tensors
           __lowercase     							=		prepare_video_inputs(self.image_processor_tester   ,						equal_resolution=snake_case_   ,						numpify=snake_case_	)
           for video in video_inputs:
               self.assertIsInstance(snake_case_   ,						snake_case_	)
               self.assertIsInstance(video[0]   ,						np.ndarray	)

           # Test not batched input
           __lowercase     							=		image_processor(video_inputs[0]   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_videos.shape   ,						(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )   ,						)

           # Test batched
           __lowercase     							=		image_processor(snake_case_   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_videos.shape   ,						(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )   ,						)




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

           """simple docstring"""



           __lowercase     							=		self.image_processing_class(**self.image_processor_dict	)
           # create random PyTorch tensors
           __lowercase     							=		prepare_video_inputs(self.image_processor_tester   ,						equal_resolution=snake_case_   ,						torchify=snake_case_	)
           for video in video_inputs:
               self.assertIsInstance(snake_case_   ,						snake_case_	)
               self.assertIsInstance(video[0]   ,						torch.Tensor	)

           # Test not batched input
           __lowercase     							=		image_processor(video_inputs[0]   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_videos.shape   ,						(
                   1,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.crop_size["""height"""],
                   self.image_processor_tester.crop_size["""width"""],
               )   ,						)

           # Test batched
           __lowercase     							=		image_processor(snake_case_   ,						return_tensors="""pt"""	).pixel_values
           self.assertEqual(
               encoded_videos.shape   ,						(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_frames,
                   self.image_processor_tester.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])





