






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'],
           )   ,       )








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])
