






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


lowerCamelCase										=  logging.get_logger(__name__)

class 					snake_case_							(       lowercase_      ):







   """simple docstring"""



   __lowerCAmelCase      =						["""pixel_values"""]

   def __init__(    self:	int   ,       snake_case__:	bool = True   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	bool = False   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[int, float] = 1 / 255   ,       snake_case__:	bool = False   ,       snake_case__:	bool = True   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = True   ,       **snake_case__:	Optional[Any]   ,       )  ->		None:


       """simple docstring"""



       super().__init__(**snake_case__	)
       _lowercase     =	size if size is not None else {'height': 346, 'width': 346}
       _lowercase     =	get_size_dict(snake_case__	)
       _lowercase     =	crop_size if crop_size is not None else {'height': 289, 'width': 289}
       _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)

       _lowercase     =	do_resize
       _lowercase     =	size
       _lowercase     =	resample
       _lowercase     =	do_center_crop
       _lowercase     =	crop_size
       _lowercase     =	do_rescale
       _lowercase     =	rescale_factor
       _lowercase     =	rescale_offset
       _lowercase     =	do_normalize
       _lowercase     =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       _lowercase     =	image_std if image_std is not None else IMAGENET_STANDARD_STD
       _lowercase     =	include_top

   def 			SCREAMING_SNAKE_CASE__				(    self:	str   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	PILImageResampling = PIL.Image.NEAREST   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Optional[int]   ,       )  ->		np.ndarray:


       """simple docstring"""



       _lowercase     =	get_size_dict(snake_case__	)
       if "height" not in size or "width" not in size:
           raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
       return resize(
           snake_case__   ,       size=(size['height'], size['width'])   ,       resample=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Dict[str, int]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	str   ,       )  ->		np.ndarray:


       """simple docstring"""



       _lowercase     =	get_size_dict(snake_case__	)
       if "height" not in size or "width" not in size:
           raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''	)
       return center_crop(snake_case__   ,       size=(size['height'], size['width'])   ,       data_format=snake_case__   ,       **snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[Any]   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[int, float]   ,       snake_case__:	bool = True   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	List[str]   ,       )  ->		Optional[int]:


       """simple docstring"""



       if offset:
           _lowercase     =	(image - 1_27.5) * scale
           if data_format is not None:
               _lowercase     =	to_channel_dimension_format(snake_case__   ,       snake_case__	)
           _lowercase     =	rescaled_image.astype(np.floataa	)
       else:
           _lowercase     =	rescale(snake_case__   ,       scale=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)
       return rescaled_image

   def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	np.ndarray   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Union[float, List[float]]   ,       snake_case__:	Optional[Union[str, ChannelDimension]] = None   ,       **snake_case__:	Any   ,       )  ->		np.ndarray:


       """simple docstring"""



       return normalize(snake_case__   ,       mean=snake_case__   ,       std=snake_case__   ,       data_format=snake_case__   ,       **snake_case__	)

   def 			SCREAMING_SNAKE_CASE__				(    self:	Optional[int]   ,       snake_case__:	ImageInput   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	Union[str, Any]=None   ,       snake_case__:	bool = None   ,       snake_case__:	Dict[str, int] = None   ,       snake_case__:	bool = None   ,       snake_case__:	float = None   ,       snake_case__:	bool = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	Optional[Union[float, List[float]]] = None   ,       snake_case__:	bool = None   ,       snake_case__:	Optional[Union[str, TensorType]] = None   ,       snake_case__:	ChannelDimension = ChannelDimension.FIRST   ,       **snake_case__:	int   ,       )  ->		PIL.Image.Image:


       """simple docstring"""



       _lowercase     =	do_resize if do_resize is not None else self.do_resize
       _lowercase     =	resample if resample is not None else self.resample
       _lowercase     =	do_center_crop if do_center_crop is not None else self.do_center_crop
       _lowercase     =	do_rescale if do_rescale is not None else self.do_rescale
       _lowercase     =	rescale_factor if rescale_factor is not None else self.rescale_factor
       _lowercase     =	rescale_offset if rescale_offset is not None else self.rescale_offset
       _lowercase     =	do_normalize if do_normalize is not None else self.do_normalize
       _lowercase     =	image_mean if image_mean is not None else self.image_mean
       _lowercase     =	image_std if image_std is not None else self.image_std
       _lowercase     =	include_top if include_top is not None else self.include_top

       _lowercase     =	size if size is not None else self.size
       _lowercase     =	get_size_dict(snake_case__	)
       _lowercase     =	crop_size if crop_size is not None else self.crop_size
       _lowercase     =	get_size_dict(snake_case__   ,       param_name='crop_size'	)

       _lowercase     =	make_list_of_images(snake_case__	)

       if not valid_images(snake_case__	):
           raise ValueError(
               'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
               'torch.Tensor, tf.Tensor or jax.ndarray.'	)

       if do_resize and size is None or resample is None:
           raise ValueError('Size and resample must be specified if do_resize is True.'	)

       if do_center_crop and crop_size is None:
           raise ValueError('Crop size must be specified if do_center_crop is True.'	)

       if do_rescale and rescale_factor is None:
           raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

       if do_normalize and (image_mean is None or image_std is None):
           raise ValueError('Image mean and std must be specified if do_normalize is True.'	)

       # All transformations expect numpy arrays.
       _lowercase     =	[to_numpy_array(snake_case__	) for image in images]

       if do_resize:
           _lowercase     =	[self.resize(image=snake_case__   ,       size=snake_case__   ,       resample=snake_case__	) for image in images]

       if do_center_crop:
           _lowercase     =	[self.center_crop(image=snake_case__   ,       size=snake_case__	) for image in images]

       if do_rescale:
           _lowercase     =	[self.rescale(image=snake_case__   ,       scale=snake_case__   ,       offset=snake_case__	) for image in images]

       if do_normalize:
           _lowercase     =	[self.normalize(image=snake_case__   ,       mean=snake_case__   ,       std=snake_case__	) for image in images]

       if include_top:
           _lowercase     =	[self.normalize(image=snake_case__   ,       mean=[0, 0, 0]   ,       std=snake_case__	) for image in images]

       _lowercase     =	[to_channel_dimension_format(snake_case__   ,       snake_case__	) for image in images]

       _lowercase     =	{'pixel_values': images}
       return BatchFeature(data=snake_case__   ,       tensor_type=snake_case__	)








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])
