






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_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


UpperCamelCase__						:      Tuple								=					logging.get_logger(__name__)







class 				lowerCamelCase_     (	a_     ):
							SCREAMING_SNAKE_CASE_						     =			['pixel_values']
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Dict[str, int] = None							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Dict[str, int] = None							,__lowerCamelCase		:					PILImageResampling = PILImageResampling.BICUBIC							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Union[int, float] = 1 / 2_55							,__lowerCamelCase		:					bool = True							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,**__lowerCamelCase		:					int							,):





								'''simple docstring'''



								super().__init__(**__lowerCamelCase					)
								a		=			crop_size if crop_size is not None else {'''height''': 2_56, '''width''': 2_56}
								a		=			get_size_dict(__lowerCamelCase							,param_name='''crop_size'''					)
								a		=			size if size is not None else {'''height''': 2_24, '''width''': 2_24}
								a		=			get_size_dict(__lowerCamelCase					)

								a		=			do_center_crop
								a		=			crop_size
								a		=			do_resize
								a		=			size
								a		=			resample
								a		=			do_rescale
								a		=			rescale_factor
								a		=			do_normalize
								a		=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
								a		=			image_std if image_std is not None else IMAGENET_DEFAULT_STD
							def  SCREAMING_SNAKE_CASE_ (						self		:					Any							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Dict[str, int]							,__lowerCamelCase		:					Optional[int] = None							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					Optional[Any]							,):





								'''simple docstring'''



								a		=			self.size if size is None else size
								a		=			get_size_dict(__lowerCamelCase					)
								a		=			get_size_dict(__lowerCamelCase							,param_name='''crop_size'''					)

								a	, a		=			get_image_size(__lowerCamelCase					)
								a		=			min(__lowerCamelCase							,__lowerCamelCase					)
								a		=			(size['''height'''] / crop_size['''height''']) * min_dim
								a		=			(size['''width'''] / crop_size['''width''']) * min_dim
								return center_crop(__lowerCamelCase							,size=(cropped_height, cropped_width)							,data_format=__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					List[str]							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Dict[str, int]							,__lowerCamelCase		:					PILImageResampling = PIL.Image.BICUBIC							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					List[str]							,):





								'''simple docstring'''



								a		=			get_size_dict(__lowerCamelCase					)
								if "height" not in size or "width" not in size:
									raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""					)
								return resize(
								    __lowerCamelCase							,size=(size['''height'''], size['''width'''])							,resample=__lowerCamelCase							,data_format=__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Union[int, float]							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					Union[str, Any]							,):





								'''simple docstring'''



								return rescale(__lowerCamelCase							,scale=__lowerCamelCase							,data_format=__lowerCamelCase							,**__lowerCamelCase					)
							def  SCREAMING_SNAKE_CASE_ (						self		:					Dict							,__lowerCamelCase		:					np.ndarray							,__lowerCamelCase		:					Union[float, List[float]]							,__lowerCamelCase		:					Union[float, List[float]]							,__lowerCamelCase		:					Optional[Union[str, ChannelDimension]] = None							,**__lowerCamelCase		:					List[str]							,):





								'''simple docstring'''



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




							def  SCREAMING_SNAKE_CASE_ (						self		:					str							,__lowerCamelCase		:					ImageInput							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[Dict[str, int]] = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[Dict[str, int]] = None							,__lowerCamelCase		:					PILImageResampling = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[float] = None							,__lowerCamelCase		:					Optional[bool] = None							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,__lowerCamelCase		:					Optional[Union[float, List[float]]] = None							,__lowerCamelCase		:					Optional[Union[str, TensorType]] = None							,__lowerCamelCase		:					ChannelDimension = ChannelDimension.FIRST							,**__lowerCamelCase		:					str							,):





								'''simple docstring'''



								a		=			do_center_crop if do_center_crop is not None else self.do_center_crop
								a		=			crop_size if crop_size is not None else self.crop_size
								a		=			get_size_dict(__lowerCamelCase							,param_name='''crop_size'''					)
								a		=			do_resize if do_resize is not None else self.do_resize
								a		=			size if size is not None else self.size
								a		=			get_size_dict(__lowerCamelCase					)
								a		=			resample if resample is not None else self.resample
								a		=			do_rescale if do_rescale is not None else self.do_rescale
								a		=			rescale_factor if rescale_factor is not None else self.rescale_factor
								a		=			do_normalize if do_normalize is not None else self.do_normalize
								a		=			image_mean if image_mean is not None else self.image_mean
								a		=			image_std if image_std is not None else self.image_std

								a		=			make_list_of_images(__lowerCamelCase					)

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

								if do_center_crop and crop_size is None:
									raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''					)

								if do_resize and size is None:
									raise ValueError('''Size must be specified if do_resize 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 image standard deviation must be specified if do_normalize is True.'''					)

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

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

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

								if do_rescale:
									a		=			[self.rescale(image=__lowerCamelCase							,scale=__lowerCamelCase					) for image in images]

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

								a		=			[to_channel_dimension_format(__lowerCamelCase							,__lowerCamelCase					) for image in images]

								a		=			{'''pixel_values''': images}
								return BatchFeature(data=__lowerCamelCase							,tensor_type=__lowerCamelCase					)













from __future__ import annotations

import sys







class 				lowerCamelCase_     :
							def __init__(						self		:					Optional[int]							,__lowerCamelCase		:					str							,__lowerCamelCase		:					int					):





								'''simple docstring'''



								a		=			letter
								a		=			freq
								a		=			{}




							def __repr__(						self		:					List[str]					):





								'''simple docstring'''



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







class 				lowerCamelCase_     :
							def __init__(						self		:					Dict							,__lowerCamelCase		:					int							,__lowerCamelCase		:					Letter | TreeNode							,__lowerCamelCase		:					Letter | TreeNode					):





								'''simple docstring'''



								a		=			freq
								a		=			left
								a		=			right





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	a		=			{}
	with open(snake_case_  ) 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(snake_case_,  snake_case_  ) for c, f in chars.items()),  key=lambda snake_case_  : x.freq  )





def   SCREAMING_SNAKE_CASE__						(   snake_case_  )   ->  Letter | TreeNode:


	"""simple docstring"""



	a		=			letters  # type: ignore
	while len(snake_case_  ) > 1:
		a		=			response.pop(0  )
		a		=			response.pop(0  )
		a		=			left.freq + right.freq
		a		=			TreeNode(snake_case_,  snake_case_,  snake_case_  )
		response.append(snake_case_  )
		response.sort(key=lambda snake_case_  : x.freq  )
	return response[0]





def   SCREAMING_SNAKE_CASE__						(   snake_case_,  snake_case_  )   ->  list[Letter]:


	"""simple docstring"""



	if isinstance(snake_case_,  snake_case_  ):
		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__						(   snake_case_  )   ->  None:


	"""simple docstring"""



	a		=			parse_file(snake_case_  )
	a		=			build_tree(snake_case_  )
	a		=			{
	    k: v for letter in traverse_tree(snake_case_,  ''''''  ) for k, v in letter.bitstring.items()
	}
	print(f"""Huffman Coding  of {file_path}: """  )
	with open(snake_case_  ) 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])





