


"""simple docstring"""






from __future__ import annotations

import sys


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						str		=      letter
								snake_case__  :						int		=      freq
								snake_case__  :						dict[str, str]		=      {}







							def __repr__(						self)   ->	str:







								'''simple docstring'''




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


class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						int		=      freq
								snake_case__  :						Letter | TreeNode		=      left
								snake_case__  :						Letter | TreeNode		=      right



def 			A__		(					_UpperCAmelCase    :      str	)							->	list[Letter]:

	'''simple docstring'''


	snake_case__  :						dict[str, int]		=      {}
	with open(_UpperCAmelCase	) as f:
		while True:
			snake_case__  :						List[str]		=      f.read(1	)
			if not c:
				break
			snake_case__  :						Union[str, Any]		=      chars[c] + 1 if c in chars else 1
	return sorted((Letter(_UpperCAmelCase				,    _UpperCAmelCase	) for c, f in chars.items())				,    key=lambda _UpperCAmelCase	: x.freq	)



def 			A__		(					_UpperCAmelCase    :      list[Letter]	)							->	Letter | TreeNode:

	'''simple docstring'''


	snake_case__  :						list[Letter | TreeNode]		=      letters  # type: ignore
	while len(_UpperCAmelCase	) > 1:
		snake_case__  :						Optional[int]		=      response.pop(0	)
		snake_case__  :						List[Any]		=      response.pop(0	)
		snake_case__  :						str		=      left.freq + right.freq
		snake_case__  :						List[Any]		=      TreeNode(_UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase	)
		response.append(_UpperCAmelCase	)
		response.sort(key=lambda _UpperCAmelCase	: x.freq	)
	return response[0]



def 			A__		(					_UpperCAmelCase    :      Letter | TreeNode				,    _UpperCAmelCase    :      str	)							->	list[Letter]:

	'''simple docstring'''


	if isinstance(_UpperCAmelCase				,    _UpperCAmelCase	):
		snake_case__  :						Any		=      bitstring
		return [root]
	snake_case__  :						TreeNode		=      root  # type: ignore
	snake_case__  :						List[Any]		=      []
	letters += traverse_tree(treenode.left				,    bitstring + "0"	)
	letters += traverse_tree(treenode.right				,    bitstring + "1"	)
	return letters



def 			A__		(					_UpperCAmelCase    :      str	)							->	None:

	'''simple docstring'''


	snake_case__  :						Dict		=      parse_file(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      build_tree(_UpperCAmelCase	)
	snake_case__  :						List[Any]		=      {
	    k: v for letter in traverse_tree(_UpperCAmelCase				,    ""	) for k, v in letter.bitstring.items()
	}
	print(F"""Huffman Coding  of {file_path}: """	)
	with open(_UpperCAmelCase	) as f:
		while True:
			snake_case__  :						Optional[int]		=      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])












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )