





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      @property
      def   UpperCAmelCase						(	self				)    ->				Union[str, Any]:






        '''simple docstring'''






        torch.manual_seed(0				)
        __snake_case				:			Any            =    UNetaDModel(
            block_out_channels=(32, 64)			,  layers_per_block=2			,  sample_size=32			,  in_channels=3			,  out_channels=3			,  down_block_types=("DownBlock2D", "AttnDownBlock2D")			,  up_block_types=("AttnUpBlock2D", "UpBlock2D")			,  )
        return model







      def   UpperCAmelCase						(	self				)    ->				Optional[Any]:






        '''simple docstring'''






        __snake_case				:			Optional[int]            =    "cpu"
        __snake_case				:			List[str]            =    self.dummy_uncond_unet
        __snake_case				:			Any            =    DDPMScheduler()

        __snake_case				:			Tuple            =    DDPMPipeline(unet=UpperCAmelCase			,  scheduler=UpperCAmelCase				)
        ddpm.to(UpperCAmelCase				)
        ddpm.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Any            =    torch.Generator(device=UpperCAmelCase				).manual_seed(0				)
        __snake_case				:			Dict            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"				).images

        __snake_case				:			Dict            =    torch.Generator(device=UpperCAmelCase				).manual_seed(0				)
        __snake_case				:			Optional[int]            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"			,  return_dict=UpperCAmelCase				)[0]

        __snake_case				:			Tuple            =    image[0, -3:, -3:, -1]
        __snake_case				:			List[str]            =    image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __snake_case				:			Dict            =    np.array(
            [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.000, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04]				)

        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2







      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        __snake_case				:			Tuple            =    self.dummy_uncond_unet
        __snake_case				:			Any            =    DDPMScheduler(prediction_type="sample"				)

        __snake_case				:			List[Any]            =    DDPMPipeline(unet=UpperCAmelCase			,  scheduler=UpperCAmelCase				)
        ddpm.to(UpperCAmelCase				)
        ddpm.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Dict            =    torch.manual_seed(0				)
        __snake_case				:			List[Any]            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"				).images

        __snake_case				:			Optional[Any]            =    torch.manual_seed(0				)
        __snake_case				:			int            =    ddpm(generator=UpperCAmelCase			,  num_inference_steps=2			,  output_type="numpy"				)[0]

        __snake_case				:			List[Any]            =    image[0, -3:, -3:, -1]
        __snake_case				:			str            =    image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __snake_case				:			Optional[Any]            =    1E-2 if torch_device != "mps" else 3E-2
        assert np.abs(image_slice.flatten() - image_eps_slice.flatten()				).max() < tolerance




@slow
@require_torch_gpu
class 			_lowerCamelCase	(      unittest.TestCase      ):



      """simple docstring"""






      def   UpperCAmelCase						(	self				)    ->				str:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    "google/ddpm-cifar10-32"

        __snake_case				:			Optional[Any]            =    UNetaDModel.from_pretrained(UpperCAmelCase				)
        __snake_case				:			Optional[Any]            =    DDPMScheduler.from_pretrained(UpperCAmelCase				)

        __snake_case				:			Union[str, Any]            =    DDPMPipeline(unet=UpperCAmelCase			,  scheduler=UpperCAmelCase				)
        ddpm.to(UpperCAmelCase				)
        ddpm.set_progress_bar_config(disable=UpperCAmelCase				)

        __snake_case				:			Tuple            =    torch.manual_seed(0				)
        __snake_case				:			Any            =    ddpm(generator=UpperCAmelCase			,  output_type="numpy"				).images

        __snake_case				:			List[Any]            =    image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        __snake_case				:			str            =    np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]				)
        assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2








from __future__ import annotations

import sys




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Union[str, Any]:






        '''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 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Any:






        '''simple docstring'''






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







def  lowerCAmelCase__(		lowercase    :      str			)	->      list[Letter]:
  __snake_case				:			dict[str, int]            =    {}
  with open(lowercase			) as f:
    while True:
      __snake_case				:			List[Any]            =    f.read(1			)
      if not c:
        break
      __snake_case				:			Optional[Any]            =    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  lowerCAmelCase__(		lowercase    :      list[Letter]			)	->      Letter | TreeNode:
  __snake_case				:			list[Letter | TreeNode]            =    letters  # type: ignore
  while len(lowercase			) > 1:
    __snake_case				:			Any            =    response.pop(0			)
    __snake_case				:			Optional[int]            =    response.pop(0			)
    __snake_case				:			str            =    left.freq + right.freq
    __snake_case				:			Dict            =    TreeNode(lowercase				,       lowercase				,       lowercase			)
    response.append(lowercase			)
    response.sort(key=lambda lowercase			: x.freq			)
  return response[0]







def  lowerCAmelCase__(		lowercase    :      Letter | TreeNode				,       lowercase    :      str			)	->      list[Letter]:
  if isinstance(lowercase				,       lowercase			):
    __snake_case				:			List[str]            =    bitstring
    return [root]
  __snake_case				:			TreeNode            =    root  # type: ignore
  __snake_case				:			Union[str, Any]            =    []
  letters += traverse_tree(treenode.left				,       bitstring + "0"			)
  letters += traverse_tree(treenode.right				,       bitstring + "1"			)
  return letters







def  lowerCAmelCase__(		lowercase    :      str			)	->      None:
  __snake_case				:			List[Any]            =    parse_file(lowercase			)
  __snake_case				:			List[str]            =    build_tree(lowercase			)
  __snake_case				:			Optional[int]            =    {
      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:
      __snake_case				:			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])

