'''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     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :str       =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     )				->     Any:
        """simple docstring"""





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

        _UpperCamelCase  :List[str]       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :str       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :str       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

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

        _UpperCamelCase  :List[str]       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :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     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.dummy_uncond_unet
        _UpperCamelCase  :str       =DDPMScheduler(prediction_type="""sample"""     )

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

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :int       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     )[0]

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

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :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     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       ="""google/ddpm-cifar10-32"""

        _UpperCamelCase  :Optional[int]       =UNetaDModel.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =DDPMScheduler.from_pretrained(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =torch.manual_seed(0     )
        _UpperCamelCase  :Optional[int]       =ddpm(generator=lowerCAmelCase__					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Any       =np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]     )
        assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2





from __future__ import annotations

import sys
class 							__lowercase   :



     def __init__(  self   :							Any      ,		lowercase__   :							str      ,		lowercase__   :							int       ):
       a_		    =  letter
       a_		    =  freq
       a_		    =  {}



     def __repr__(  self   :							List[Any]       ):
       return f"{self.letter}:{self.freq}"



class 							__lowercase   :



     def __init__(  self   :							Optional[int]      ,		lowercase__   :							int      ,		lowercase__   :							Letter | TreeNode      ,		lowercase__   :							Letter | TreeNode       ):
       a_		    =  freq
       a_		    =  left
       a_		    =  right






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if isinstance(_A      ,      _A			):
    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 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


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