




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 							__lowercase   (			a__	,		unittest.TestCase    ):
     _lowerCAmelCase		       =						PaintByExamplePipeline
     _lowerCAmelCase		       =						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
     _lowerCAmelCase		       =						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     _lowerCAmelCase		       =						frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       torch.manual_seed(0       )
       a_		    =  UNetaDConditionModel(
           block_out_channels=(3_2, 6_4)      ,		layers_per_block=2      ,		sample_size=3_2      ,		in_channels=9      ,		out_channels=4      ,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')      ,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')      ,		cross_attention_dim=3_2      ,		)
       a_		    =  PNDMScheduler(skip_prk_steps=lowercase__       )
       torch.manual_seed(0       )
       a_		    =  AutoencoderKL(
           block_out_channels=[3_2, 6_4]      ,		in_channels=3      ,		out_channels=3      ,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']      ,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']      ,		latent_channels=4      ,		)
       torch.manual_seed(0       )
       a_		    =  CLIPVisionConfig(
           hidden_size=3_2      ,		projection_dim=3_2      ,		intermediate_size=3_7      ,		layer_norm_eps=1e-05      ,		num_attention_heads=4      ,		num_hidden_layers=5      ,		image_size=3_2      ,		patch_size=4      ,		)
       a_		    =  PaintByExampleImageEncoder(lowercase__      ,		proj_size=3_2       )
       a_		    =  CLIPImageProcessor(crop_size=3_2      ,		size=3_2       )

       a_		    =  {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''image_encoder''': image_encoder,
           '''safety_checker''': None,
           '''feature_extractor''': feature_extractor,
       }
       return components



     def 				__magic_name__		(  self   :							Tuple      ,		lowercase__   :							List[Any]       ):
       a_		    =  np.array(image.convert('''RGB'''       )       )
       a_		    =  image[None].transpose(0      ,		3      ,		1      ,		2       )
       a_		    =  torch.from_numpy(lowercase__       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
       return image



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							Union[str, Any]="cpu"      ,		lowercase__   :							List[str]=0       ):
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       a_		    =  floats_tensor((1, 3, 3_2, 3_2)      ,		rng=random.Random(lowercase__       )       ).to(lowercase__       )
       a_		    =  image.cpu().permute(0      ,		2      ,		3      ,		1       )[0]
       a_		    =  Image.fromarray(np.uinta(lowercase__       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )
       a_		    =  Image.fromarray(np.uinta(image + 4       )       ).convert('''RGB'''       ).resize((6_4, 6_4)       )
       a_		    =  Image.fromarray(np.uinta(lowercase__       )       ).convert('''RGB'''       ).resize((3_2, 3_2)       )

       if str(lowercase__       ).startswith('''mps'''       ):
         a_		    =  torch.manual_seed(lowercase__       )
       else:
         a_		    =  torch.Generator(device=lowercase__       ).manual_seed(lowercase__       )
       a_		    =  {
           '''example_image''': example_image,
           '''image''': init_image,
           '''mask_image''': mask_image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs



     def 				__magic_name__		(  self   :							List[str]       ):
       a_		    =  self.get_dummy_components()

       # make sure here that pndm scheduler skips prk
       a_		    =  PaintByExamplePipeline(**lowercase__       )
       a_		    =  pipe.to('''cpu'''       )
       pipe.set_progress_bar_config(disable=lowercase__       )

       a_		    =  self.get_dummy_inputs()
       a_		    =  pipe(**lowercase__       )
       a_		    =  output.images

       a_		    =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 6_4, 6_4, 3)
       a_		    =  np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]       )

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



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       a_		    =  '''cpu'''
       a_		    =  self.get_dummy_inputs()
       inputs.pop('''mask_image'''       )
       a_		    =  self.convert_to_pt(inputs.pop('''image'''       )       )
       a_		    =  image.clamp(0      ,		1       ) / 2

       # make sure here that pndm scheduler skips prk
       a_		    =  PaintByExamplePipeline(**self.get_dummy_components()       )
       a_		    =  pipe.to(lowercase__       )
       pipe.set_progress_bar_config(disable=lowercase__       )

       a_		    =  pipe(image=lowercase__      ,		mask_image=mask_image[:, 0]      ,		**lowercase__       )
       a_		    =  output.images

       a_		    =  image.cpu().permute(0      ,		2      ,		3      ,		1       )[0]
       a_		    =  mask_image.cpu().permute(0      ,		2      ,		3      ,		1       )[0]

       a_		    =  Image.fromarray(np.uinta(lowercase__       )       ).convert('''RGB'''       )
       a_		    =  Image.fromarray(np.uinta(lowercase__       )       ).convert('''RGB'''       )

       a_		    =  pipe(**self.get_dummy_inputs()       )
       a_		    =  output.images

       assert out_a.shape == (1, 6_4, 6_4, 3)
       assert np.abs(out_a.flatten() - out_a.flatten()       ).max() < 5e-2



     def 				__magic_name__		(  self   :							Optional[Any]       ):
       super().test_inference_batch_single_identical(expected_max_diff=3e-3       )



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



     def 				__magic_name__		(  self   :							Union[str, Any]       ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



     def 				__magic_name__		(  self   :							Dict       ):
       # make sure here that pndm scheduler skips prk
       a_		    =  load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/dog_in_bucket.png'''       )
       a_		    =  load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/mask.png'''       )
       a_		    =  load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/panda.jpg'''       )

       a_		    =  PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''       )
       a_		    =  pipe.to(lowercase__       )
       pipe.set_progress_bar_config(disable=lowercase__       )

       a_		    =  torch.manual_seed(3_2_1       )
       a_		    =  pipe(
           image=lowercase__      ,		mask_image=lowercase__      ,		example_image=lowercase__      ,		generator=lowercase__      ,		guidance_scale=5.0      ,		num_inference_steps=5_0      ,		output_type='''np'''      ,		)

       a_		    =  output.images

       a_		    =  image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_1_2, 5_1_2, 3)
       a_		    =  np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]       )

       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])
