



'''simple docstring'''





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 __A      (  A       ,       unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase   :	Optional[Any]										=	PaintByExamplePipeline
 __lowerCamelCase   :	Optional[int]										=	IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
 __lowerCamelCase   :	str										=	IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 __lowerCamelCase   :	Tuple										=	frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



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


      """simple docstring"""



      torch.manual_seed(0	)
      _a     =  UNetaDConditionModel(
          block_out_channels=(32, 64)			,       layers_per_block=2			,       sample_size=32			,       in_channels=9			,       out_channels=4			,       down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,       up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,       cross_attention_dim=32			,       )
      _a     =  PNDMScheduler(skip_prk_steps=A	)
      torch.manual_seed(0	)
      _a     =  AutoencoderKL(
          block_out_channels=[32, 64]			,       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=32			,       projection_dim=32			,       intermediate_size=37			,       layer_norm_eps=1E-05			,       num_attention_heads=4			,       num_hidden_layers=5			,       image_size=32			,       patch_size=4			,       )
      _a     =  PaintByExampleImageEncoder(A			,       proj_size=32	)
      _a     =  CLIPImageProcessor(crop_size=32			,       size=32	)

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



 def  a__  (self			,       A	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  np.array(image.convert('''RGB'''	)	)
      _a     =  image[None].transpose(0			,       3			,       1			,       2	)
      _a     =  torch.from_numpy(A	).to(dtype=torch.floataa	) / 127.5 - 1.0
      return image



 def  a__  (self			,       A="cpu"			,       A=0	)   ->					Union[str, Any]:


      """simple docstring"""



      _a     =  floats_tensor((1, 3, 32, 32)			,       rng=random.Random(A	)	).to(A	)
      _a     =  image.cpu().permute(0			,       2			,       3			,       1	)[0]
      _a     =  Image.fromarray(np.uinta(A	)	).convert('''RGB'''	).resize((64, 64)	)
      _a     =  Image.fromarray(np.uinta(image + 4	)	).convert('''RGB'''	).resize((64, 64)	)
      _a     =  Image.fromarray(np.uinta(A	)	).convert('''RGB'''	).resize((32, 32)	)

      if str(A	).startswith('''mps'''	):
           _a     =  torch.manual_seed(A	)
      else:
           _a     =  torch.Generator(device=A	).manual_seed(A	)
      _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  a__  (self	)   ->					Dict:


      """simple docstring"""



      _a     =  self.get_dummy_components()

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

      _a     =  self.get_dummy_inputs()
      _a     =  pipe(**A	)
      _a     =  output.images

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

      assert image.shape == (1, 64, 64, 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  a__  (self	)   ->					Tuple:


      """simple docstring"""



      _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(A	)
      pipe.set_progress_bar_config(disable=A	)

      _a     =  pipe(image=A			,       mask_image=mask_image[:, 0]			,       **A	)
      _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(A	)	).convert('''RGB'''	)
      _a     =  Image.fromarray(np.uinta(A	)	).convert('''RGB'''	)

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

      assert out_a.shape == (1, 64, 64, 3)
      assert np.abs(out_a.flatten() - out_a.flatten()	).max() < 5E-2



 def  a__  (self	)   ->					str:


      """simple docstring"""



      super().test_inference_batch_single_identical(expected_max_diff=3E-3	)






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



 '''simple docstring'''







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


      """simple docstring"""



      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



 def  a__  (self	)   ->					int:


      """simple docstring"""



      _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(A	)
      pipe.set_progress_bar_config(disable=A	)

      _a     =  torch.manual_seed(321	)
      _a     =  pipe(
          image=A			,       mask_image=A			,       example_image=A			,       generator=A			,       guidance_scale=5.0			,       num_inference_steps=50			,       output_type='''np'''			,       )

      _a     =  output.images

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

      assert image.shape == (1, 512, 512, 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








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



