"""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 SCREAMING_SNAKE_CASE     (    lowerCAmelCase_   ,      unittest.TestCase	):







	"""simple docstring"""


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


	def        _lowerCAmelCase				(   self		:      Any				)				->			Tuple:




				'''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=__SCREAMING_SNAKE_CASE				)
				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(__SCREAMING_SNAKE_CASE					,   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        _lowerCAmelCase				(   self		:      Optional[int]					,   _snake_case		:      Optional[int]				)				->			List[Any]:




				'''simple docstring'''






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


	def        _lowerCAmelCase				(   self		:      int					,   _snake_case		:      int="cpu"					,   _snake_case		:      List[Any]=0				)				->			Optional[Any]:




				'''simple docstring'''






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

				if str(__SCREAMING_SNAKE_CASE				).startswith('mps'				):
							a__     				=		torch.manual_seed(__SCREAMING_SNAKE_CASE				)
				else:
							a__     				=		torch.Generator(device=__SCREAMING_SNAKE_CASE				).manual_seed(__SCREAMING_SNAKE_CASE				)
				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        _lowerCAmelCase				(   self		:      Tuple				)				->			Dict:




				'''simple docstring'''






				a__     				=		self.get_dummy_components()

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

				a__     				=		self.get_dummy_inputs()
				a__     				=		pipe(**__SCREAMING_SNAKE_CASE				)
				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        _lowerCAmelCase				(   self		:      List[Any]				)				->			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(__SCREAMING_SNAKE_CASE				)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

				a__     				=		pipe(image=__SCREAMING_SNAKE_CASE					,   mask_image=mask_image[:, 0]					,   **__SCREAMING_SNAKE_CASE				)
				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(__SCREAMING_SNAKE_CASE				)				).convert('RGB'				)
				a__     				=		Image.fromarray(np.uinta(__SCREAMING_SNAKE_CASE				)				).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        _lowerCAmelCase				(   self		:      Optional[int]				)				->			List[Any]:




				'''simple docstring'''






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

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







	"""simple docstring"""


	def        _lowerCAmelCase				(   self		:      int				)				->			Optional[int]:




				'''simple docstring'''






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



	def        _lowerCAmelCase				(   self		:      Optional[Any]				)				->			str:




				'''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(__SCREAMING_SNAKE_CASE				)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE				)

				a__     				=		torch.manual_seed(321				)
				a__     				=		pipe(
				    image=__SCREAMING_SNAKE_CASE					,   mask_image=__SCREAMING_SNAKE_CASE					,   example_image=__SCREAMING_SNAKE_CASE					,   generator=__SCREAMING_SNAKE_CASE					,   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 platform
from argparse import ArgumentParser

import huggingface_hub
import pandas
import pyarrow

from datasets import __version__ as version
from datasets.commands import BaseDatasetsCLICommand





def   _lowerCamelCase ( UpperCAmelCase__					)							->     Any:


   '''simple docstring'''


   return EnvironmentCommand()




class SCREAMING_SNAKE_CASE     (    a	):







 """simple docstring"""


 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      ArgumentParser				)				->			int:




    '''simple docstring'''






    a__     				=		parser.add_parser('env'					,   help='Print relevant system environment info.'				)
    download_parser.set_defaults(func=_snake_case				)


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




    '''simple docstring'''






    a__     				=		{
        '`datasets` version': version,
        'Platform': platform.platform(),
        'Python version': platform.python_version(),
        'Huggingface_hub version': huggingface_hub.__version__,
        'PyArrow version': pyarrow.__version__,
        'Pandas version': pandas.__version__,
    }

    print('\nCopy-and-paste the text below in your GitHub issue.\n'				)
    print(self.format_dict(_snake_case				)				)

    return info



 @staticmethod
 def        _lowerCAmelCase				(   _snake_case		:      Tuple				)				->			Optional[int]:




    '''simple docstring'''






    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]				) + "\n"


