





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    ):
	UpperCAmelCase__      =    PaintByExamplePipeline
	UpperCAmelCase__      =    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
	UpperCAmelCase__      =    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	UpperCAmelCase__      =    frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
	def   _lowercase       (					self	):






				'''simple docstring'''







				torch.manual_seed(0	)
				UpperCAmelCase     	=       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							,      )
				UpperCAmelCase     	=       PNDMScheduler(skip_prk_steps=_A	)
				torch.manual_seed(0	)
				UpperCAmelCase     	=       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	)
				UpperCAmelCase     	=       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							,      )
				UpperCAmelCase     	=       PaintByExampleImageEncoder(_A							,      proj_size=3_2	)
				UpperCAmelCase     	=       CLIPImageProcessor(crop_size=3_2							,      size=3_2	)

				UpperCAmelCase     	=       {
				    '''unet''': unet,
				    '''scheduler''': scheduler,
				    '''vae''': vae,
				    '''image_encoder''': image_encoder,
				    '''safety_checker''': None,
				    '''feature_extractor''': feature_extractor,
				}
				return components
	def   _lowercase       (					self							,      _A	):






				'''simple docstring'''







				UpperCAmelCase     	=       np.array(image.convert('''RGB'''	)	)
				UpperCAmelCase     	=       image[None].transpose(0							,      3							,      1							,      2	)
				UpperCAmelCase     	=       torch.from_numpy(_A	).to(dtype=torch.floataa	) / 1_27.5 - 1.0
				return image
	def   _lowercase       (					self							,      _A="cpu"							,      _A=0	):






				'''simple docstring'''







				UpperCAmelCase     	=       floats_tensor((1, 3, 3_2, 3_2)							,      rng=random.Random(_A	)	).to(_A	)
				UpperCAmelCase     	=       image.cpu().permute(0							,      2							,      3							,      1	)[0]
				UpperCAmelCase     	=       Image.fromarray(np.uinta(_A	)	).convert('''RGB'''	).resize((6_4, 6_4)	)
				UpperCAmelCase     	=       Image.fromarray(np.uinta(image + 4	)	).convert('''RGB'''	).resize((6_4, 6_4)	)
				UpperCAmelCase     	=       Image.fromarray(np.uinta(_A	)	).convert('''RGB'''	).resize((3_2, 3_2)	)

				if str(_A	).startswith('''mps'''	):
							UpperCAmelCase     	=       torch.manual_seed(_A	)
				else:
							UpperCAmelCase     	=       torch.Generator(device=_A	).manual_seed(_A	)
				UpperCAmelCase     	=       {
				    '''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   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       self.get_dummy_components()

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

				UpperCAmelCase     	=       self.get_dummy_inputs()
				UpperCAmelCase     	=       pipe(**_A	)
				UpperCAmelCase     	=       output.images

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

				assert image.shape == (1, 6_4, 6_4, 3)
				UpperCAmelCase     	=       np.array([0.46_86, 0.56_87, 0.40_07, 0.52_18, 0.57_41, 0.44_82, 0.49_40, 0.46_29, 0.45_03]	)

				assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       '''cpu'''
				UpperCAmelCase     	=       self.get_dummy_inputs()
				inputs.pop('''mask_image'''	)
				UpperCAmelCase     	=       self.convert_to_pt(inputs.pop('''image'''	)	)
				UpperCAmelCase     	=       image.clamp(0							,      1	) / 2

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

				UpperCAmelCase     	=       pipe(image=_A							,      mask_image=mask_image[:, 0]							,      **_A	)
				UpperCAmelCase     	=       output.images

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

				UpperCAmelCase     	=       Image.fromarray(np.uinta(_A	)	).convert('''RGB'''	)
				UpperCAmelCase     	=       Image.fromarray(np.uinta(_A	)	).convert('''RGB'''	)

				UpperCAmelCase     	=       pipe(**self.get_dummy_inputs()	)
				UpperCAmelCase     	=       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   _lowercase       (					self	):






				'''simple docstring'''







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

@slow
@require_torch_gpu
class 			A_ (unittest.TestCase    ):
	def   _lowercase       (					self	):






				'''simple docstring'''







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







	def   _lowercase       (					self	):






				'''simple docstring'''







				UpperCAmelCase     	=       load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/paint_by_example/dog_in_bucket.png'''	)
				UpperCAmelCase     	=       load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/paint_by_example/mask.png'''	)
				UpperCAmelCase     	=       load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/paint_by_example/panda.jpg'''	)

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

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

				UpperCAmelCase     	=       output.images

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				UpperCAmelCase     	=       np.array([0.48_34, 0.48_11, 0.48_74, 0.51_22, 0.50_81, 0.51_44, 0.52_91, 0.52_90, 0.53_74]	)

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










import numpy as np





def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)						->       int:







			'''simple docstring'''

			UpperCAmelCase     	=       n_draws * class_counts / class_counts.sum()
			# floored means we don't overshoot n_samples, but probably undershoot
			UpperCAmelCase     	=       np.floor(UpperCamelCase__				)
			# we add samples according to how much "left over" probability
			# they had, until we arrive at n_samples
			UpperCAmelCase     	=       int(n_draws - floored.sum()				)
			if need_to_add > 0:
						UpperCAmelCase     	=       continuous - floored
						UpperCAmelCase     	=       np.sort(np.unique(UpperCamelCase__				)				)[::-1]
						# add according to remainder, but break ties
						# randomly to avoid biases
						for value in values:
									((UpperCAmelCase)	,    )     	=       np.where(remainder == value				)
									# if we need_to_add less than what's in inds
									# we draw randomly from them.
									# if we need to add more, we add them all and
									# go to the next value
									UpperCAmelCase     	=       min(len(UpperCamelCase__				)    ,    UpperCamelCase__				)
									UpperCAmelCase     	=       rng.choice(UpperCamelCase__    ,    size=UpperCamelCase__    ,    replace=UpperCamelCase__				)
									floored[inds] += 1
									need_to_add -= add_now
									if need_to_add == 0:
												break
			return floored.astype(np.intaa				)






def 		__SCREAMING_SNAKE_CASE						(       UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__=10				)						->       Any:







			'''simple docstring'''

			UpperCAmelCase	,    UpperCAmelCase     	=       np.unique(UpperCamelCase__    ,    return_inverse=UpperCamelCase__				)
			UpperCAmelCase     	=       classes.shape[0]
			UpperCAmelCase     	=       np.bincount(UpperCamelCase__				)
			if np.min(UpperCamelCase__				) < 2:
						raise ValueError('''Minimum class count error'''				)
			if n_train < n_classes:
						raise ValueError(
						    '''The train_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_train, n_classes)				)
			if n_test < n_classes:
						raise ValueError(
						    '''The test_size = %d should be greater or ''' '''equal to the number of classes = %d''' % (n_test, n_classes)				)
			UpperCAmelCase     	=       np.split(np.argsort(UpperCamelCase__    ,    kind='''mergesort'''				)    ,    np.cumsum(UpperCamelCase__				)[:-1]				)
			for _ in range(UpperCamelCase__				):
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)
						UpperCAmelCase     	=       class_counts - n_i
						UpperCAmelCase     	=       approximate_mode(UpperCamelCase__    ,    UpperCamelCase__    ,    UpperCamelCase__				)

						UpperCAmelCase     	=       []
						UpperCAmelCase     	=       []

						for i in range(UpperCamelCase__				):
									UpperCAmelCase     	=       rng.permutation(class_counts[i]				)
									UpperCAmelCase     	=       class_indices[i].take(UpperCamelCase__    ,    mode='''clip'''				)
									train.extend(perm_indices_class_i[: n_i[i]]				)
									test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)
						UpperCAmelCase     	=       rng.permutation(UpperCamelCase__				)

						yield train, test



