
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 						__magic_name__				(  __lowerCamelCase,			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  _A							(							self      :		Union[str, Any]			):
							torch.manual_seed(0			)
							lowerCAmelCase						:   int							=       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  ,					)
							lowerCAmelCase						:   Optional[Any]							=       PNDMScheduler(skip_prk_steps=_UpperCamelCase			)
							torch.manual_seed(0			)
							lowerCAmelCase						:   Tuple							=       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			)
							lowerCAmelCase						:   Union[str, Any]							=       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  ,					)
							lowerCAmelCase						:   Tuple							=       PaintByExampleImageEncoder(_UpperCamelCase  ,					proj_size=3_2			)
							lowerCAmelCase						:   Optional[int]							=       CLIPImageProcessor(crop_size=3_2  ,					size=3_2			)

							lowerCAmelCase						:   List[str]							=       {
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''image_encoder''': image_encoder,
							    '''safety_checker''': None,
							    '''feature_extractor''': feature_extractor,
							}
							return components





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		int			):
							lowerCAmelCase						:   List[str]							=       np.array(image.convert('''RGB'''			)			)
							lowerCAmelCase						:   Dict							=       image[None].transpose(0  ,					3  ,					1  ,					2			)
							lowerCAmelCase						:   Optional[int]							=       torch.from_numpy(_UpperCamelCase			).to(dtype=torch.floataa			) / 1_2_7.5 - 1.0
							return image





		def  _A							(							self      :		List[Any]  ,					lowerCamelCase__      :		Any="cpu"  ,					lowerCamelCase__      :		int=0			):
							lowerCAmelCase						:   List[str]							=       floats_tensor((1, 3, 3_2, 3_2)  ,					rng=random.Random(_UpperCamelCase			)			).to(_UpperCamelCase			)
							lowerCAmelCase						:   Union[str, Any]							=       image.cpu().permute(0  ,					2  ,					3  ,					1			)[0]
							lowerCAmelCase						:   Tuple							=       Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			).resize((6_4, 6_4)			)
							lowerCAmelCase						:   Dict							=       Image.fromarray(np.uinta(image + 4			)			).convert('''RGB'''			).resize((6_4, 6_4)			)
							lowerCAmelCase						:   List[str]							=       Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			).resize((3_2, 3_2)			)

							if str(_UpperCamelCase			).startswith('''mps'''			):
												lowerCAmelCase						:   Optional[int]							=       torch.manual_seed(_UpperCamelCase			)
							else:
												lowerCAmelCase						:   Optional[Any]							=       torch.Generator(device=_UpperCamelCase			).manual_seed(_UpperCamelCase			)
							lowerCAmelCase						:   Tuple							=       {
							    '''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      :		Union[str, Any]			):
							lowerCAmelCase						:   Optional[int]							=       self.get_dummy_components()

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase						:   Dict							=       PaintByExamplePipeline(**_UpperCamelCase			)
							lowerCAmelCase						:   Union[str, Any]							=       pipe.to('''cpu'''			)
							pipe.set_progress_bar_config(disable=_UpperCamelCase			)

							lowerCAmelCase						:   Union[str, Any]							=       self.get_dummy_inputs()
							lowerCAmelCase						:   List[str]							=       pipe(**_UpperCamelCase			)
							lowerCAmelCase						:   Optional[int]							=       output.images

							lowerCAmelCase						:   Dict							=       image[0, -3:, -3:, -1]

							assert image.shape == (1, 6_4, 6_4, 3)
							lowerCAmelCase						:   Dict							=       np.array([0.4_6_8_6, 0.5_6_8_7, 0.4_0_0_7, 0.5_2_1_8, 0.5_7_4_1, 0.4_4_8_2, 0.4_9_4_0, 0.4_6_2_9, 0.4_5_0_3]			)

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





		def  _A							(							self      :		Optional[int]			):
							lowerCAmelCase						:   Optional[Any]							=       '''cpu'''
							lowerCAmelCase						:   Union[str, Any]							=       self.get_dummy_inputs()
							inputs.pop('''mask_image'''			)
							lowerCAmelCase						:   Optional[int]							=       self.convert_to_pt(inputs.pop('''image'''			)			)
							lowerCAmelCase						:   List[str]							=       image.clamp(0  ,					1			) / 2

							# make sure here that pndm scheduler skips prk
							lowerCAmelCase						:   List[str]							=       PaintByExamplePipeline(**self.get_dummy_components()			)
							lowerCAmelCase						:   Any							=       pipe.to(_UpperCamelCase			)
							pipe.set_progress_bar_config(disable=_UpperCamelCase			)

							lowerCAmelCase						:   str							=       pipe(image=_UpperCamelCase  ,					mask_image=mask_image[:, 0]  ,					**_UpperCamelCase			)
							lowerCAmelCase						:   Any							=       output.images

							lowerCAmelCase						:   Optional[int]							=       image.cpu().permute(0  ,					2  ,					3  ,					1			)[0]
							lowerCAmelCase						:   Optional[int]							=       mask_image.cpu().permute(0  ,					2  ,					3  ,					1			)[0]

							lowerCAmelCase						:   Optional[int]							=       Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			)
							lowerCAmelCase						:   List[str]							=       Image.fromarray(np.uinta(_UpperCamelCase			)			).convert('''RGB'''			)

							lowerCAmelCase						:   Any							=       pipe(**self.get_dummy_inputs()			)
							lowerCAmelCase						:   Any							=       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  _A							(							self      :		List[str]			):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3			)



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





		def  _A							(							self      :		List[str]			):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def  _A							(							self      :		Optional[Any]			):
							lowerCAmelCase						:   List[str]							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/dog_in_bucket.png'''			)
							lowerCAmelCase						:   Tuple							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/mask.png'''			)
							lowerCAmelCase						:   Tuple							=       load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/paint_by_example/panda.jpg'''			)

							lowerCAmelCase						:   Dict							=       PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''			)
							lowerCAmelCase						:   str							=       pipe.to(_UpperCamelCase			)
							pipe.set_progress_bar_config(disable=_UpperCamelCase			)

							lowerCAmelCase						:   Dict							=       torch.manual_seed(3_2_1			)
							lowerCAmelCase						:   Optional[Any]							=       pipe(
							    image=_UpperCamelCase  ,					mask_image=_UpperCamelCase  ,					example_image=_UpperCamelCase  ,					generator=_UpperCamelCase  ,					guidance_scale=5.0  ,					num_inference_steps=5_0  ,					output_type='''np'''  ,					)

							lowerCAmelCase						:   List[Any]							=       output.images

							lowerCAmelCase						:   str							=       image[0, -3:, -3:, -1]

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase						:   List[str]							=       np.array([0.4_8_3_4, 0.4_8_1_1, 0.4_8_7_4, 0.5_1_2_2, 0.5_0_8_1, 0.5_1_4_4, 0.5_2_9_1, 0.5_2_9_0, 0.5_3_7_4]			)

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


def       UpperCAmelCase__							(    __magic_name__  :       Union[str, Any]							,					__magic_name__  :       int					):


					'''simple docstring'''

					sequence.append(__magic_name__					)

					lowerCAmelCase						:   Tuple							=       0
					while sequence[index] != target:
										index += 1

					sequence.pop()

					if index == len(__magic_name__					):
										return None

					return index


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE     :				Dict             =       input('Enter numbers separated by comma:\n').strip()
		__SCREAMING_SNAKE_CASE     :				int             =       [int(item) for item in user_input.split(',')]

		__SCREAMING_SNAKE_CASE     :				str             =       input('Enter a single number to be found in the list:\n')
		__SCREAMING_SNAKE_CASE     :				Tuple             =       int(target_input)
		__SCREAMING_SNAKE_CASE     :				Union[str, Any]             =       sentinel_linear_search(sequence, target)
		if result is not None:
				print(f"""{target} found at positions: {result}""")
		else:
				print('Not found')
