
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						(   SCREAMING_SNAKE_CASE__			,    unittest.TestCase						):
							lowercase_			:			Optional[int]			   =PaintByExamplePipeline
							lowercase_			:			List[str]			   =IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
							lowercase_			:			str			   =IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							lowercase_			:			str			   =frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




							def 					A__ (							self):
												torch.manual_seed(0)
												lowercase		      =		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  ,)
												lowercase		      =		PNDMScheduler(skip_prk_steps=A__)
												torch.manual_seed(0)
												lowercase		      =		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)
												lowercase		      =		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  ,)
												lowercase		      =		PaintByExampleImageEncoder(A__  ,proj_size=3_2)
												lowercase		      =		CLIPImageProcessor(crop_size=3_2  ,size=3_2)

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




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




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

												if str(A__).startswith('''mps'''):
																	lowercase		      =		torch.manual_seed(A__)
												else:
																	lowercase		      =		torch.Generator(device=A__).manual_seed(A__)
												lowercase		      =		{
												    '''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):
												lowercase		      =		self.get_dummy_components()

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

												lowercase		      =		self.get_dummy_inputs()
												lowercase		      =		pipe(**A__)
												lowercase		      =		output.images

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

												assert image.shape == (1, 6_4, 6_4, 3)
												lowercase		      =		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):
												lowercase		      =		'''cpu'''
												lowercase		      =		self.get_dummy_inputs()
												inputs.pop('''mask_image''')
												lowercase		      =		self.convert_to_pt(inputs.pop('''image'''))
												lowercase		      =		image.clamp(0  ,1) / 2

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

												lowercase		      =		pipe(image=A__  ,mask_image=mask_image[:, 0]  ,**A__)
												lowercase		      =		output.images

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

												lowercase		      =		Image.fromarray(np.uinta(A__)).convert('''RGB''')
												lowercase		      =		Image.fromarray(np.uinta(A__)).convert('''RGB''')

												lowercase		      =		pipe(**self.get_dummy_inputs())
												lowercase		      =		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):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3)







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




							def 					A__ (							self):
												# clean up the VRAM after each test
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()





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

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

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

												lowercase		      =		output.images

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowercase		      =		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






import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference







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




							def 					A__ (							self):
												lowercase		      =		'''runwayml/stable-diffusion-v1-5'''
												lowercase		      =		TextToVideoZeroPipeline.from_pretrained(A__  ,torch_dtype=torch.floataa).to('''cuda''')
												lowercase		      =		DDIMScheduler.from_config(pipe.scheduler.config)
												lowercase		      =		torch.Generator(device='''cuda''').manual_seed(0)

												lowercase		      =		'''A bear is playing a guitar on Times Square'''
												lowercase		      =		pipe(prompt=A__  ,generator=A__).images

												lowercase		      =		load_pt(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt''')

												assert_mean_pixel_difference(A__  ,A__)




