
'''simple docstring'''

from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)


class 					a_					(_a      ):




							def __init__(       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = 0.93				,				snake_case_ = True				,				):
									_lowerCAmelCase    :					List[str]							=   mixing_coeff
									_lowerCAmelCase    :					int							=   leapfrog_steps

									super().__init__()
									self.register_modules(
									    vae=snake_case_				,				text_encoder=snake_case_				,				tokenizer=snake_case_				,				unet=snake_case_				,				scheduler=snake_case_				,				)

									_lowerCAmelCase    :					Optional[Any]							=   2 ** (len(self.vae.config.block_out_channels					) - 1)
									_lowerCAmelCase    :					str							=   VaeImageProcessor(vae_scale_factor=self.vae_scale_factor					)




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_ = None				,				snake_case_ = False					):
									_lowerCAmelCase    :					int							=   self.tokenizer(
									    snake_case_				,				padding="""max_length"""				,				max_length=self.tokenizer.model_max_length				,				truncation=snake_case_				,				return_tensors="""pt"""				,				)

									_lowerCAmelCase    :					int							=   self.text_encoder(text_inputs.input_ids.to(self.device					)					).last_hidden_state

									_lowerCAmelCase    :					Tuple							=   prompt_embeds.to(dtype=self.text_encoder.dtype				,				device=self.device					)

									if do_classifier_free_guidance:
											_lowerCAmelCase    :					Any							=   """""" if negative_prompt is None else negative_prompt

											_lowerCAmelCase    :					Any							=   self.tokenizer(
											    snake_case_				,				padding="""max_length"""				,				max_length=self.tokenizer.model_max_length				,				truncation=snake_case_				,				return_tensors="""pt"""				,				)

											_lowerCAmelCase    :					Union[str, Any]							=   self.text_encoder(uncond_input.input_ids.to(self.device					)					).last_hidden_state

											_lowerCAmelCase    :					List[str]							=   torch.cat([negative_prompt_embeds, prompt_embeds]					)

									return prompt_embeds




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
									_lowerCAmelCase    :					str							=   self.mixing_coeff * x + (1 - self.mixing_coeff) * y
									_lowerCAmelCase    :					Optional[Any]							=   self.mixing_coeff * y + (1 - self.mixing_coeff) * x

									return [x, y]




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
									_lowerCAmelCase    :					Tuple							=   (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
									_lowerCAmelCase    :					Union[str, Any]							=   (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

									return [x, y]




							def 							__UpperCamelCase    (       self				,				snake_case_					):
									# as self.alphas_cumprod is always in cpu
									_lowerCAmelCase    :					int							=   int(snake_case_					)

									_lowerCAmelCase    :					Any							=   self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

									return alpha_prod, 1 - alpha_prod




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				):
									_lowerCAmelCase    :					Dict							=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									_lowerCAmelCase    :					Union[str, Any]							=   self._get_alpha_and_beta(snake_case_					)
									_lowerCAmelCase    :					Dict							=   self._get_alpha_and_beta(snake_case_					)

									_lowerCAmelCase    :					Tuple							=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
									_lowerCAmelCase    :					Optional[Any]							=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

									_lowerCAmelCase    :					Optional[int]							=   (base - b_t * model_output) / a_t

									return model_input, next_model_input.to(base.dtype					)




							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				):
									_lowerCAmelCase    :					List[str]							=   timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

									_lowerCAmelCase    :					List[Any]							=   self._get_alpha_and_beta(snake_case_					)
									_lowerCAmelCase    :					Union[str, Any]							=   self._get_alpha_and_beta(snake_case_					)

									_lowerCAmelCase    :					Dict							=   (alpha_prod_t_prev / alpha_prod_t) ** 0.5
									_lowerCAmelCase    :					Any							=   -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
									_lowerCAmelCase    :					Dict							=   a_t * base + b_t * model_output

									return model_input, next_model_input.to(base.dtype					)




							@torch.no_grad()
							def 							__UpperCamelCase    (       self				,				snake_case_					):
									_lowerCAmelCase    :					List[Any]							=   1 / self.vae.config.scaling_factor * latents
									_lowerCAmelCase    :					List[Any]							=   self.vae.decode(snake_case_					).sample
									_lowerCAmelCase    :					Any							=   (image / 2 + 0.5).clamp(0				,				1					)
									return image




							@torch.no_grad()
							def 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = None				,				):
									_lowerCAmelCase    :					Dict							=   guidance_scale > 1.0

									_lowerCAmelCase    :					Any							=   image.to(device=self.device				,				dtype=text_embeds.dtype					)
									_lowerCAmelCase    :					Dict							=   self.vae.encode(snake_case_					).latent_dist.sample(snake_case_					)

									_lowerCAmelCase    :					Any							=   self.vae.config.scaling_factor * latent

									_lowerCAmelCase    :					str							=   [latent.clone(), latent.clone()]

									for i, t in tqdm(enumerate(snake_case_					)				,				total=len(snake_case_					)					):
											_lowerCAmelCase    :					Dict							=   self.noise_mixing_layer(x=coupled_latents[0]				,				y=coupled_latents[1]					)

											# j - model_input index, k - base index
											for j in range(2					):
													_lowerCAmelCase    :					int							=   j ^ 1

													if self.leapfrog_steps:
															if i % 2 == 0:
																	_lowerCAmelCase    :					Tuple							=   j, k

													_lowerCAmelCase    :					Any							=   coupled_latents[j]
													_lowerCAmelCase    :					Optional[Any]							=   coupled_latents[k]

													_lowerCAmelCase    :					List[Any]							=   torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

													_lowerCAmelCase    :					Union[str, Any]							=   self.unet(snake_case_				,				snake_case_				,				encoder_hidden_states=snake_case_					).sample

													if do_classifier_free_guidance:
															_lowerCAmelCase    :					List[Any]							=   noise_pred.chunk(2					)
															_lowerCAmelCase    :					Optional[int]							=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													_lowerCAmelCase    :					str							=   self.noise_step(
													    base=snake_case_				,				model_input=snake_case_				,				model_output=snake_case_				,				timestep=snake_case_				,				)

													_lowerCAmelCase    :					Any							=   model_input

									return coupled_latents







							@torch.no_grad()
							def __call__(       self				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_ = 3.0				,				snake_case_ = 5_0				,				snake_case_ = 0.8				,				snake_case_ = None				,				snake_case_ = None				,				snake_case_ = "pil"				,				):
									_lowerCAmelCase    :					Any							=   guidance_scale > 1.0

									_lowerCAmelCase    :					int							=   self.image_processor.preprocess(snake_case_					)

									_lowerCAmelCase    :					List[str]							=   self._encode_prompt(snake_case_				,				snake_case_				,				snake_case_					)
									_lowerCAmelCase    :					Dict							=   self._encode_prompt(snake_case_				,				snake_case_				,				snake_case_					)

									self.scheduler.set_timesteps(snake_case_				,				self.device					)

									_lowerCAmelCase    :					Optional[Any]							=   num_inference_steps - int(num_inference_steps * strength					)
									_lowerCAmelCase    :					Any							=   self.scheduler.timesteps[t_limit:]
									_lowerCAmelCase    :					Optional[Any]							=   fwd_timesteps.flip(0					)

									_lowerCAmelCase    :					Any							=   self.prepare_latents(snake_case_				,				snake_case_				,				snake_case_				,				snake_case_				,				snake_case_					)

									for i, t in tqdm(enumerate(snake_case_					)				,				total=len(snake_case_					)					):
											# j - model_input index, k - base index
											for k in range(2					):
													_lowerCAmelCase    :					Any							=   k ^ 1

													if self.leapfrog_steps:
															if i % 2 == 1:
																	_lowerCAmelCase    :					Dict							=   j, k

													_lowerCAmelCase    :					Any							=   coupled_latents[j]
													_lowerCAmelCase    :					Optional[Any]							=   coupled_latents[k]

													_lowerCAmelCase    :					Optional[Any]							=   torch.cat([model_input] * 2					) if do_classifier_free_guidance else model_input

													_lowerCAmelCase    :					Optional[Any]							=   self.unet(snake_case_				,				snake_case_				,				encoder_hidden_states=snake_case_					).sample

													if do_classifier_free_guidance:
															_lowerCAmelCase    :					Optional[int]							=   noise_pred.chunk(2					)
															_lowerCAmelCase    :					Dict							=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

													_lowerCAmelCase    :					str							=   self.denoise_step(
													    base=snake_case_				,				model_input=snake_case_				,				model_output=snake_case_				,				timestep=snake_case_				,				)

													_lowerCAmelCase    :					Optional[int]							=   model_input

											_lowerCAmelCase    :					Optional[Any]							=   self.denoise_mixing_layer(x=coupled_latents[0]				,				y=coupled_latents[1]					)

									# either one is fine
									_lowerCAmelCase    :					Union[str, Any]							=   coupled_latents[0]

									if output_type not in ["latent", "pt", "np", "pil"]:
											_lowerCAmelCase    :					Dict							=   (
											    f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
											    """`pil`, `np`, `pt`, `latent`"""
											)
											deprecate("""Unsupported output_type"""				,				"""1.0.0"""				,				snake_case_				,				standard_warn=snake_case_					)
											_lowerCAmelCase    :					Dict							=   """np"""

									if output_type == "latent":
											_lowerCAmelCase    :					List[Any]							=   final_latent
									else:
											_lowerCAmelCase    :					Optional[int]							=   self.decode_latents(snake_case_					)
											_lowerCAmelCase    :					int							=   self.image_processor.postprocess(snake_case_				,				output_type=snake_case_					)

									return image




'''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      ):
       __lowerCAmelCase      :			Union[str, Any]      = PaintByExamplePipeline
       __lowerCAmelCase      :			int      = IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
       __lowerCAmelCase      :			str      = IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       __lowerCAmelCase      :			Any      = frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess




       def 							__UpperCamelCase    (       self					):
         torch.manual_seed(0					)
         _lowerCAmelCase    :					Dict							=   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    :					List[Any]							=   PNDMScheduler(skip_prk_steps=snake_case_					)
         torch.manual_seed(0					)
         _lowerCAmelCase    :					List[Any]							=   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    :					List[str]							=   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    :					Optional[int]							=   PaintByExampleImageEncoder(snake_case_				,				proj_size=3_2					)
         _lowerCAmelCase    :					Optional[int]							=   CLIPImageProcessor(crop_size=3_2				,				size=3_2					)

         _lowerCAmelCase    :					Union[str, Any]							=   {
             """unet""": unet,
             """scheduler""": scheduler,
             """vae""": vae,
             """image_encoder""": image_encoder,
             """safety_checker""": None,
             """feature_extractor""": feature_extractor,
         }
         return components




       def 							__UpperCamelCase    (       self				,				snake_case_					):
         _lowerCAmelCase    :					Optional[int]							=   np.array(image.convert("""RGB"""					)					)
         _lowerCAmelCase    :					Any							=   image[None].transpose(0				,				3				,				1				,				2					)
         _lowerCAmelCase    :					List[str]							=   torch.from_numpy(snake_case_					).to(dtype=torch.floataa					) / 127.5 - 1.0
         return image




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

         if str(snake_case_					).startswith("""mps"""					):
           _lowerCAmelCase    :					str							=   torch.manual_seed(snake_case_					)
         else:
           _lowerCAmelCase    :					Tuple							=   torch.Generator(device=snake_case_					).manual_seed(snake_case_					)
         _lowerCAmelCase    :					str							=   {
             """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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Dict							=   self.get_dummy_components()

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Optional[Any]							=   PaintByExamplePipeline(**snake_case_					)
         _lowerCAmelCase    :					List[Any]							=   pipe.to("""cpu"""					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         _lowerCAmelCase    :					List[str]							=   pipe(**snake_case_					)
         _lowerCAmelCase    :					Any							=   output.images

         _lowerCAmelCase    :					Optional[int]							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         _lowerCAmelCase    :					Optional[Any]							=   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 							__UpperCamelCase    (       self					):
         _lowerCAmelCase    :					Optional[int]							=   """cpu"""
         _lowerCAmelCase    :					int							=   self.get_dummy_inputs()
         inputs.pop("""mask_image"""					)
         _lowerCAmelCase    :					Optional[int]							=   self.convert_to_pt(inputs.pop("""image"""					)					)
         _lowerCAmelCase    :					int							=   image.clamp(0				,				1					) / 2

         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					List[Any]							=   PaintByExamplePipeline(**self.get_dummy_components()					)
         _lowerCAmelCase    :					Optional[int]							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   pipe(image=snake_case_				,				mask_image=mask_image[:, 0]				,				**snake_case_					)
         _lowerCAmelCase    :					str							=   output.images

         _lowerCAmelCase    :					List[str]							=   image.cpu().permute(0				,				2				,				3				,				1					)[0]
         _lowerCAmelCase    :					List[str]							=   mask_image.cpu().permute(0				,				2				,				3				,				1					)[0]

         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)
         _lowerCAmelCase    :					Any							=   Image.fromarray(np.uinta(snake_case_					)					).convert("""RGB"""					)

         _lowerCAmelCase    :					str							=   pipe(**self.get_dummy_inputs()					)
         _lowerCAmelCase    :					int							=   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 							__UpperCamelCase    (       self					):
         super().test_inference_batch_single_identical(expected_max_diff=3E-3					)



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




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







       def 							__UpperCamelCase    (       self					):
         # make sure here that pndm scheduler skips prk
         _lowerCAmelCase    :					Union[str, Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/dog_in_bucket.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/mask.png"""					)
         _lowerCAmelCase    :					List[Any]							=   load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/paint_by_example/panda.jpg"""					)

         _lowerCAmelCase    :					str							=   PaintByExamplePipeline.from_pretrained("""Fantasy-Studio/Paint-by-Example"""					)
         _lowerCAmelCase    :					int							=   pipe.to(snake_case_					)
         pipe.set_progress_bar_config(disable=snake_case_					)

         _lowerCAmelCase    :					str							=   torch.manual_seed(3_2_1					)
         _lowerCAmelCase    :					Optional[int]							=   pipe(
             image=snake_case_				,				mask_image=snake_case_				,				example_image=snake_case_				,				generator=snake_case_				,				guidance_scale=5.0				,				num_inference_steps=5_0				,				output_type="""np"""				,				)

         _lowerCAmelCase    :					List[str]							=   output.images

         _lowerCAmelCase    :					List[str]							=   image[0, -3:, -3:, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _lowerCAmelCase    :					Any							=   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


