



"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class  A_     (	unittest.TestCase							):



						"""simple docstring"""






						@property
						def       UpperCAmelCase__	(   self					:Union[str, Any]   )							->     Optional[Any]:
												UpperCAmelCase			 =					1
												UpperCAmelCase			 =					3
												UpperCAmelCase			 =					8
												UpperCAmelCase			 =					8

												UpperCAmelCase			 =					torch.rand((batch_size, num_channels, height, width)   )

												return sample






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[Any]   )							->     Dict:
												if isinstance(image[0]  ,    PIL.Image.Image   ):
																		return np.stack([np.array(lowercase_   ) for i in image]  ,    axis=0   )
												elif isinstance(lowercase_  ,    torch.Tensor   ):
																		return image.cpu().numpy().transpose(0  ,    2  ,    3  ,    1   )
												return image






						def       UpperCAmelCase__	(   self					:Optional[int]   )							->     Tuple:
												UpperCAmelCase			 =					VaeImageProcessor(do_resize=lowercase_  ,    do_normalize=lowercase_   )

												UpperCAmelCase			 =					self.dummy_sample
												UpperCAmelCase			 =					self.to_np(lowercase_   )

												for output_type in ["pt", "np", "pil"]:
																		UpperCAmelCase			 =					image_processor.postprocess(
																		    image_processor.preprocess(lowercase_   )  ,    output_type=lowercase_  ,    )
																		UpperCAmelCase			 =					self.to_np(lowercase_   )
																		UpperCAmelCase			 =					(input_np * 2_55).round() if output_type == 'pil' else input_np
																		assert (
																		    np.abs(in_np - out_np   ).max() < 1E-6
																		), f"""decoded output does not match input for output_type {output_type}"""






						def       UpperCAmelCase__	(   self					:Tuple   )							->     Dict:
												UpperCAmelCase			 =					VaeImageProcessor(do_resize=lowercase_  ,    do_normalize=lowercase_   )
												UpperCAmelCase			 =					self.dummy_sample.cpu().numpy().transpose(0  ,    2  ,    3  ,    1   )

												for output_type in ["pt", "np", "pil"]:
																		UpperCAmelCase			 =					image_processor.postprocess(image_processor.preprocess(lowercase_   )  ,    output_type=lowercase_   )

																		UpperCAmelCase			 =					self.to_np(lowercase_   )
																		UpperCAmelCase			 =					(input_np * 2_55).round() if output_type == 'pil' else input_np
																		assert (
																		    np.abs(in_np - out_np   ).max() < 1E-6
																		), f"""decoded output does not match input for output_type {output_type}"""






						def       UpperCAmelCase__	(   self					:Any   )							->     List[str]:
												UpperCAmelCase			 =					VaeImageProcessor(do_resize=lowercase_  ,    do_normalize=lowercase_   )

												UpperCAmelCase			 =					self.dummy_sample.cpu().numpy().transpose(0  ,    2  ,    3  ,    1   )
												UpperCAmelCase			 =					image_processor.numpy_to_pil(lowercase_   )

												for output_type in ["pt", "np", "pil"]:
																		UpperCAmelCase			 =					image_processor.postprocess(image_processor.preprocess(lowercase_   )  ,    output_type=lowercase_   )
																		for i, o in zip(lowercase_  ,    lowercase_   ):
																								UpperCAmelCase			 =					np.array(lowercase_   )
																								UpperCAmelCase			 =					self.to_np(lowercase_   ) if output_type == 'pil' else (self.to_np(lowercase_   ) * 2_55).round()
																								assert (
																								    np.abs(in_np - out_np   ).max() < 1E-6
																								), f"""decoded output does not match input for output_type {output_type}"""






						def       UpperCAmelCase__	(   self					:List[str]   )							->     str:
												UpperCAmelCase			 =					VaeImageProcessor(do_resize=lowercase_  ,    do_normalize=lowercase_   )

												UpperCAmelCase			 =					self.dummy_sample
												UpperCAmelCase			 =					input_pt_ad.squeeze(0   )

												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )
												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )

												UpperCAmelCase			 =					self.to_np(self.dummy_sample   )
												UpperCAmelCase			 =					input_np_ad.squeeze(0   )

												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )
												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )

												assert np.abs(out_pt_ad - out_pt_ad   ).max() < 1E-6
												assert np.abs(out_np_ad - out_np_ad   ).max() < 1E-6






						def       UpperCAmelCase__	(   self					:str   )							->     int:
												UpperCAmelCase			 =					VaeImageProcessor(do_resize=lowercase_  ,    do_normalize=lowercase_   )

												UpperCAmelCase			 =					self.dummy_sample
												UpperCAmelCase			 =					list(lowercase_   )

												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )

												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )

												UpperCAmelCase			 =					self.to_np(self.dummy_sample   )
												list(lowercase_   )

												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )

												UpperCAmelCase			 =					image_processor.postprocess(
												    image_processor.preprocess(lowercase_   )  ,    output_type='np'  ,    )

												assert np.abs(out_pt_ad - out_pt_list   ).max() < 1E-6
												assert np.abs(out_np_ad - out_np_list   ).max() < 1E-6










"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    lowercase_  ,    generator=lowercase_   ).prev_sample

												UpperCAmelCase			 =					(image / 2 + 0.5).clamp(0  ,    1   )
												UpperCAmelCase			 =					image.cpu().permute(0  ,    2  ,    3  ,    1   ).numpy()
												if output_type == "pil":
																		UpperCAmelCase			 =					self.numpy_to_pil(lowercase_   )

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowercase_   )





