






'''simple docstring'''





from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 				snake_case__     (			UpperCamelCase):




					def __init__(       self       :						Any							,				_A       :						List[Any]							,				_A       :						Tuple     )  ->    int:
								super().__init__()
								self.register_modules(unet=_A							,				scheduler=_A     )





					@torch.no_grad()
					def __call__(       self       :						Any							,				_A       :						int = 1							,				_A       :						Optional[torch.Generator] = None							,				_A       :						int = 50							,				_A       :						Optional[str] = "pil"							,				_A       :						bool = True							,				**_A       :						int							,				)  ->    Union[ImagePipelineOutput, Tuple]:
								UpperCAmelCase_     :   Union[str, Any]       =   torch.randn(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,				generator=_A							,				)
								UpperCAmelCase_     :   List[str]       =   image.to(self.device     )

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

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

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											UpperCAmelCase_     :   Dict       =   self.scheduler.step(_A							,				_A							,				_A     ).prev_sample

								UpperCAmelCase_     :   Any       =   (image / 2 + 0.5).clamp(0							,				1     )
								UpperCAmelCase_     :   int       =   image.cpu().permute(0							,				2							,				3							,				1     ).numpy()
								if output_type == "pil":
											UpperCAmelCase_     :   str       =   self.numpy_to_pil(_A     )

								if not return_dict:
											return (image,), "This is a local test"

								return ImagePipelineOutput(images=_A     ), "This is a local test"














'''simple docstring'''





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 				snake_case__     (			unittest.TestCase):




					def 						A     (       self       :						Optional[Any]     )  ->    List[str]:
								UpperCAmelCase_     :   Optional[Any]       =   '''runwayml/stable-diffusion-v1-5'''
								UpperCAmelCase_     :   Optional[int]       =   TextToVideoZeroPipeline.from_pretrained(_A							,				torch_dtype=torch.floataa     ).to('''cuda'''     )
								UpperCAmelCase_     :   Tuple       =   DDIMScheduler.from_config(pipe.scheduler.config     )
								UpperCAmelCase_     :   Tuple       =   torch.Generator(device='''cuda'''     ).manual_seed(0     )

								UpperCAmelCase_     :   List[Any]       =   '''A bear is playing a guitar on Times Square'''
								UpperCAmelCase_     :   List[Any]       =   pipe(prompt=_A							,				generator=_A     ).images

								UpperCAmelCase_     :   Any       =   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     )


