






'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

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


class 				snake_case__     (			lowercase__):




					def __init__(       self       :						str							,				_A       :						str							,				_A       :						Optional[int]     )  ->    Dict:
								super().__init__()
								self.register_modules(unet=lowerCAmelCase__							,				scheduler=lowerCAmelCase__     )





					@torch.no_grad()
					def __call__(       self       :						Dict							,				_A       :						int = 1							,				_A       :						Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				_A       :						int = 10_00							,				_A       :						Optional[str] = "pil"							,				_A       :						bool = True							,				)  ->    Union[ImagePipelineOutput, Tuple]:
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size							,				lowerCAmelCase__     ):
											UpperCAmelCase_     :   str       =   (
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
								else:
											UpperCAmelCase_     :   List[Any]       =   (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_     :   Tuple       =   randn_tensor(lowerCAmelCase__							,				generator=lowerCAmelCase__     )
											UpperCAmelCase_     :   Optional[Any]       =   image.to(self.device     )
								else:
											UpperCAmelCase_     :   Tuple       =   randn_tensor(lowerCAmelCase__							,				generator=lowerCAmelCase__							,				device=self.device     )

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

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

											# 2. compute previous image: x_t -> x_t-1
											UpperCAmelCase_     :   Optional[Any]       =   self.scheduler.step(lowerCAmelCase__							,				lowerCAmelCase__							,				lowerCAmelCase__							,				generator=lowerCAmelCase__     ).prev_sample

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

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=lowerCAmelCase__     )














'''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     )


