'''simple docstring'''
from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput






class 			__lowercase		(						__A						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         super().__init__()
         self.register_modules(unet=_lowerCamelCase     ,			scheduler=_lowerCamelCase    )







     @torch.no_grad()
     def __call__(		self     ,			_lowerCamelCase = 1     ,			_lowerCamelCase = None     ,			_lowerCamelCase = 5_0     ,			_lowerCamelCase = "pil"     ,			_lowerCamelCase = True     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,			generator=_lowerCamelCase     ,			)
         __UpperCamelCase	:	Optional[int]		    =  image.to(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	:	str		    =  self.unet(_lowerCamelCase     ,			_lowerCamelCase    ).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	:	List[Any]		    =  self.scheduler.step(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ).prev_sample

         __UpperCamelCase	:	List[str]		    =  (image / 2 + 0.5).clamp(0     ,			1    )
         __UpperCamelCase	:	Any		    =  image.cpu().permute(0     ,			2     ,			3     ,			1    ).numpy()
         if output_type == "pil":
             __UpperCamelCase	:	Tuple		    =  self.numpy_to_pil(_lowerCamelCase    )

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

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




'''simple docstring'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url







@pytest.mark.parametrize('repo_id'				,     ['canonical_dataset_name', 'org-name/dataset-name']			)
@pytest.mark.parametrize('path'				,     ['filename.csv', 'filename with blanks.csv']			)
@pytest.mark.parametrize('revision'				,     [None, 'v2']			)
def  _UpperCamelCase (     _a	:	Any				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  hf_hub_url(repo_id=_a				,     path=_a				,     revision=_a			)
    assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_a			)}"""



