


'''simple docstring'''






import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      super().__init__()
      self.register_modules(vqvae=__magic_name__ ,			unet=__magic_name__ ,			scheduler=__magic_name__  )




 @torch.no_grad()
 def __call__(	self ,			__magic_name__ = 1 ,			__magic_name__ = None ,			__magic_name__ = 0.0 ,			__magic_name__ = 50 ,			__magic_name__ = "pil" ,			__magic_name__ = True ,			**__magic_name__ ,			)   ->							Union[Tuple, ImagePipelineOutput]:
      _a      =							randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=__magic_name__ ,			)
      _a      =							latents.to(self.device  )

      # scale the initial noise by the standard deviation required by the scheduler
      _a      =							latents * self.scheduler.init_noise_sigma

      self.scheduler.set_timesteps(__magic_name__  )

      # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
      _a      =							'eta' in set(inspect.signature(self.scheduler.step  ).parameters.keys()  )

      _a      =							{}
      if accepts_eta:
           _a      =							eta

      for t in self.progress_bar(self.scheduler.timesteps  ):
           _a      =							self.scheduler.scale_model_input(__magic_name__ ,			__magic_name__  )
           # predict the noise residual
           _a      =							self.unet(__magic_name__ ,			__magic_name__  ).sample
           # compute the previous noisy sample x_t -> x_t-1
           _a      =							self.scheduler.step(__magic_name__ ,			__magic_name__ ,			__magic_name__ ,			**__magic_name__  ).prev_sample

      # decode the image latents with the VAE
      _a      =							self.vqvae.decode(__magic_name__  ).sample

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

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=__magic_name__  )





'''simple docstring'''






import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline






@pytest.mark.integration
def 		_A			()				->					Optional[Any]:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT						):
          with pytest.raises(lowerCAmelCase__						):
               requests.request('GET'			,  'https://huggingface.co'						)
          with pytest.raises(requests.exceptions.ConnectTimeout						):
               requests.request('GET'			,  'https://huggingface.co'			,  timeout=1.0						)






@pytest.mark.integration
def 		_A			()				->					Any:




     '''simple docstring'''



     with offline(OfflineSimulationMode.CONNECTION_FAILS						):
          with pytest.raises(requests.exceptions.ConnectionError						):
               requests.request('GET'			,  'https://huggingface.co'						)






def 		_A			()				->					Dict:




     '''simple docstring'''



     with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1						):
          with pytest.raises(lowerCAmelCase__						):
               http_head('https://huggingface.co'						)

