





from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 				_UpperCamelCase       (						_lowercase  ):

       def __init__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			List[Any]:




              """simple docstring"""


              super().__init__()
              self.register_modules(unet=lowerCamelCase__       , scheduler=lowerCamelCase__		)



       @torch.no_grad()
       def __call__(		self:			Dict       , _SCREAMING_SNAKE_CASE:			List[str] = 1       , _SCREAMING_SNAKE_CASE:			List[Any] = None       , _SCREAMING_SNAKE_CASE:			Tuple = 50       , _SCREAMING_SNAKE_CASE:			Optional[int] = "pil"       , _SCREAMING_SNAKE_CASE:			Optional[Any] = True       , **_SCREAMING_SNAKE_CASE:			Optional[Any]       , )							->			Union[ImagePipelineOutput, Tuple]:




              """simple docstring"""


              UpperCamelCase_ 							=	torch.randn(
                  (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       , generator=lowerCamelCase__       , )
              UpperCamelCase_ 							=	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_ 							=	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_ 							=	self.scheduler.step(lowerCamelCase__       , lowerCamelCase__       , lowerCamelCase__		).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(lowerCamelCase__		)

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

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





"""simple docstring"""






import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info



def 			A__		(					_UpperCAmelCase    :      Optional[int]	)							->	Tuple:

	'''simple docstring'''


	class 			SCREAMING_SNAKE_CASE_  :




								'''simple docstring'''





								def __init__(						self	,					lowerCamelCase__)   ->	None:







									'''simple docstring'''




									assert isinstance(lowerCamelCase__	,					(bytes, str))
									snake_case__  :						int		=      content







								def        UpperCAmelCase (						self)   ->	Union[str, Any]:







									'''simple docstring'''




									return json.loads(self.content)

	def mock_response(*_UpperCAmelCase    :      str				,    **_UpperCAmelCase    :      Optional[int]	):
		assert args[0] == AUTHENTICATED_USER_ENDPOINT
		assert "Authorization" in kwargs["headers"]
		assert kwargs["headers"]["Authorization"].startswith("token "	)
		assert "Accept" in kwargs["headers"]
		return FakeResponse(b"{\"login\":\"test\",\"id\":1}"	)

	monkeypatch.setattr(_UpperCAmelCase				,    "get"				,    _UpperCAmelCase	)
	snake_case__  :						str		=      fetch_github_info("token"	)
	assert result["login"] == "test"
	assert result["id"] == 1





