

from typing import List, Optional, Tuple, Union

import torch

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







class 			lowercase    (	lowercase_      ):




				def __init__( self				,       snake_case				,       snake_case	):
											super().__init__()

											# make sure scheduler can always be converted to DDIM
											snake_case_          =   DDIMScheduler.from_config(scheduler.config	)

											self.register_modules(unet=snake_case				,       scheduler=snake_case	)




				@torch.no_grad()
				def __call__( self				,       snake_case = 1				,       snake_case = None				,       snake_case = 0.0				,       snake_case = 50				,       snake_case = None				,       snake_case = "pil"				,       snake_case = True				,       ):

											# Sample gaussian noise to begin loop
											if isinstance(self.unet.config.sample_size				,       snake_case	):
																		snake_case_          =   (
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
											else:
																		snake_case_          =   (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

											if isinstance(snake_case				,       snake_case	) and len(snake_case	) != batch_size:
																		raise ValueError(
																		    F'''You have passed a list of generators of length {len(snake_case	)}, but requested an effective batch'''
																		    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

											snake_case_          =   randn_tensor(snake_case				,       generator=snake_case				,       device=self.device				,       dtype=self.unet.dtype	)

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

											for t in self.progress_bar(self.scheduler.timesteps	):
																		# 1. predict noise model_output
																		snake_case_          =   self.unet(snake_case				,       snake_case	).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
																		snake_case_          =   self.scheduler.step(
																		    snake_case				,       snake_case				,       snake_case				,       eta=snake_case				,       use_clipped_model_output=snake_case				,       generator=snake_case	).prev_sample

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=snake_case	)







from __future__ import annotations

import os
from typing import Any

import requests

_UpperCAmelCase		:       int						    =	"""https://api.github.com"""

# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
_UpperCAmelCase		:       Dict						    =	BASE_URL + """/user"""

# https://github.com/settings/tokens
_UpperCAmelCase		:       Optional[Any]						    =	os.environ.get("""USER_TOKEN""", """""")





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   {
							    'Authorization': F'''token {auth_token}''',
							    'Accept': 'application/vnd.github.v3+json',
							}
							return requests.get(UpperCamelCase__       ,							headers=UpperCamelCase__				).json()


if __name__ == "__main__":  # pragma: no cover
					if USER_TOKEN:
										for key, value in fetch_github_info(USER_TOKEN).items():
															print(F'''{key}: {value}''')
					else:
										raise ValueError("""'USER_TOKEN' field cannot be empty.""")




