


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 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					def __init__(					self			,							snake_case_			,							snake_case_			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								super().__init__()
								self.register_modules(vqvae=snake_case_			,							unet=snake_case_			,							scheduler=snake_case_    )






					@torch.no_grad()
					def __call__(					self			,							snake_case_ = 1			,							snake_case_ = None			,							snake_case_ = 0.0			,							snake_case_ = 5_0			,							snake_case_ = "pil"			,							snake_case_ = True			,							**snake_case_			,							) ->      Union[Tuple, ImagePipelineOutput]:







								'''simple docstring'''

								__lowercase			       = randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,							generator=snake_case_			,							)
								__lowercase			       = latents.to(self.device    )

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

								self.scheduler.set_timesteps(snake_case_    )

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

								__lowercase			       = {}
								if accepts_eta:
											__lowercase			       = eta

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

								# decode the image latents with the VAE
								__lowercase			       = self.vqvae.decode(snake_case_    ).sample

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

								if not return_dict:
											return (image,)

								return ImagePipelineOutput(images=snake_case_    )






def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = hex_num.strip()
			if not hex_num:
						raise ValueError('''No value was passed to the function'''	)

			__lowercase			       = hex_num[0] == '''-'''
			if is_negative:
						__lowercase			       = hex_num[1:]

			try:
						__lowercase			       = int(_UpperCamelCase							,      16	)
			except ValueError:
						raise ValueError('''Invalid value was passed to the function'''	)

			__lowercase			       = ''''''
			while int_num > 0:
						__lowercase			       = str(int_num % 2	) + bin_str
						int_num >>= 1

			return int(('''-''' + bin_str) if is_negative else bin_str	)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


