
'''simple docstring'''







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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 			lowerCAmelCase__       (  lowerCamelCase_					):






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							):


											"""simple docstring"""





											super().__init__()
											self.register_modules(transformer=__SCREAMING_SNAKE_CASE    ,							vae=__SCREAMING_SNAKE_CASE    ,							scheduler=__SCREAMING_SNAKE_CASE		)

											# create a imagenet -> id dictionary for easier use
											lowercase_     :					Union[str, Any]													=	{}
											if idalabel is not None:
																	for key, value in idalabel.items():
																							for label in value.split(''','''		):
																													lowercase_     :					int													=	int(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					str													=	dict(sorted(self.labels.items()		)		)






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if not isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																	lowercase_     :					Union[str, Any]													=	list(__SCREAMING_SNAKE_CASE		)

											for l in label:
																	if l not in self.labels:
																							raise ValueError(
																							    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''		)

											return [self.labels[l] for l in label]






					@torch.no_grad()
					def __call__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = 4.0    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = 50    ,							__SCREAMING_SNAKE_CASE = "pil"    ,							__SCREAMING_SNAKE_CASE = True    ,							):


											"""simple docstring"""





											lowercase_     :					Tuple													=	len(__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	self.transformer.config.sample_size
											lowercase_     :					Dict													=	self.transformer.config.in_channels

											lowercase_     :					List[str]													=	randn_tensor(
											    shape=(batch_size, latent_channels, latent_size, latent_size)    ,							generator=__SCREAMING_SNAKE_CASE    ,							device=self.device    ,							dtype=self.transformer.dtype    ,							)
											lowercase_     :					Any													=	torch.cat([latents] * 2		) if guidance_scale > 1 else latents

											lowercase_     :					Any													=	torch.tensor(__SCREAMING_SNAKE_CASE    ,							device=self.device		).reshape(-1		)
											lowercase_     :					List[Any]													=	torch.tensor([10_00] * batch_size    ,							device=self.device		)
											lowercase_     :					str													=	torch.cat([class_labels, class_null]    ,							0		) if guidance_scale > 1 else class_labels

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

											for t in self.progress_bar(self.scheduler.timesteps		):
																	if guidance_scale > 1:
																							lowercase_     :					Optional[Any]													=	latent_model_input[: len(__SCREAMING_SNAKE_CASE		) // 2]
																							lowercase_     :					List[str]													=	torch.cat([half, half]    ,							dim=0		)
																	lowercase_     :					Tuple													=	self.scheduler.scale_model_input(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	lowercase_     :					int													=	t
																	if not torch.is_tensor(__SCREAMING_SNAKE_CASE		):
																							# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																							# This would be a good case for the `match` statement (Python 3.10+)
																							lowercase_     :					int													=	latent_model_input.device.type == '''mps'''
																							if isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																													lowercase_     :					List[str]													=	torch.floataa if is_mps else torch.floataa
																							else:
																													lowercase_     :					Optional[Any]													=	torch.intaa if is_mps else torch.intaa
																							lowercase_     :					List[str]													=	torch.tensor([timesteps]    ,							dtype=__SCREAMING_SNAKE_CASE    ,							device=latent_model_input.device		)
																	elif len(timesteps.shape		) == 0:
																							lowercase_     :					Union[str, Any]													=	timesteps[None].to(latent_model_input.device		)
																	# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																	lowercase_     :					Optional[Any]													=	timesteps.expand(latent_model_input.shape[0]		)
																	# predict noise model_output
																	lowercase_     :					Optional[Any]													=	self.transformer(
																	    __SCREAMING_SNAKE_CASE    ,							timestep=__SCREAMING_SNAKE_CASE    ,							class_labels=__SCREAMING_SNAKE_CASE		).sample

																	# perform guidance
																	if guidance_scale > 1:
																							lowercase_     :					Tuple													=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																							lowercase_     :					Dict													=	torch.split(__SCREAMING_SNAKE_CASE    ,							len(__SCREAMING_SNAKE_CASE		) // 2    ,							dim=0		)

																							lowercase_     :					Optional[Any]													=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																							lowercase_     :					List[Any]													=	torch.cat([half_eps, half_eps]    ,							dim=0		)

																							lowercase_     :					List[Any]													=	torch.cat([eps, rest]    ,							dim=1		)

																	# learned sigma
																	if self.transformer.config.out_channels // 2 == latent_channels:
																							lowercase_     :					List[Any]													=	torch.split(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							dim=1		)
																	else:
																							lowercase_     :					Tuple													=	noise_pred

																	# compute previous image: x_t -> x_t-1
																	lowercase_     :					List[str]													=	self.scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		).prev_sample

											if guidance_scale > 1:
																	lowercase_     :					str													=	latent_model_input.chunk(2    ,							dim=0		)
											else:
																	lowercase_     :					Union[str, Any]													=	latent_model_input

											lowercase_     :					Dict													=	1 / self.vae.config.scaling_factor * latents
											lowercase_     :					Tuple													=	self.vae.decode(__SCREAMING_SNAKE_CASE		).sample

											lowercase_     :					Optional[int]													=	(samples / 2 + 0.5).clamp(0    ,							1		)

											# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
											lowercase_     :					List[str]													=	samples.cpu().permute(0    ,							2    ,							3    ,							1		).float().numpy()

											if output_type == "pil":
																	lowercase_     :					Any													=	self.numpy_to_pil(__SCREAMING_SNAKE_CASE		)

											if not return_dict:
																	return (samples,)

											return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)





'''simple docstring'''







from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def  snake_case_		(    __SCREAMING_SNAKE_CASE :			int							):



						"""simple docstring"""





						lowercase_     :					Tuple													=	prime_factors(__SCREAMING_SNAKE_CASE							)
						if is_square_free(__SCREAMING_SNAKE_CASE							):
												return -1 if len(__SCREAMING_SNAKE_CASE							) % 2 else 1
						return 0


if __name__ == "__main__":
							import doctest

							doctest.testmod()



