"""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		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					def __init__(						self			:				Dict   , _snake_case			:				TransformeraDModel   , _snake_case			:				AutoencoderKL   , _snake_case			:				KarrasDiffusionSchedulers   , _snake_case			:				Optional[Dict[int, str]] = None   , )							->       Any:
									super().__init__()
									self.register_modules(transformer=_snake_case   , vae=_snake_case   , scheduler=_snake_case		)

									# create a imagenet -> id dictionary for easier use
									SCREAMING_SNAKE_CASE__		  =				{}
									if idalabel is not None:
													for key, value in idalabel.items():
																	for label in value.split(","		):
																					SCREAMING_SNAKE_CASE__		  =				int(_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				dict(sorted(self.labels.items()		)		)



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Union[str, List[str]]		)							->       List[int]:

									if not isinstance(_snake_case   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				list(_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			:				Optional[Any]   , _snake_case			:				List[int]   , _snake_case			:				float = 4.0   , _snake_case			:				Optional[Union[torch.Generator, List[torch.Generator]]] = None   , _snake_case			:				int = 50   , _snake_case			:				Optional[str] = "pil"   , _snake_case			:				bool = True   , )							->       Union[ImagePipelineOutput, Tuple]:
									SCREAMING_SNAKE_CASE__		  =				len(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				self.transformer.config.sample_size
									SCREAMING_SNAKE_CASE__		  =				self.transformer.config.in_channels

									SCREAMING_SNAKE_CASE__		  =				randn_tensor(
									    shape=(batch_size, latent_channels, latent_size, latent_size)   , generator=_snake_case   , device=self.device   , dtype=self.transformer.dtype   , )
									SCREAMING_SNAKE_CASE__		  =				torch.cat([latents] * 2		) if guidance_scale > 1 else latents

									SCREAMING_SNAKE_CASE__		  =				torch.tensor(_snake_case   , device=self.device		).reshape(-1		)
									SCREAMING_SNAKE_CASE__		  =				torch.tensor([1000] * batch_size   , device=self.device		)
									SCREAMING_SNAKE_CASE__		  =				torch.cat([class_labels, class_null]   , 0		) if guidance_scale > 1 else class_labels

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

									for t in self.progress_bar(self.scheduler.timesteps		):
													if guidance_scale > 1:
																	SCREAMING_SNAKE_CASE__		  =				latent_model_input[: len(_snake_case		) // 2]
																	SCREAMING_SNAKE_CASE__		  =				torch.cat([half, half]   , dim=0		)
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.scale_model_input(_snake_case   , _snake_case		)

													SCREAMING_SNAKE_CASE__		  =				t
													if not torch.is_tensor(_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+)
																	SCREAMING_SNAKE_CASE__		  =				latent_model_input.device.type == "mps"
																	if isinstance(_snake_case   , _snake_case		):
																					SCREAMING_SNAKE_CASE__		  =				torch.floataa if is_mps else torch.floataa
																	else:
																					SCREAMING_SNAKE_CASE__		  =				torch.intaa if is_mps else torch.intaa
																	SCREAMING_SNAKE_CASE__		  =				torch.tensor([timesteps]   , dtype=_snake_case   , device=latent_model_input.device		)
													elif len(timesteps.shape		) == 0:
																	SCREAMING_SNAKE_CASE__		  =				timesteps[None].to(latent_model_input.device		)
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													SCREAMING_SNAKE_CASE__		  =				timesteps.expand(latent_model_input.shape[0]		)
													# predict noise model_output
													SCREAMING_SNAKE_CASE__		  =				self.transformer(
													    _snake_case   , timestep=_snake_case   , class_labels=_snake_case		).sample

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

																	SCREAMING_SNAKE_CASE__		  =				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																	SCREAMING_SNAKE_CASE__		  =				torch.cat([half_eps, half_eps]   , dim=0		)

																	SCREAMING_SNAKE_CASE__		  =				torch.cat([eps, rest]   , dim=1		)

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																	SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				torch.split(_snake_case   , _snake_case   , dim=1		)
													else:
																	SCREAMING_SNAKE_CASE__		  =				noise_pred

													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__		  =				self.scheduler.step(_snake_case   , _snake_case   , _snake_case		).prev_sample

									if guidance_scale > 1:
													SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				latent_model_input.chunk(2   , dim=0		)
									else:
													SCREAMING_SNAKE_CASE__		  =				latent_model_input

									SCREAMING_SNAKE_CASE__		  =				1 / self.vae.config.scaling_factor * latents
									SCREAMING_SNAKE_CASE__		  =				self.vae.decode(_snake_case		).sample

									SCREAMING_SNAKE_CASE__		  =				(samples / 2 + 0.5).clamp(0   , 1		)

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

									if output_type == "pil":
													SCREAMING_SNAKE_CASE__		  =				self.numpy_to_pil(_snake_case		)

									if not return_dict:
													return (samples,)

									return ImagePipelineOutput(images=_snake_case		)





"""simple docstring"""






def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   List[str]:
				if not head:
								return True
				# split the list to two parts
				SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				head.next, head
				while fast and fast.next:
								SCREAMING_SNAKE_CASE__		  =				fast.next.next
								SCREAMING_SNAKE_CASE__		  =				slow.next
				SCREAMING_SNAKE_CASE__		  =				slow.next
				SCREAMING_SNAKE_CASE__		  =				None  # Don't forget here! But forget still works!
				# reverse the second part
				SCREAMING_SNAKE_CASE__		  =				None
				while second:
								SCREAMING_SNAKE_CASE__		  =				second.next
								SCREAMING_SNAKE_CASE__		  =				node
								SCREAMING_SNAKE_CASE__		  =				second
								SCREAMING_SNAKE_CASE__		  =				nxt
				# compare two parts
				# second part has the same or one less node
				while node:
								if node.val != head.val:
												return False
								SCREAMING_SNAKE_CASE__		  =				node.next
								SCREAMING_SNAKE_CASE__		  =				head.next
				return True




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				if not head or not head.next:
								return True

				# 1. Get the midpoint (slow)
				SCREAMING_SNAKE_CASE__		  =				SCREAMING_SNAKE_CASE__		  =				SCREAMING_SNAKE_CASE__		  =				head
				while fast and fast.next:
								SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				fast.next.next, slow.next

				# 2. Push the second half into the stack
				SCREAMING_SNAKE_CASE__		  =				[slow.val]
				while slow.next:
								SCREAMING_SNAKE_CASE__		  =				slow.next
								stack.append(slow.val       )

				# 3. Comparison
				while stack:
								if stack.pop() != cur.val:
												return False
								SCREAMING_SNAKE_CASE__		  =				cur.next

				return True




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   str:
				if not head or not head.next:
								return True
				SCREAMING_SNAKE_CASE__		  =				{}
				SCREAMING_SNAKE_CASE__		  =				0
				while head:
								if head.val in d:
												d[head.val].append(__UpperCAmelCase       )
								else:
												SCREAMING_SNAKE_CASE__		  =				[pos]
								SCREAMING_SNAKE_CASE__		  =				head.next
								pos += 1
				SCREAMING_SNAKE_CASE__		  =				pos - 1
				SCREAMING_SNAKE_CASE__		  =				0
				for v in d.values():
								if len(__UpperCAmelCase       ) % 2 != 0:
												middle += 1
								else:
												SCREAMING_SNAKE_CASE__		  =				0
												for i in range(0  ,					len(__UpperCAmelCase       )       ):
																if v[i] + v[len(__UpperCAmelCase       ) - 1 - step] != checksum:
																				return False
																step += 1
								if middle > 1:
												return False
				return True




