






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 (lowercase_     ):







		'''simple docstring'''



		def __init__(				self							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase = None							,						)      ->				List[Any]:
							super().__init__()
							self.register_modules(transformer=_UpperCamelCase							,						vae=_UpperCamelCase							,						scheduler=_UpperCamelCase  )

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



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

							if not isinstance(_UpperCamelCase							,						_UpperCamelCase  ):
												UpperCAmelCase_       : int				=		list(_UpperCamelCase  )

							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							,						_UpperCamelCase							,						_UpperCamelCase = 4.0							,						_UpperCamelCase = None							,						_UpperCamelCase = 5_0							,						_UpperCamelCase = "pil"							,						_UpperCamelCase = True							,						)      ->				List[Any]:
							UpperCAmelCase_       : Tuple				=		len(_UpperCamelCase  )
							UpperCAmelCase_       : Tuple				=		self.transformer.config.sample_size
							UpperCAmelCase_       : Optional[int]				=		self.transformer.config.in_channels

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

							UpperCAmelCase_       : Any				=		torch.tensor(_UpperCamelCase							,						device=self.device  ).reshape(-1  )
							UpperCAmelCase_       : int				=		torch.tensor([1_0_0_0] * batch_size							,						device=self.device  )
							UpperCAmelCase_       : Tuple				=		torch.cat([class_labels, class_null]							,						0  ) if guidance_scale > 1 else class_labels

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

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

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

												# perform guidance
												if guidance_scale > 1:
																	UpperCAmelCase_			,    UpperCAmelCase_       : int				=		noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																	UpperCAmelCase_			,    UpperCAmelCase_       : List[str]				=		torch.split(_UpperCamelCase							,						len(_UpperCamelCase  ) // 2							,						dim=0  )

																	UpperCAmelCase_       : Dict				=		uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																	UpperCAmelCase_       : int				=		torch.cat([half_eps, half_eps]							,						dim=0  )

																	UpperCAmelCase_       : List[str]				=		torch.cat([eps, rest]							,						dim=1  )

												# learned sigma
												if self.transformer.config.out_channels // 2 == latent_channels:
																	UpperCAmelCase_			,    UpperCAmelCase_       : Any				=		torch.split(_UpperCamelCase							,						_UpperCamelCase							,						dim=1  )
												else:
																	UpperCAmelCase_       : Tuple				=		noise_pred

												# compute previous image: x_t -> x_t-1
												UpperCAmelCase_       : Any				=		self.scheduler.step(_UpperCamelCase							,						_UpperCamelCase							,						_UpperCamelCase  ).prev_sample

							if guidance_scale > 1:
												UpperCAmelCase_			,    UpperCAmelCase_       : List[Any]				=		latent_model_input.chunk(2							,						dim=0  )
							else:
												UpperCAmelCase_       : Any				=		latent_model_input

							UpperCAmelCase_       : List[Any]				=		1 / self.vae.config.scaling_factor * latents
							UpperCAmelCase_       : Dict				=		self.vae.decode(_UpperCamelCase  ).sample

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

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

							if output_type == "pil":
												UpperCAmelCase_       : int				=		self.numpy_to_pil(_UpperCamelCase  )

							if not return_dict:
												return (samples,)

							return ImagePipelineOutput(images=_UpperCamelCase  )









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




