


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_ (       _a):




						def __init__(	self						:   Tuple   ,     __A						:   Tuple   ,     __A						:   Union[str, Any]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any] = None   ,     )      ->Optional[Any]:






									"""simple docstring"""






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

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




						def  _snake_case					(	self						:   Dict   ,     __A						:   str						)      ->Optional[int]:






									"""simple docstring"""






									if not isinstance(UpperCamelCase_   ,     UpperCamelCase_						):
												a__ :Tuple						  =    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						:   Optional[int]   ,     __A						:   Union[str, Any]   ,     __A						:   List[str] = 4.0   ,     __A						:   List[Any] = None   ,     __A						:   Union[str, Any] = 50   ,     __A						:   Any = "pil"   ,     __A						:   int = True   ,     )      ->List[Any]:






									"""simple docstring"""






									a__ :Any						  =    len(UpperCamelCase_						)
									a__ :Any						  =    self.transformer.config.sample_size
									a__ :Optional[int]						  =    self.transformer.config.in_channels

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

									a__ :Union[str, Any]						  =    torch.tensor(UpperCamelCase_   ,     device=self.device						).reshape(-1						)
									a__ :Union[str, Any]						  =    torch.tensor([1000] * batch_size   ,     device=self.device						)
									a__ :Union[str, Any]						  =    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:
															a__ :Optional[Any]						  =    latent_model_input[: len(UpperCamelCase_						) // 2]
															a__ :str						  =    torch.cat([half, half]   ,     dim=0						)
												a__ :str						  =    self.scheduler.scale_model_input(UpperCamelCase_   ,     UpperCamelCase_						)

												a__ :Optional[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+)
															a__ :List[Any]						  =    latent_model_input.device.type == "mps"
															if isinstance(UpperCamelCase_   ,     UpperCamelCase_						):
																		a__ :Dict						  =    torch.floataa if is_mps else torch.floataa
															else:
																		a__ :List[Any]						  =    torch.intaa if is_mps else torch.intaa
															a__ :Optional[Any]						  =    torch.tensor([timesteps]   ,     dtype=UpperCamelCase_   ,     device=latent_model_input.device						)
												elif len(timesteps.shape						) == 0:
															a__ :Optional[int]						  =    timesteps[None].to(latent_model_input.device						)
												# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
												a__ :Any						  =    timesteps.expand(latent_model_input.shape[0]						)
												# predict noise model_output
												a__ :Tuple						  =    self.transformer(
												    UpperCamelCase_   ,     timestep=UpperCamelCase_   ,     class_labels=UpperCamelCase_						).sample

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

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

															a__ :Dict						  =    torch.cat([eps, rest]   ,     dim=1						)

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

												# compute previous image: x_t -> x_t-1
												a__ :str						  =    self.scheduler.step(UpperCamelCase_   ,     UpperCamelCase_   ,     UpperCamelCase_						).prev_sample

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

									a__ :Any						  =    1 / self.vae.config.scaling_factor * latents
									a__ :List[Any]						  =    self.vae.decode(UpperCamelCase_						).sample

									a__ :List[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
									a__ :Optional[Any]						  =    samples.cpu().permute(0   ,     2   ,     3   ,     1						).float().numpy()

									if output_type == "pil":
												a__ :Union[str, Any]						  =    self.numpy_to_pil(UpperCamelCase_						)

									if not return_dict:
												return (samples,)

									return ImagePipelineOutput(images=UpperCamelCase_						)






import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


snake_case__			 =						'''scheduler_config.json'''


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     1
						lowerCamelCase_         =     2
						lowerCamelCase_         =     3
						lowerCamelCase_         =     4
						lowerCamelCase_         =     5
						lowerCamelCase_         =     6
						lowerCamelCase_         =     7
						lowerCamelCase_         =     8
						lowerCamelCase_         =     9
						lowerCamelCase_         =     10
						lowerCamelCase_         =     11
						lowerCamelCase_         =     12
						lowerCamelCase_         =     13
						lowerCamelCase_         =     14






@dataclass
class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     42






class 					lowerCAmelCase_ :
						lowerCamelCase_         =     SCHEDULER_CONFIG_NAME
						lowerCamelCase_         =     []
						lowerCamelCase_         =     True




						@classmethod
						def  _snake_case					(	cls						:   Union[str, Any]   ,     __A						:   Dict[str, Any] = None   ,     __A						:   Optional[str] = None   ,     __A						:   Optional[int]=False   ,     **__A						:   Union[str, Any]   ,     )      ->List[Any]:






									"""simple docstring"""






									a__  ,  a__  ,  a__ :List[Any]						  =    cls.load_config(
									    pretrained_model_name_or_path=__A   ,     subfolder=__A   ,     return_unused_kwargs=__A   ,     return_commit_hash=__A   ,     **__A   ,     )
									return cls.from_config(__A   ,     return_unused_kwargs=__A   ,     **__A						)




						def  _snake_case					(	self						:   str   ,     __A						:   Union[str, os.PathLike]   ,     __A						:   bool = False   ,     **__A						:   Optional[Any]						)      ->str:






									"""simple docstring"""






									self.save_config(save_directory=__A   ,     push_to_hub=__A   ,     **__A						)




						@property
						def  _snake_case					(	self						:   List[Any]						)      ->Dict:






									"""simple docstring"""






									return self._get_compatibles()






						@classmethod
						def  _snake_case					(	cls						:   Dict						)      ->int:






									"""simple docstring"""






									a__ :Optional[int]						  =    list(set([cls.__name__] + cls._compatibles						)						)
									a__ :Union[str, Any]						  =    importlib.import_module(__name__.split("."						)[0]						)
									a__ :Optional[int]						  =    [
									    getattr(__A   ,     __A						) for c in compatible_classes_str if hasattr(__A   ,     __A						)
									]
									return compatible_classes


