

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 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case = None					,  )   -> Dict:




						'''simple docstring'''
						super().__init__()
						self.register_modules(transformer=__snake_case					,  vae=__snake_case					,  scheduler=__snake_case					)

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





			def 		__magic_name__    (		self					,  __snake_case					)   -> List[int]:




						'''simple docstring'''

						if not isinstance(__snake_case					,  __snake_case					):
									__a        =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					,  __snake_case					,  __snake_case = 4.0					,  __snake_case = None					,  __snake_case = 50					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						__a        =len(__snake_case					)
						__a        =self.transformer.config.sample_size
						__a        =self.transformer.config.in_channels

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

						__a        =torch.tensor(__snake_case					,  device=self.device					).reshape(-1					)
						__a        =torch.tensor([1000] * batch_size					,  device=self.device					)
						__a        =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:
												__a        =latent_model_input[: len(__snake_case					) // 2]
												__a        =torch.cat([half, half]					,  dim=0					)
									__a        =self.scheduler.scale_model_input(__snake_case					,  __snake_case					)

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

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

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

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

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

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

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

						__a        =1 / self.vae.config.scaling_factor * latents
						__a        =self.vae.decode(__snake_case					).sample

						__a        =(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        =samples.cpu().permute(0					,  2					,  3					,  1					).float().numpy()

						if output_type == "pil":
									__a        =self.numpy_to_pil(__snake_case					)

						if not return_dict:
									return (samples,)

						return ImagePipelineOutput(images=__snake_case					)









_lowerCAmelCase					:			Optional[int]  				=			"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_lowerCAmelCase					:			Tuple  				=			[{"type": "code", "content": INSTALL_CONTENT}]
_lowerCAmelCase					:			Optional[int]  				=			{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






