


'''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 						a__					(					a_   ):





			'''simple docstring'''

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

						# 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(lowerCAmelCase_	)
									__A=	dict(sorted(self.labels.items()	)	)
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Union[str, List[str]]	)      ->		List[int]:

						if not isinstance(lowerCAmelCase_			,    lowerCAmelCase_	):
									__A=	list(lowerCAmelCase_	)

						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							:			List[Any]			,    lowerCAmelCase_							:			List[int]			,    lowerCAmelCase_							:			float = 4.0			,    lowerCAmelCase_							:			Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    lowerCAmelCase_							:			int = 50			,    lowerCAmelCase_							:			Optional[str] = "pil"			,    lowerCAmelCase_							:			bool = True			,    )      ->		Union[ImagePipelineOutput, Tuple]:
						__A=	len(lowerCAmelCase_	)
						__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=lowerCAmelCase_			,    device=self.device			,    dtype=self.transformer.dtype			,    )
						__A=	torch.cat([latents] * 2	) if guidance_scale > 1 else latents

						__A=	torch.tensor(lowerCAmelCase_			,    device=self.device	).reshape(-1	)
						__A=	torch.tensor([1_000] * 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(lowerCAmelCase_	)

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

									__A=	t
									if not torch.is_tensor(lowerCAmelCase_	):
												# 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(lowerCAmelCase_			,    lowerCAmelCase_	):
															__A=	torch.floataa if is_mps else torch.floataa
												else:
															__A=	torch.intaa if is_mps else torch.intaa
												__A=	torch.tensor([timesteps]			,    dtype=lowerCAmelCase_			,    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(
									    lowerCAmelCase_			,    timestep=lowerCAmelCase_			,    class_labels=lowerCAmelCase_	).sample

									# perform guidance
									if guidance_scale > 1:
												__A,	__A=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
												__A,	__A=	torch.split(lowerCAmelCase_			,    len(lowerCAmelCase_	) // 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(lowerCAmelCase_			,    lowerCAmelCase_			,    dim=1	)
									else:
												__A=	noise_pred

									# compute previous image: x_t -> x_t-1
									__A=	self.scheduler.step(lowerCAmelCase_			,    lowerCAmelCase_			,    lowerCAmelCase_	).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(lowerCAmelCase_	).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(lowerCAmelCase_	)

						if not return_dict:
									return (samples,)

						return ImagePipelineOutput(images=lowerCAmelCase_	)







'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCAmelCase__  			=				{}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__  			=				['''BartphoTokenizer''']

if TYPE_CHECKING:
					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_bartpho import BartphoTokenizer

else:
					import sys

					UpperCAmelCase__  			=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



