
"""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					(       _lowercase   ):




			"""simple docstring"""







			def __init__(					self    :       Optional[Any]							, _snake_case    :       TransformeraDModel							, _snake_case    :       AutoencoderKL							, _snake_case    :       KarrasDiffusionSchedulers							, _snake_case    :       Optional[Dict[int, str]] = None							, )   ->			int:







								"""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   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       Union[str, List[str]]						)   ->			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    :       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]:







								"""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([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(_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						)






"""simple docstring"""





from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase_       :    Any						      =			{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    str						      =			[
		    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''VanForImageClassification''',
		    '''VanModel''',
		    '''VanPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_van import (
				    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VanForImageClassification,
				    VanModel,
				    VanPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_       :    Optional[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




