

from typing import TYPE_CHECKING

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


lowercase			:				Union[str, Any]   =     {
    "configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase			:				Dict   =     [
		    "GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "GraphormerForGraphClassification",
		    "GraphormerModel",
		    "GraphormerPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_graphormer import (
				    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GraphormerForGraphClassification,
				    GraphormerModel,
				    GraphormerPreTrainedModel,
				)


else:
		import sys

		lowercase			:				str   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)













import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

             # scale the initial noise by the standard deviation required by the scheduler
             _lowerCamelCase				:  Tuple						=		latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

