

from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase				)     ->  Dict:
							'''simple docstring'''






							super().__init__()

							# make sure scheduler can always be converted to DDIM
							A__						=	DDIMScheduler.from_config(scheduler.config				)

							self.register_modules(unet=lowercase					,					scheduler=lowercase				)


			@torch.no_grad()
			def __call__(			self					,					lowercase = 1					,					lowercase = None					,					lowercase = 0.0					,					lowercase = 50					,					lowercase = None					,					lowercase = "pil"					,					lowercase = True					,					)     ->  Union[ImagePipelineOutput, Tuple]:
							'''simple docstring'''






							if isinstance(self.unet.config.sample_size					,					lowercase				):
											A__						=	(
											    batch_size,
											    self.unet.config.in_channels,
											    self.unet.config.sample_size,
											    self.unet.config.sample_size,
											)
							else:
											A__						=	(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

							if isinstance(lowercase					,					lowercase				) and len(lowercase				) != batch_size:
											raise ValueError(
											    F'You have passed a list of generators of length {len(lowercase				)}, but requested an effective batch'
											    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'				)

							A__						=	randn_tensor(lowercase					,					generator=lowercase					,					device=self.device					,					dtype=self.unet.dtype				)

							# set step values
							self.scheduler.set_timesteps(lowercase				)

							for t in self.progress_bar(self.scheduler.timesteps				):
											# 1. predict noise model_output
											A__						=	self.unet(lowercase					,					lowercase				).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											A__						=	self.scheduler.step(
											    lowercase					,					lowercase					,					lowercase					,					eta=lowercase					,					use_clipped_model_output=lowercase					,					generator=lowercase				).prev_sample

							A__						=	(image / 2 + 0.5).clamp(0					,					1				)
							A__						=	image.cpu().permute(0					,					2					,					3					,					1				).numpy()
							if output_type == "pil":
											A__						=	self.numpy_to_pil(lowercase				)

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowercase				)


"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			   = {
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "LiltForQuestionAnswering",
           "LiltForSequenceClassification",
           "LiltForTokenClassification",
           "LiltModel",
           "LiltPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_lilt import (
                  LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  LiltForQuestionAnswering,
                  LiltForSequenceClassification,
                  LiltForTokenClassification,
                  LiltModel,
                  LiltPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
