


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    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__ )						->					Optional[int]:
					super().__init__()

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

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

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

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size					, a__ ):
						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(a__					, a__ ) and len(a__ ) != batch_size:
						raise ValueError(
						    f'You have passed a list of generators of length {len(a__ )}, but requested an effective batch'
						    f' size of {batch_size}. Make sure the batch size matches the length of the generators.' )

					A             =					randn_tensor(a__					, generator=a__					, device=self.device					, dtype=self.unet.dtype )

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

					for t in self.progress_bar(self.scheduler.timesteps ):
						# 1. predict noise model_output
						A             =					self.unet(a__					, a__ ).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(
						    a__					, a__					, a__					, eta=a__					, use_clipped_model_output=a__					, generator=a__ ).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(a__ )

					if not return_dict:
						return (image,)

					return ImagePipelineOutput(images=a__ )







_lowercase							:   Dict						=						"0.21.0"

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
				from .utils import rich



