

'''simple docstring'''
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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , snake_case_  , snake_case_   ):
   super().__init__()

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

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







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

   # Sample gaussian noise to begin loop
   if isinstance(self.unet.config.sample_size  , snake_case_   ):
     lowercase									=(
         batch_size,
         self.unet.config.in_channels,
         self.unet.config.sample_size,
         self.unet.config.sample_size,
     )
   else:
     lowercase									=(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

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

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

   for t in self.progress_bar(self.scheduler.timesteps   ):
     # 1. predict noise model_output
     lowercase									=self.unet(snake_case_  , snake_case_   ).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
     lowercase									=self.scheduler.step(
         snake_case_  , snake_case_  , snake_case_  , eta=snake_case_  , use_clipped_model_output=snake_case_  , generator=snake_case_   ).prev_sample

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

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case_   )




_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



