




'''simple docstring'''





# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput




class    __lowerCAmelCase       (							lowerCAmelCase):






       def __init__(   self:					List[str]       ,					_lowerCAmelCase:					List[Any]       ,					_lowerCAmelCase:					Optional[Any]				):
         super().__init__()
         self.register_modules(unet=_lowerCAmelCase       ,					scheduler=_lowerCAmelCase				)






       @torch.no_grad()
       def __call__(   self:					int       ,					_lowerCAmelCase:					int = 1       ,					_lowerCAmelCase:					Optional[torch.Generator] = None       ,					_lowerCAmelCase:					int = 50       ,					_lowerCAmelCase:					Optional[str] = "pil"       ,					_lowerCAmelCase:					bool = True       ,					**_lowerCAmelCase:					Union[str, Any]       ,					):
         lowercase    :List[Any]      	=						torch.randn(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,					generator=_lowerCAmelCase       ,					)
         lowercase    :Any      	=						image.to(self.device				)

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

         for t in self.progress_bar(self.scheduler.timesteps				):
           # 1. predict noise model_output
           lowercase    :Tuple      	=						self.unet(_lowerCAmelCase       ,					_lowerCAmelCase				).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    :Optional[Any]      	=						self.scheduler.step(_lowerCAmelCase       ,					_lowerCAmelCase       ,					_lowerCAmelCase				).prev_sample

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

         if not return_dict:
           return (image,), "This is a local test"

         return ImagePipelineOutput(images=_lowerCAmelCase				), "This is a local test"









_UpperCAmelCase	:						Union[str, Any]			=     "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



