






'''simple docstring'''

import importlib
import shutil
import threading
import warnings
from typing import List

import fsspec
import fsspec.asyn

from . import compression
from .hffilesystem import HfFileSystem


UpperCamelCase      					=importlib.util.find_spec("s3fs") is not None

if _has_safs:
      from .safilesystem import SaFileSystem  # noqa: F401

UpperCamelCase      					=[
    compression.BzaFileSystem,
    compression.GzipFileSystem,
    compression.LzaFileSystem,
    compression.XzFileSystem,
    compression.ZstdFileSystem,
]

# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
      if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
            warnings.warn(f"A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.")
      fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)



def 				snake_case       (  a_				:			str						)							->					str:





      """simple docstring"""

      if "://" in dataset_path:
            UpperCamelCase_					: Tuple            =				dataset_path.split("""://"""						)[1]
      return dataset_path



def 				snake_case       (  a_				:			fsspec.AbstractFileSystem						)							->					bool:





      """simple docstring"""

      if fs is not None and fs.protocol != "file":
            return True
      else:
            return False



def 				snake_case       (  a_				:			fsspec.AbstractFileSystem     , a_				:			str     , a_				:			str						)							->					Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_					: str            =				not is_remote_filesystem(a_						)
      if is_local:
            # LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
            shutil.move(fs._strip_protocol(a_						)     , fs._strip_protocol(a_						)						)
      else:
            fs.mv(a_     , a_     , recursive=a_						)



def 				snake_case       (  )							->					None:





      """simple docstring"""

      if hasattr(fsspec.asyn     , """reset_lock"""						):
            # for future fsspec>2022.05.0
            fsspec.asyn.reset_lock()
      else:
            UpperCamelCase_					: List[Any]            =				None
            UpperCamelCase_					: str            =				None
            UpperCamelCase_					: List[Any]            =				threading.Lock()














'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

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




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






