






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   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    UNetaDModel
		_snake_case    :    ScoreSdeVeScheduler



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase   )	->							int:
							super().__init__()
							self.register_modules(unet=_UpperCamelCase				,		scheduler=_UpperCamelCase   )



		@torch.no_grad()
		def __call__(							self				,		_UpperCamelCase = 1				,		_UpperCamelCase = 2_0_0_0				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		**_UpperCamelCase				,		)	->							Union[ImagePipelineOutput, Tuple]:
							UpperCAmelCase_    :      List[Any]            =						self.unet.config.sample_size
							UpperCAmelCase_    :      Optional[Any]            =						(batch_size, 3, img_size, img_size)

							UpperCAmelCase_    :      Dict            =						self.unet

							UpperCAmelCase_    :      Optional[Any]            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase   ) * self.scheduler.init_noise_sigma
							UpperCAmelCase_    :      List[Any]            =						sample.to(self.device   )

							self.scheduler.set_timesteps(_UpperCamelCase   )
							self.scheduler.set_sigmas(_UpperCamelCase   )

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

												# correction step
												for _ in range(self.scheduler.config.correct_steps   ):
																	UpperCAmelCase_    :      str            =						self.unet(_UpperCamelCase				,		_UpperCamelCase   ).sample
																	UpperCAmelCase_    :      Tuple            =						self.scheduler.step_correct(_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase   ).prev_sample

												# prediction step
												UpperCAmelCase_    :      str            =						model(_UpperCamelCase				,		_UpperCamelCase   ).sample
												UpperCAmelCase_    :      List[Any]            =						self.scheduler.step_pred(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase   )

												UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						output.prev_sample, output.prev_sample_mean

							UpperCAmelCase_    :      Any            =						sample_mean.clamp(0				,		1   )
							UpperCAmelCase_    :      Optional[Any]            =						sample.cpu().permute(0				,		2				,		3				,		1   ).numpy()
							if output_type == "pil":
												UpperCAmelCase_    :      Tuple            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (sample,)

							return ImagePipelineOutput(images=_UpperCamelCase   )










from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ''
 SCREAMING_SNAKE_CASE__      =       'hf-legacy'  # "hf://"" is reserved for hffs




 def __init__(		self					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(self					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       repo_info
       a						:int 							=       token
       a						:int 							=       None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.dir_cache is None:
             a						:Dict 							=       {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   a						:List[Any] 							=       {
                       '''name''': hf_file.rfilename,
                       '''size''': None,
                       '''type''': '''file''',
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCamelCase    ): {'''name''': str(_lowerCamelCase    ), '''size''': None, '''type''': '''directory'''}
                           for d in list(PurePosixPath(hf_file.rfilename    ).parents    )[:-1]
                       }    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = "rb"					,  **_lowerCamelCase					,  ):
       if not isinstance(self.repo_info					,  _lowerCamelCase    ):
             raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''    )
       a						:Optional[int] 							=       hf_hub_url(self.repo_info.id					,  _lowerCamelCase					,  revision=self.repo_info.sha    )
       return fsspec.open(
           _lowerCamelCase					,  mode=_lowerCamelCase					,  headers=get_authentication_headers_for_url(_lowerCamelCase					,  use_auth_token=self.token    )					,  client_kwargs={'''trust_env''': True}					,  ).open()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:Union[str, Any] 							=       self._strip_protocol(_lowerCamelCase    )
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=False					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:str 							=       PurePosixPath(path.strip('''/'''    )    )
       a						:Tuple 							=       {}
       for p, f in self.dir_cache.items():
             a						:Optional[int] 							=       PurePosixPath(p.strip('''/'''    )    )
             a						:str 							=       p.parent
             if root == path:
                   a						:List[str] 							=       f
       a						:Any 							=       list(paths.values()    )
       if detail:
             return out
       else:
             return sorted(f['''name'''] for f in out    )





