

import os
from typing import Optional

import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE



class SCREAMING_SNAKE_CASE      (    UpperCamelCase__					):
  """simple docstring"""






  lowerCamelCase       :     List[Any]		 =""
  lowerCamelCase       :     int		 =(
      None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
  )
  lowerCamelCase       :     Union[str, Any]		 =None  # compression type in fsspec. ex: "gzip"
  lowerCamelCase       :     str		 =None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz







  def __init__(     self					:       Any			,				lowerCAmelCase					:       str = ""			,				lowerCAmelCase					:       Optional[str] = None			,				lowerCAmelCase					:       Optional[dict] = None			,				**lowerCAmelCase					:       Dict       )				->      List[str]:






     """simple docstring"""







     super().__init__(self			,				**__snake_case       )
     # always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
     __lowerCAmelCase    :     Any          =		fsspec.open(
         __snake_case			,				mode="""rb"""			,				protocol=__snake_case			,				compression=self.compression			,				client_kwargs={
             """requote_redirect_url""": False,  # see https://github.com/huggingface/datasets/pull/5459
             """trust_env""": True,  # Enable reading proxy env variables.
             **(target_options or {}).pop("""client_kwargs"""			,				{}       ),  # To avoid issues if it was already passed.
         }			,				**(target_options or {})			,				)
     __lowerCAmelCase    :     Union[str, Any]          =		os.path.basename(self.file.path.split("""::"""       )[0]       )
     __lowerCAmelCase    :     Optional[int]          =		(
         self.compressed_name[: self.compressed_name.rindex("""."""       )]
         if """.""" in self.compressed_name
         else self.compressed_name
     )
     __lowerCAmelCase    :     Optional[Any]          =		None







  @classmethod
  def 			SCREAMING_SNAKE_CASE       (     cls					:       Tuple			,				lowerCAmelCase					:       str       )				->      int:






     """simple docstring"""







     return super()._strip_protocol(__snake_case       ).lstrip("""/"""       )







  def 			SCREAMING_SNAKE_CASE       (     self					:       Any       )				->      Any:






     """simple docstring"""







     if self.dir_cache is None:
        __lowerCAmelCase    :     Optional[Any]          =		{**self.file.fs.info(self.file.path       ), """name""": self.uncompressed_name}
        __lowerCAmelCase    :     Optional[Any]          =		{f["""name"""]: f}







  def 			SCREAMING_SNAKE_CASE       (     self					:       Any			,				lowerCAmelCase					:       str       )				->      List[str]:






     """simple docstring"""







     return self.file.open().read()







  def 			SCREAMING_SNAKE_CASE       (     self					:       List[Any]			,				lowerCAmelCase					:       str			,				lowerCAmelCase					:       str = "rb"			,				lowerCAmelCase					:       str=None			,				lowerCAmelCase					:       List[Any]=True			,				lowerCAmelCase					:       Any=None			,				**lowerCAmelCase					:       Any			,				)				->      int:






     """simple docstring"""







     __lowerCAmelCase    :     Tuple          =		self._strip_protocol(__snake_case       )
     if mode != "rb":
        raise ValueError(f'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\''''       )
     return self.file.open()






class SCREAMING_SNAKE_CASE      (    UpperCamelCase__					):
  """simple docstring"""






  lowerCamelCase       :     Optional[Any]		 ="bz2"
  lowerCamelCase       :     int		 ="bz2"
  lowerCamelCase       :     List[Any]		 =".bz2"






class SCREAMING_SNAKE_CASE      (    UpperCamelCase__					):
  """simple docstring"""






  lowerCamelCase       :     List[Any]		 ="gzip"
  lowerCamelCase       :     Any		 ="gzip"
  lowerCamelCase       :     int		 =".gz"






class SCREAMING_SNAKE_CASE      (    UpperCamelCase__					):
  """simple docstring"""






  lowerCamelCase       :     Union[str, Any]		 ="lz4"
  lowerCamelCase       :     List[str]		 ="lz4"
  lowerCamelCase       :     int		 =".lz4"






class SCREAMING_SNAKE_CASE      (    UpperCamelCase__					):
  """simple docstring"""






  lowerCamelCase       :     int		 ="xz"
  lowerCamelCase       :     str		 ="xz"
  lowerCamelCase       :     Union[str, Any]		 =".xz"






class SCREAMING_SNAKE_CASE      (    UpperCamelCase__					):
  """simple docstring"""






  lowerCamelCase       :     List[str]		 ="zstd"
  lowerCamelCase       :     Any		 ="zstd"
  lowerCamelCase       :     Dict		 =".zst"







  def __init__(     self					:       Optional[Any]			,				lowerCAmelCase					:       str			,				lowerCAmelCase					:       str = "rb"			,				lowerCAmelCase					:       Optional[str] = None			,				lowerCAmelCase					:       Optional[dict] = None			,				lowerCAmelCase					:       int = DEFAULT_BLOCK_SIZE			,				**lowerCAmelCase					:       List[Any]			,				)				->      Dict:






     """simple docstring"""







     super().__init__(
         fo=__snake_case			,				mode=__snake_case			,				target_protocol=__snake_case			,				target_options=__snake_case			,				block_size=__snake_case			,				**__snake_case			,				)
     # We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
     #
     # File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
     #     out.close = close
     # AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
     #
     # see https://github.com/intake/filesystem_spec/issues/725
     __lowerCAmelCase    :     Dict          =		self.file.__enter__



     class SCREAMING_SNAKE_CASE      :
       """simple docstring"""






       def __init__(     self					:       List[Any]			,				lowerCAmelCase					:       List[Any]       )				->      List[str]:






          """simple docstring"""







          __lowerCAmelCase    :     int          =		file_







       def __enter__(     self					:       Dict       )				->      List[str]:






          """simple docstring"""







          self._file.__enter__()
          return self







       def __exit__(     self					:       Dict			,				*lowerCAmelCase					:       str			,				**lowerCAmelCase					:       Any       )				->      List[str]:






          """simple docstring"""







          self._file.__exit__(*__snake_case			,				**__snake_case       )







       def __iter__(     self					:       Tuple       )				->      Optional[int]:






          """simple docstring"""







          return iter(self._file       )







       def 			SCREAMING_SNAKE_CASE       (     self					:       Union[str, Any]       )				->      Optional[int]:






          """simple docstring"""







          return next(self._file       )







       def __getattr__(     self					:       Any			,				lowerCAmelCase					:       Optional[Any]       )				->      str:






          """simple docstring"""







          return getattr(self._file			,				__snake_case       )

     def fixed_enter(*lowerCAmelCase					:       Union[str, Any]			,				**lowerCAmelCase					:       int       ):
        return WrappedFile(_enter(*__snake_case			,				**__snake_case       )       )

     __lowerCAmelCase    :     List[str]          =		fixed_enter








# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=torch.linspace(1     ,			self.config.sampling_eps     ,			__snake_case     ,			device=__snake_case			)

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=None			):
										'''simple docstring'''

										if self.timesteps is None:
														raise ValueError(
														    """`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"""			)

										# TODO(Patrick) better comments + non-PyTorch
										# postprocess model score
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

										__magic_name__						:						int										=self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
										__magic_name__						:						Dict										=beta_t.flatten()
										while len(beta_t.shape			) < len(x.shape			):
														__magic_name__						:						Any										=beta_t.unsqueeze(-1			)
										__magic_name__						:						Dict										=-0.5 * beta_t * x

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

										# add noise
										__magic_name__						:						Optional[int]										=randn_tensor(x.shape     ,			layout=x.layout     ,			generator=__snake_case     ,			device=x.device     ,			dtype=x.dtype			)
										__magic_name__						:						Optional[Any]										=x_mean + diffusion * math.sqrt(-dt			) * noise

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




