





import os
from typing import Optional

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




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       ""
							lowerCamelCase_							     =       (
							    None  # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
							)
							lowerCamelCase_							     =       None  # compression type in fsspec. ex: "gzip"
							lowerCamelCase_							     =       None  # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz


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



									"""simple docstring"""



									super().__init__(self				,					**__A		)
									# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
									SCREAMING_SNAKE_CASE__								=   fsspec.open(
									    __A				,					mode="""rb"""				,					protocol=__A				,					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 {})				,					)
									SCREAMING_SNAKE_CASE__								=   os.path.basename(self.file.path.split("""::"""		)[0]		)
									SCREAMING_SNAKE_CASE__								=   (
									    self.compressed_name[: self.compressed_name.rindex("""."""		)]
									    if """.""" in self.compressed_name
									    else self.compressed_name
									)
									SCREAMING_SNAKE_CASE__								=   None


							@classmethod
							def   _snake_case	(				cls     :Any				,					__A     :Tuple		) -> List[str]:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



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


							def   _snake_case	(				self     :Optional[int]				,					__A     :str		) -> str:



									"""simple docstring"""



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


							def   _snake_case	(				self     :List[str]				,					__A     :str				,					__A     :str = "rb"				,					__A     :int=None				,					__A     :List[str]=True				,					__A     :Optional[Any]=None				,					**__A     :Union[str, Any]				,					) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self._strip_protocol(__A		)
									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 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "bz2"
							lowerCamelCase_							     =       "bz2"
							lowerCamelCase_							     =       ".bz2"




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "gzip"
							lowerCamelCase_							     =       "gzip"
							lowerCamelCase_							     =       ".gz"




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "lz4"
							lowerCamelCase_							     =       "lz4"
							lowerCamelCase_							     =       ".lz4"




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "xz"
							lowerCamelCase_							     =       "xz"
							lowerCamelCase_							     =       ".xz"




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       "zstd"
							lowerCamelCase_							     =       "zstd"
							lowerCamelCase_							     =       ".zst"


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



									"""simple docstring"""



									super().__init__(
									    fo=__A				,					mode=__A				,					target_protocol=__A				,					target_options=__A				,					block_size=__A				,					**__A				,					)
									# 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
									SCREAMING_SNAKE_CASE__								=   self.file.__enter__




									class 	UpperCamelCase_							:


																def __init__(				self     :int				,					__A     :Tuple		) -> int:



																		"""simple docstring"""



																		SCREAMING_SNAKE_CASE__								=   file_


																def __enter__(				self     :Optional[Any]		) -> Optional[int]:



																		"""simple docstring"""



																		self._file.__enter__()
																		return self


																def __exit__(				self     :Optional[Any]				,					*__A     :List[Any]				,					**__A     :int		) -> Tuple:



																		"""simple docstring"""



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


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



																		"""simple docstring"""



																		return iter(self._file		)


																def   _snake_case	(				self     :Dict		) -> Dict:



																		"""simple docstring"""



																		return next(self._file		)


																def __getattr__(				self     :Union[str, Any]				,					__A     :List[str]		) -> Optional[Any]:



																		"""simple docstring"""



																		return getattr(self._file				,					__A		)

									def fixed_enter(*__A     :List[Any]				,					**__A     :Optional[int]		):
											return WrappedFile(_enter(*__A				,					**__A		)		)

									SCREAMING_SNAKE_CASE__								=   fixed_enter



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

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




@dataclass
class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		torch.FloatTensor
	_lowerCAmelCase     :		Optional[torch.FloatTensor]										=						None






class 							lowerCamelCase   (  _UpperCamelCase							,						_UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						2


	@register_to_config
	def __init__(    self				,     lowercase__ = 0.0_2				,     lowercase__ = 1_0_0				,     lowercase__ = 1.0_0_7				,     lowercase__ = 8_0				,     lowercase__ = 0.0_5				,     lowercase__ = 5_0				,     ):
							# standard deviation of the initial noise distribution
							__UpperCAmelCase			:							Union[str, Any]	    =	sigma_max

							# setable values
							__UpperCAmelCase			:							int	    =	None
							__UpperCAmelCase			:							np.IntTensor	    =	None
							__UpperCAmelCase			:							torch.FloatTensor	    =	None  # sigma(t_i)


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							return sample


	def  A(    self				,     lowercase__				,     lowercase__ = None):
							__UpperCAmelCase			:							int	    =	num_inference_steps
							__UpperCAmelCase			:							List[str]	    =	np.arange(0				,     self.num_inference_steps)[::-1].copy()
							__UpperCAmelCase			:							Any	    =	torch.from_numpy(lowercase__).to(lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	[
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in self.timesteps
							]
							__UpperCAmelCase			:							Tuple	    =	torch.tensor(lowercase__				,     dtype=torch.floataa				,     device=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None):
							if self.config.s_min <= sigma <= self.config.s_max:
													__UpperCAmelCase			:							int	    =	min(self.config.s_churn / self.num_inference_steps				,     2**0.5 - 1)
							else:
													__UpperCAmelCase			:							int	    =	0

							# sample eps ~ N(0, S_noise^2 * I)
							__UpperCAmelCase			:							List[str]	    =	self.config.s_noise * randn_tensor(sample.shape				,     generator=lowercase__).to(sample.device)
							__UpperCAmelCase			:							Optional[int]	    =	sigma + gamma * sigma
							__UpperCAmelCase			:							List[Any]	    =	sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							str	    =	sample_hat + sigma_hat * model_output
							__UpperCAmelCase			:							Tuple	    =	(sample_hat - pred_original_sample) / sigma_hat
							__UpperCAmelCase			:							str	    =	sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = True				,     ):
							__UpperCAmelCase			:							Any	    =	sample_prev + sigma_prev * model_output
							__UpperCAmelCase			:							List[str]	    =	(sample_prev - pred_original_sample) / sigma_prev
							__UpperCAmelCase			:							Union[str, Any]	    =	sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative)

							return KarrasVeOutput(
							    prev_sample=lowercase__				,     derivative=lowercase__				,     pred_original_sample=lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__):
							raise NotImplementedError()

