
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        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		""""""
  a__           =		"""hf-legacy"""  # "hf://"" is reserved for hffs
  def __init__( self			: Optional[Any]			,							UpperCamelCase__			: Optional[DatasetInfo] = None			,							UpperCamelCase__			: Optional[str] = None			,							**UpperCamelCase__			: Optional[int]			,							)			->	str:







    """simple docstring"""






    super().__init__(self			,							**UpperCamelCase__    )
    __magic_name__				     =       repo_info
    __magic_name__				     =       token
    __magic_name__				     =       None
  def 							_lowercase							( self			: Tuple    )			->	Union[str, Any]:







    """simple docstring"""






    if self.dir_cache is None:
      __magic_name__				     =       {}
      for hf_file in self.repo_info.siblings:
        # TODO(QL): add sizes
        __magic_name__				     =       {
            """name""": hf_file.rfilename,
            """size""": None,
            """type""": """file""",
        }
        self.dir_cache.update(
            {
                str(UpperCamelCase__    ): {"""name""": str(UpperCamelCase__    ), """size""": None, """type""": """directory"""}
                for d in list(PurePosixPath(hf_file.rfilename    ).parents    )[:-1]
            }    )
  def 							_lowercase							( self			: Tuple			,							UpperCamelCase__			: str			,							UpperCamelCase__			: str = "rb"			,							**UpperCamelCase__			: Dict			,							)			->	str:







    """simple docstring"""






    if not isinstance(self.repo_info			,							UpperCamelCase__    ):
      raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''    )
    __magic_name__				     =       hf_hub_url(self.repo_info.id			,							UpperCamelCase__			,							revision=self.repo_info.sha    )
    return fsspec.open(
        UpperCamelCase__			,							mode=UpperCamelCase__			,							headers=get_authentication_headers_for_url(UpperCamelCase__			,							use_auth_token=self.token    )			,							client_kwargs={"""trust_env""": True}			,							).open()
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: Tuple			,							**UpperCamelCase__			: Any    )			->	Any:







    """simple docstring"""






    self._get_dirs()
    __magic_name__				     =       self._strip_protocol(UpperCamelCase__    )
    if path in self.dir_cache:
      return self.dir_cache[path]
    else:
      raise FileNotFoundError(UpperCamelCase__    )





  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Dict			,							UpperCamelCase__			: Union[str, Any]=False			,							**UpperCamelCase__			: List[Any]    )			->	List[Any]:







    """simple docstring"""






    self._get_dirs()
    __magic_name__				     =       PurePosixPath(path.strip("""/"""    )    )
    __magic_name__				     =       {}
    for p, f in self.dir_cache.items():
      __magic_name__				     =       PurePosixPath(p.strip("""/"""    )    )
      __magic_name__				     =       p.parent
      if root == path:
        __magic_name__				     =       f
    __magic_name__				     =       list(paths.values()    )
    if detail:
      return out
    else:
      return sorted(f["""name"""] for f in out    )








'''simple docstring'''
import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
def 				lowercase__   (   __lowercase						:  Features     ) ->      Optional[int]:



       """simple docstring"""






       __UpperCamelCase       =  np.inf

       def set_batch_size(__lowercase						:  FeatureType     ) -> None:
              nonlocal batch_size
              if isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ):
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
              elif isinstance(__lowercase				,  __lowercase     ) and feature.dtype == "binary":
                     __UpperCamelCase       =  min(__lowercase				,  config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

       _visit(__lowercase				,  __lowercase     )

       return None if batch_size is np.inf else batch_size

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							NestedDataStructureLike[PathLike]							,	__A       :							Optional[NamedSplit] = None							,	__A       :							Optional[Features] = None							,	__A       :							str = None							,	__A       :							bool = False							,	__A       :							bool = False							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        super().__init__(
            __A							,	split=__A							,	features=__A							,	cache_dir=__A							,	keep_in_memory=__A							,	streaming=__A							,	num_proc=__A							,	**__A							,	)
        __UpperCamelCase       =  path_or_paths if isinstance(__A							,	__A	) else {self.split: path_or_paths}
        __UpperCamelCase       =  _PACKAGED_DATASETS_MODULES['parquet'][1]
        __UpperCamelCase       =  Parquet(
            cache_dir=__A							,	data_files=__A							,	features=__A							,	hash=__A							,	**__A							,	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        # Build iterable dataset
        if self.streaming:
               __UpperCamelCase       =  self.builder.as_streaming_dataset(split=self.split	)
        # Build regular (map-style) dataset
        else:
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None
               __UpperCamelCase       =  None

               self.builder.download_and_prepare(
                   download_config=__A							,	download_mode=__A							,	verification_mode=__A							,	base_path=__A							,	num_proc=self.num_proc							,	)
               __UpperCamelCase       =  self.builder.as_dataset(
                   split=self.split							,	verification_mode=__A							,	in_memory=self.keep_in_memory	)
        return dataset






class      snake_case     :




 """simple docstring"""



 def __init__(       self       :							List[str]							,	__A       :							Dataset							,	__A       :							Union[PathLike, BinaryIO]							,	__A       :							Optional[int] = None							,	**__A       :							Dict							,	):
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  path_or_buf
        __UpperCamelCase       =  batch_size or get_writer_batch_size(dataset.features	)
        __UpperCamelCase       =  parquet_writer_kwargs






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

        if isinstance(self.path_or_buf							,	(str, bytes, os.PathLike)	):
               with open(self.path_or_buf							,	'wb+'	) as buffer:
                      __UpperCamelCase       =  self._write(file_obj=__A							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        else:
               __UpperCamelCase       =  self._write(file_obj=self.path_or_buf							,	batch_size=__A							,	**self.parquet_writer_kwargs	)
        return written






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							BinaryIO							,	__A       :							int							,	**__A       :							List[str]	):
        __UpperCamelCase       =  0
        __UpperCamelCase       =  parquet_writer_kwargs.pop('path_or_buf'							,	__A	)
        __UpperCamelCase       =  self.dataset.features.arrow_schema

        __UpperCamelCase       =  pq.ParquetWriter(__A							,	schema=__A							,	**__A	)

        for offset in logging.tqdm(
            range(0							,	len(self.dataset	)							,	__A	)							,	unit='ba'							,	disable=not logging.is_progress_bar_enabled()							,	desc='Creating parquet from Arrow format'							,	):
               __UpperCamelCase       =  query_table(
                   table=self.dataset._data							,	key=slice(__A							,	offset + batch_size	)							,	indices=self.dataset._indices if self.dataset._indices is not None else None							,	)
               writer.write_table(__A	)
               written += batch.nbytes
        writer.close()
        return written
