




'''simple docstring'''





import multiprocessing
import os
from typing import BinaryIO, Optional, Union

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.csv.csv import Csv
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader






class 			A			(		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(    self  :   Dict	,  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__  :   List[str]	,  )				->       Any:
           super().__init__(
               A__	,  split=A__	,  features=A__	,  cache_dir=A__	,  keep_in_memory=A__	,  streaming=A__	,  num_proc=A__	,  **A__	,  )
           lowerCamelCase__        =				path_or_paths if isinstance(A__	,  A__			) else {self.split: path_or_paths}
           lowerCamelCase__        =				Csv(
               cache_dir=A__	,  data_files=A__	,  features=A__	,  **A__	,  )




     def  __snake_case   (    self  :   List[Any]			)				->       int:
           # Build iterable dataset
           if self.streaming:
                 lowerCamelCase__        =				self.builder.as_streaming_dataset(split=self.split			)
           # Build regular (map-style) dataset
           else:
                 lowerCamelCase__        =				None
                 lowerCamelCase__        =				None
                 lowerCamelCase__        =				None
                 lowerCamelCase__        =				None

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






class 			A			:
     """simple docstring"""

     def __init__(    self  :   List[Any]	,  A__  :   Dataset	,  A__  :   Union[PathLike, BinaryIO]	,  A__  :   Optional[int] = None	,  A__  :   Optional[int] = None	,  **A__  :   Optional[Any]	,  )				->       str:
           if num_proc is not None and num_proc <= 0:
                 raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""			)

           lowerCamelCase__        =				dataset
           lowerCamelCase__        =				path_or_buf
           lowerCamelCase__        =				batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           lowerCamelCase__        =				num_proc
           lowerCamelCase__        =				'utf-8'
           lowerCamelCase__        =				to_csv_kwargs




     def  __snake_case   (    self  :   Optional[int]			)				->       int:
           lowerCamelCase__        =				self.to_csv_kwargs.pop('path_or_buf'	,  A__			)
           lowerCamelCase__        =				self.to_csv_kwargs.pop('header'	,  A__			)
           lowerCamelCase__        =				self.to_csv_kwargs.pop('index'	,  A__			)

           if isinstance(self.path_or_buf	,  (str, bytes, os.PathLike)			):
                 with open(self.path_or_buf	,  'wb+'			) as buffer:
                       lowerCamelCase__        =				self._write(file_obj=A__	,  header=A__	,  index=A__	,  **self.to_csv_kwargs			)
           else:
                 lowerCamelCase__        =				self._write(file_obj=self.path_or_buf	,  header=A__	,  index=A__	,  **self.to_csv_kwargs			)
           return written




     def  __snake_case   (    self  :   List[str]	,  A__  :   List[str]			)				->       List[Any]:
           lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__        =				args

           lowerCamelCase__        =				query_table(
               table=self.dataset.data	,  key=slice(A__	,  offset + self.batch_size			)	,  indices=self.dataset._indices	,  )
           lowerCamelCase__        =				batch.to_pandas().to_csv(
               path_or_buf=A__	,  header=header if (offset == 0) else False	,  index=A__	,  **A__			)
           return csv_str.encode(self.encoding			)




     def  __snake_case   (    self  :   Any	,  A__  :   BinaryIO	,  A__  :   str	,  A__  :   str	,  **A__  :   Optional[int]			)				->       int:
           lowerCamelCase__        =				0

           if self.num_proc is None or self.num_proc == 1:
                 for offset in logging.tqdm(
                     range(0	,  len(self.dataset			)	,  self.batch_size			)	,  unit='ba'	,  disable=not logging.is_progress_bar_enabled()	,  desc='Creating CSV from Arrow format'	,  ):
                       lowerCamelCase__        =				self._batch_csv((offset, header, index, to_csv_kwargs)			)
                       written += file_obj.write(A__			)

           else:
                 lowerCamelCase__  ,		lowerCamelCase__        =				len(self.dataset			), self.batch_size
                 with multiprocessing.Pool(self.num_proc			) as pool:
                       for csv_str in logging.tqdm(
                           pool.imap(
                               self._batch_csv	,  [(offset, header, index, to_csv_kwargs) for offset in range(0	,  A__	,  A__			)]	,  )	,  total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size	,  unit='ba'	,  disable=not logging.is_progress_bar_enabled()	,  desc='Creating CSV from Arrow format'	,  ):
                             written += file_obj.write(A__			)

           return written





from ..utils import DummyObject, requires_backends






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Optional[Any]     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   Dict	,  *A__  :   Optional[int]	,  **A__  :   Dict			)				->       Union[str, Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[int]	,  *A__  :   Dict	,  **A__  :   List[str]			)				->       Union[str, Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   Optional[Any]	,  *A__  :   Dict	,  **A__  :   List[Any]			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)






class 			A			(		metaclass=_lowerCAmelCase		):
     """simple docstring"""

     _snake_case:	Dict     =["torch", "transformers", "invisible_watermark"]




     def __init__(    self  :   List[str]	,  *A__  :   str	,  **A__  :   int			)				->       List[Any]:
           requires_backends(self	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   str	,  *A__  :   Any	,  **A__  :   Dict			)				->       Optional[Any]:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)




     @classmethod
     def  __snake_case   (    cls  :   List[str]	,  *A__  :   Optional[Any]	,  **A__  :   int			)				->       Dict:
           requires_backends(cls	,  ['torch', 'transformers', 'invisible_watermark']			)