




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       UpperCAmelCase_   ( __snake_case							):




							'''simple docstring'''



							def __init__(       self							:						Tuple							,  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]							,  )      ->       Union[str, Any]:
										super().__init__(
										    a							,  split=a							,  features=a							,  cache_dir=a							,  keep_in_memory=a							,  streaming=a							,  num_proc=a							,  **a							,  )
										SCREAMING_SNAKE_CASE     =       path_or_paths if isinstance(a							,  a      ) else {self.split: path_or_paths}
										SCREAMING_SNAKE_CASE     =       Csv(
										    cache_dir=a							,  data_files=a							,  features=a							,  **a							,  )







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

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





class       UpperCAmelCase_   :




							'''simple docstring'''



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

										SCREAMING_SNAKE_CASE     =       dataset
										SCREAMING_SNAKE_CASE     =       path_or_buf
										SCREAMING_SNAKE_CASE     =       batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										SCREAMING_SNAKE_CASE     =       num_proc
										SCREAMING_SNAKE_CASE     =       """utf-8"""
										SCREAMING_SNAKE_CASE     =       to_csv_kwargs





							def  _UpperCAmelCase  (       self							:						str      )      ->       int:
										SCREAMING_SNAKE_CASE     =       self.to_csv_kwargs.pop("""path_or_buf"""							,  a      )
										SCREAMING_SNAKE_CASE     =       self.to_csv_kwargs.pop("""header"""							,  a      )
										SCREAMING_SNAKE_CASE     =       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:
																SCREAMING_SNAKE_CASE     =       self._write(file_obj=a							,  header=a							,  index=a							,  **self.to_csv_kwargs      )
										else:
													SCREAMING_SNAKE_CASE     =       self._write(file_obj=self.path_or_buf							,  header=a							,  index=a							,  **self.to_csv_kwargs      )
										return written





							def  _UpperCAmelCase  (       self							:						Tuple							,  a							:						Union[str, Any]      )      ->       Dict:
										SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       args

										SCREAMING_SNAKE_CASE     =       query_table(
										    table=self.dataset.data							,  key=slice(a							,  offset + self.batch_size      )							,  indices=self.dataset._indices							,  )
										SCREAMING_SNAKE_CASE     =       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  _UpperCAmelCase  (       self							:						Any							,  a							:						BinaryIO							,  a							:						List[Any]							,  a							:						Optional[Any]							,  **a							:						Optional[int]      )      ->       int:
										SCREAMING_SNAKE_CASE     =       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"""							,  ):
																SCREAMING_SNAKE_CASE     =       self._batch_csv((offset, header, index, to_csv_kwargs)      )
																written += file_obj.write(a      )

										else:
													SCREAMING_SNAKE_CASE  ,	SCREAMING_SNAKE_CASE     =       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









"""simple docstring"""






from ..utils import DummyObject, requires_backends
class 	_lowerCAmelCase    (      metaclass=__snake_case       ):
							__lowerCAmelCase					:    Optional[Any]     =  ['''tensorflow_text''']

							def __init__( self       :    Optional[int] ,		*a       :    int ,		**a       :    str    )   ->    str:

									"""simple docstring"""
									requires_backends(self ,		['''tensorflow_text''']    )