
"""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    (   __a					):



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None     ,						_lowerCamelCase = False     ,						_lowerCamelCase = False     ,						_lowerCamelCase = None     ,						**_lowerCamelCase     ,						) ->       Union[str, Any]:




										'''simple docstring'''
										super().__init__(
										    _lowerCamelCase     ,						split=_lowerCamelCase     ,						features=_lowerCamelCase     ,						cache_dir=_lowerCamelCase     ,						keep_in_memory=_lowerCamelCase     ,						streaming=_lowerCamelCase     ,						num_proc=_lowerCamelCase     ,						**_lowerCamelCase     ,						)
										__lowercase						      =					path_or_paths if isinstance(_lowerCamelCase     ,						_lowerCamelCase					) else {self.split: path_or_paths}
										__lowercase						      =					Csv(
										    cache_dir=_lowerCamelCase     ,						data_files=_lowerCamelCase     ,						features=_lowerCamelCase     ,						**_lowerCamelCase     ,						)





							def      SCREAMING_SNAKE_CASE	(    self					) ->       Optional[int]:




										'''simple docstring'''
										# Build iterable dataset
										if self.streaming:
													__lowercase						      =					self.builder.as_streaming_dataset(split=self.split					)
										# Build regular (map-style) dataset
										else:
													__lowercase						      =					None
													__lowercase						      =					None
													__lowercase						      =					None
													__lowercase						      =					None

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





class 				__a    :



							'''simple docstring'''







							def __init__(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase = None     ,						_lowerCamelCase = None     ,						**_lowerCamelCase     ,						) ->       Tuple:




										'''simple docstring'''
										if num_proc is not None and num_proc <= 0:
													raise ValueError(f'''num_proc {num_proc} must be an integer > 0.'''					)

										__lowercase						      =					dataset
										__lowercase						      =					path_or_buf
										__lowercase						      =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										__lowercase						      =					num_proc
										__lowercase						      =					"utf-8"
										__lowercase						      =					to_csv_kwargs
							def      SCREAMING_SNAKE_CASE	(    self					) ->       int:




										'''simple docstring'''
										__lowercase						      =					self.to_csv_kwargs.pop("path_or_buf"     ,						_lowerCamelCase					)
										__lowercase						      =					self.to_csv_kwargs.pop("header"     ,						_lowerCamelCase					)
										__lowercase						      =					self.to_csv_kwargs.pop("index"     ,						_lowerCamelCase					)

										if isinstance(self.path_or_buf     ,						(str, bytes, os.PathLike)					):
													with open(self.path_or_buf     ,						"wb+"					) as buffer:
																__lowercase						      =					self._write(file_obj=_lowerCamelCase     ,						header=_lowerCamelCase     ,						index=_lowerCamelCase     ,						**self.to_csv_kwargs					)
										else:
													__lowercase						      =					self._write(file_obj=self.path_or_buf     ,						header=_lowerCamelCase     ,						index=_lowerCamelCase     ,						**self.to_csv_kwargs					)
										return written
							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase					) ->       List[Any]:




										'''simple docstring'''
										__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase						      =					args

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





							def      SCREAMING_SNAKE_CASE	(    self     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						_lowerCamelCase     ,						**_lowerCamelCase					) ->       int:




										'''simple docstring'''
										__lowercase						      =					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"     ,						):
																__lowercase						      =					self._batch_csv((offset, header, index, to_csv_kwargs)					)
																written += file_obj.write(_lowerCamelCase					)

										else:
													__lowercase      ,				__lowercase						      =					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     ,						_lowerCamelCase     ,						_lowerCamelCase					)]     ,						)     ,						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(_lowerCamelCase					)

										return written







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase							 =							{'''configuration_sew_d''': ['''SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWDConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowercase							 =							[
					    '''SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''SEWDForCTC''',
					    '''SEWDForSequenceClassification''',
					    '''SEWDModel''',
					    '''SEWDPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_sew_d import (
										    SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SEWDForCTC,
										    SEWDForSequenceClassification,
										    SEWDModel,
										    SEWDPreTrainedModel,
										)


else:
					import sys

					_lowercase							 =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





