


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     snake_case__  (      _A  ):
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = False     ,      _UpperCAmelCase = False     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										super().__init__(
										    _UpperCAmelCase     ,      split=_UpperCAmelCase     ,      features=_UpperCAmelCase     ,      cache_dir=_UpperCAmelCase     ,      keep_in_memory=_UpperCAmelCase     ,      streaming=_UpperCAmelCase     ,      num_proc=_UpperCAmelCase     ,      **_UpperCAmelCase     ,      )
										__magic_name__				:							str				=      path_or_paths if isinstance(_UpperCAmelCase     ,      _UpperCAmelCase      ) else {self.split: path_or_paths}
										__magic_name__				:							Dict				=      Csv(
										    cache_dir=_UpperCAmelCase     ,      data_files=_UpperCAmelCase     ,      features=_UpperCAmelCase     ,      **_UpperCAmelCase     ,      )





				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										if self.streaming:
																__magic_name__				:							List[Any]				=      self.builder.as_streaming_dataset(split=self.split      )
										# Build regular (map-style) dataset
										else:
																__magic_name__				:							Any				=      None
																__magic_name__				:							Any				=      None
																__magic_name__				:							Union[str, Any]				=      None
																__magic_name__				:							List[Any]				=      None

																self.builder.download_and_prepare(
																    download_config=_UpperCAmelCase     ,      download_mode=_UpperCAmelCase     ,      verification_mode=_UpperCAmelCase     ,      base_path=_UpperCAmelCase     ,      num_proc=self.num_proc     ,      )
																__magic_name__				:							Any				=      self.builder.as_dataset(
																    split=self.split     ,      verification_mode=_UpperCAmelCase     ,      in_memory=self.keep_in_memory      )
										return dataset






class     snake_case__  :
				def __init__(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase = None     ,      _UpperCAmelCase = None     ,      **_UpperCAmelCase     ,      ):




										"""simple docstring"""




										if num_proc is not None and num_proc <= 0:
																raise ValueError(F"num_proc {num_proc} must be an integer > 0."      )

										__magic_name__				:							List[Any]				=      dataset
										__magic_name__				:							Dict				=      path_or_buf
										__magic_name__				:							List[str]				=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
										__magic_name__				:							Any				=      num_proc
										__magic_name__				:							Optional[Any]				=      'utf-8'
										__magic_name__				:							str				=      to_csv_kwargs
				def  UpperCamelCase_						(  self      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      self.to_csv_kwargs.pop('path_or_buf'     ,      _UpperCAmelCase      )
										__magic_name__				:							str				=      self.to_csv_kwargs.pop('header'     ,      _UpperCAmelCase      )
										__magic_name__				:							Union[str, Any]				=      self.to_csv_kwargs.pop('index'     ,      _UpperCAmelCase      )

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




										"""simple docstring"""




										__magic_name__		, __magic_name__		, __magic_name__		, __magic_name__				:							Optional[int]				=      args

										__magic_name__				:							Dict				=      query_table(
										    table=self.dataset.data     ,      key=slice(_UpperCAmelCase     ,      offset + self.batch_size      )     ,      indices=self.dataset._indices     ,      )
										__magic_name__				:							Optional[Any]				=      batch.to_pandas().to_csv(
										    path_or_buf=_UpperCAmelCase     ,      header=header if (offset == 0) else False     ,      index=_UpperCAmelCase     ,      **_UpperCAmelCase      )
										return csv_str.encode(self.encoding      )





				def  UpperCamelCase_						(  self     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      _UpperCAmelCase     ,      **_UpperCAmelCase      ):




										"""simple docstring"""




										__magic_name__				:							List[Any]				=      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'     ,      ):
																						__magic_name__				:							str				=      self._batch_csv((offset, header, index, to_csv_kwargs)      )
																						written += file_obj.write(_UpperCAmelCase      )

										else:
																__magic_name__		, __magic_name__				:							Dict				=      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     ,      _UpperCAmelCase     ,      _UpperCAmelCase      )]     ,      )     ,      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(_UpperCAmelCase      )

										return written






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case__     							= {
    'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
    'tokenization_flaubert': ['FlaubertTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'FlaubertForMultipleChoice',
			    'FlaubertForQuestionAnswering',
			    'FlaubertForQuestionAnsweringSimple',
			    'FlaubertForSequenceClassification',
			    'FlaubertForTokenClassification',
			    'FlaubertModel',
			    'FlaubertWithLMHeadModel',
			    'FlaubertPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__     							= [
			    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TFFlaubertForMultipleChoice',
			    'TFFlaubertForQuestionAnsweringSimple',
			    'TFFlaubertForSequenceClassification',
			    'TFFlaubertForTokenClassification',
			    'TFFlaubertModel',
			    'TFFlaubertPreTrainedModel',
			    'TFFlaubertWithLMHeadModel',
			]


if TYPE_CHECKING:
			from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
			from .tokenization_flaubert import FlaubertTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flaubert import (
						    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FlaubertForMultipleChoice,
						    FlaubertForQuestionAnswering,
						    FlaubertForQuestionAnsweringSimple,
						    FlaubertForSequenceClassification,
						    FlaubertForTokenClassification,
						    FlaubertModel,
						    FlaubertPreTrainedModel,
						    FlaubertWithLMHeadModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_flaubert import (
						    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFFlaubertForMultipleChoice,
						    TFFlaubertForQuestionAnsweringSimple,
						    TFFlaubertForSequenceClassification,
						    TFFlaubertForTokenClassification,
						    TFFlaubertModel,
						    TFFlaubertPreTrainedModel,
						    TFFlaubertWithLMHeadModel,
						)

else:
			import sys

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


