


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				(   _UpperCAmelCase):
					def __init__(						self       :						Dict	,      _lowerCAmelCase       :						NestedDataStructureLike[PathLike]	,      _lowerCAmelCase       :						Optional[NamedSplit] = None	,      _lowerCAmelCase       :						Optional[Features] = None	,      _lowerCAmelCase       :						str = None	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						bool = False	,      _lowerCAmelCase       :						Optional[int] = None	,      **_lowerCAmelCase       :						Union[str, Any]	,      )							->					List[Any]:



											'''simple docstring'''





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







					def 	a			(						self       :						int  )							->					List[str]:



											'''simple docstring'''





											if self.streaming:
																	__magic_name__					=      self.builder.as_streaming_dataset(split=self.split  )
											# Build regular (map-style) dataset
											else:
																	__magic_name__					=      None
																	__magic_name__					=      None
																	__magic_name__					=      None
																	__magic_name__					=      None

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







class      __snake_case				:
					def __init__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Dataset	,      _lowerCAmelCase       :						Union[PathLike, BinaryIO]	,      _lowerCAmelCase       :						Optional[int] = None	,      _lowerCAmelCase       :						Optional[int] = None	,      **_lowerCAmelCase       :						Optional[Any]	,      )							->					Any:



											'''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__					=      dataset
											__magic_name__					=      path_or_buf
											__magic_name__					=      batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
											__magic_name__					=      num_proc
											__magic_name__					=      'utf-8'
											__magic_name__					=      to_csv_kwargs
					def 	a			(						self       :						Union[str, Any]  )							->					int:



											'''simple docstring'''





											__magic_name__					=      self.to_csv_kwargs.pop('path_or_buf'	,      _lowerCAmelCase  )
											__magic_name__					=      self.to_csv_kwargs.pop('header'	,      _lowerCAmelCase  )
											__magic_name__					=      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:
																							__magic_name__					=      self._write(file_obj=_lowerCAmelCase	,      header=_lowerCAmelCase	,      index=_lowerCAmelCase	,      **self.to_csv_kwargs  )
											else:
																	__magic_name__					=      self._write(file_obj=self.path_or_buf	,      header=_lowerCAmelCase	,      index=_lowerCAmelCase	,      **self.to_csv_kwargs  )
											return written
					def 	a			(						self       :						Tuple	,      _lowerCAmelCase       :						List[Any]  )							->					List[Any]:



											'''simple docstring'''





											__magic_name__		,      __magic_name__		,      __magic_name__		,      __magic_name__					=      args

											__magic_name__					=      query_table(
											    table=self.dataset.data	,      key=slice(_lowerCAmelCase	,      offset + self.batch_size  )	,      indices=self.dataset._indices	,      )
											__magic_name__					=      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 	a			(						self       :						Any	,      _lowerCAmelCase       :						BinaryIO	,      _lowerCAmelCase       :						Optional[int]	,      _lowerCAmelCase       :						Optional[int]	,      **_lowerCAmelCase       :						Any  )							->					int:



											'''simple docstring'''





											__magic_name__					=      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__					=      self._batch_csv((offset, header, index, to_csv_kwargs)  )
																							written += file_obj.write(_lowerCAmelCase  )

											else:
																	__magic_name__		,      __magic_name__					=      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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase			:					Optional[int]         =							{
    '''configuration_dpr''': ['''DPR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPRConfig'''],
    '''tokenization_dpr''': [
        '''DPRContextEncoderTokenizer''',
        '''DPRQuestionEncoderTokenizer''',
        '''DPRReaderOutput''',
        '''DPRReaderTokenizer''',
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					List[str]         =							[
					    '''DPRContextEncoderTokenizerFast''',
					    '''DPRQuestionEncoderTokenizerFast''',
					    '''DPRReaderTokenizerFast''',
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[Any]         =							[
					    '''DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DPRContextEncoder''',
					    '''DPRPretrainedContextEncoder''',
					    '''DPRPreTrainedModel''',
					    '''DPRPretrainedQuestionEncoder''',
					    '''DPRPretrainedReader''',
					    '''DPRQuestionEncoder''',
					    '''DPRReader''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase			:					Optional[int]         =							[
					    '''TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFDPRContextEncoder''',
					    '''TFDPRPretrainedContextEncoder''',
					    '''TFDPRPretrainedQuestionEncoder''',
					    '''TFDPRPretrainedReader''',
					    '''TFDPRQuestionEncoder''',
					    '''TFDPRReader''',
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					UpperCamelCase			:					List[Any]         =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

