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    __lowerCAmelCase					(    a_  ):



 '''simple docstring'''

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

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

               self.builder.download_and_prepare(
                   download_config=lowerCamelCase    ,     download_mode=lowerCamelCase    ,     verification_mode=lowerCamelCase    ,     base_path=lowerCamelCase    ,     num_proc=self.num_proc    ,     )
               _UpperCamelCase       :    Dict					  =					self.builder.as_dataset(
                   split=self.split    ,     verification_mode=lowerCamelCase    ,     in_memory=self.keep_in_memory					)
        return dataset







class    __lowerCAmelCase					:



 '''simple docstring'''

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

        _UpperCamelCase       :    List[Any]					  =					dataset
        _UpperCamelCase       :    Optional[int]					  =					path_or_buf
        _UpperCamelCase       :    Tuple					  =					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        _UpperCamelCase       :    int					  =					num_proc
        _UpperCamelCase       :    Union[str, Any]					  =					'utf-8'
        _UpperCamelCase       :    Union[str, Any]					  =					to_csv_kwargs

 def    __snake_case   (     self    :						Dict					)	->							int:
        _UpperCamelCase       :    List[Any]					  =					self.to_csv_kwargs.pop('path_or_buf'    ,     lowerCamelCase					)
        _UpperCamelCase       :    int					  =					self.to_csv_kwargs.pop('header'    ,     lowerCamelCase					)
        _UpperCamelCase       :    Optional[Any]					  =					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:
                      _UpperCamelCase       :    Any					  =					self._write(file_obj=lowerCamelCase    ,     header=lowerCamelCase    ,     index=lowerCamelCase    ,     **self.to_csv_kwargs					)
        else:
               _UpperCamelCase       :    int					  =					self._write(file_obj=self.path_or_buf    ,     header=lowerCamelCase    ,     index=lowerCamelCase    ,     **self.to_csv_kwargs					)
        return written

 def    __snake_case   (     self    :						Union[str, Any]    ,     lowerCamelCase    :						Tuple					)	->							str:
        _UpperCamelCase       :    List[Any]					  =					args

        _UpperCamelCase       :    Union[str, Any]					  =					query_table(
            table=self.dataset.data    ,     key=slice(lowerCamelCase    ,     offset + self.batch_size					)    ,     indices=self.dataset._indices    ,     )
        _UpperCamelCase       :    Tuple					  =					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    __snake_case   (     self    :						Tuple    ,     lowerCamelCase    :						BinaryIO    ,     lowerCamelCase    :						str    ,     lowerCamelCase    :						Union[str, Any]    ,     **lowerCamelCase    :						int					)	->							int:
        _UpperCamelCase       :    List[str]					  =					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'    ,     ):
                      _UpperCamelCase       :    List[str]					  =					self._batch_csv((offset, header, index, to_csv_kwargs)					)
                      written += file_obj.write(lowerCamelCase					)

        else:
               _UpperCamelCase       :    Union[str, Any]					  =					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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase     						=			{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			['''MPNetTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MPNetForMaskedLM''',
     '''MPNetForMultipleChoice''',
     '''MPNetForQuestionAnswering''',
     '''MPNetForSequenceClassification''',
     '''MPNetForTokenClassification''',
     '''MPNetLayer''',
     '''MPNetModel''',
     '''MPNetPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCAmelCase     						=			[
     '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFMPNetEmbeddings''',
     '''TFMPNetForMaskedLM''',
     '''TFMPNetForMultipleChoice''',
     '''TFMPNetForQuestionAnswering''',
     '''TFMPNetForSequenceClassification''',
     '''TFMPNetForTokenClassification''',
     '''TFMPNetMainLayer''',
     '''TFMPNetModel''',
     '''TFMPNetPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
 from .tokenization_mpnet import MPNetTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mpnet_fast import MPNetTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mpnet import (
      MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      MPNetForMaskedLM,
      MPNetForMultipleChoice,
      MPNetForQuestionAnswering,
      MPNetForSequenceClassification,
      MPNetForTokenClassification,
      MPNetLayer,
      MPNetModel,
      MPNetPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_mpnet import (
      TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFMPNetEmbeddings,
      TFMPNetForMaskedLM,
      TFMPNetForMultipleChoice,
      TFMPNetForQuestionAnswering,
      TFMPNetForSequenceClassification,
      TFMPNetForTokenClassification,
      TFMPNetMainLayer,
      TFMPNetModel,
      TFMPNetPreTrainedModel,
  )

else:
 import sys

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



