



"""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    __snake_case(					__A       ):




    def __init__(   self    ,							A_    ,							A_ = None    ,							A_ = None    ,							A_ = None    ,							A_ = False    ,							A_ = False    ,							A_ = None    ,							**A_    ,							):





      '''simple docstring'''



      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   A   (   self			):





      '''simple docstring'''



      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    __snake_case:




    def __init__(   self    ,							A_    ,							A_    ,							A_ = None    ,							A_ = None    ,							**A_    ,							):





      '''simple docstring'''



      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   A   (   self			):





      '''simple docstring'''



      _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   A   (   self    ,							A_			):





      '''simple docstring'''



      _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   A   (   self    ,							A_    ,							A_    ,							A_    ,							**A_			):





      '''simple docstring'''



      _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 typing import TYPE_CHECKING

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


lowerCamelCase				:     Dict								=  {
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     Optional[Any]								=  ["""CamembertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     int								=  [
        """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """CamembertForCausalLM""",
        """CamembertForMaskedLM""",
        """CamembertForMultipleChoice""",
        """CamembertForQuestionAnswering""",
        """CamembertForSequenceClassification""",
        """CamembertForTokenClassification""",
        """CamembertModel""",
        """CamembertPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase				:     str								=  [
        """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFCamembertForCausalLM""",
        """TFCamembertForMaskedLM""",
        """TFCamembertForMultipleChoice""",
        """TFCamembertForQuestionAnswering""",
        """TFCamembertForSequenceClassification""",
        """TFCamembertForTokenClassification""",
        """TFCamembertModel""",
        """TFCamembertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert import CamembertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_camembert_fast import CamembertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_camembert import (
            CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CamembertForCausalLM,
            CamembertForMaskedLM,
            CamembertForMultipleChoice,
            CamembertForQuestionAnswering,
            CamembertForSequenceClassification,
            CamembertForTokenClassification,
            CamembertModel,
            CamembertPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_camembert import (
            TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFCamembertForCausalLM,
            TFCamembertForMaskedLM,
            TFCamembertForMultipleChoice,
            TFCamembertForQuestionAnswering,
            TFCamembertForSequenceClassification,
            TFCamembertForTokenClassification,
            TFCamembertModel,
            TFCamembertPreTrainedModel,
        )

else:
    import sys

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