


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      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   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						,    )
     lowerCamelCase			: Any   =    path_or_paths if isinstance(__A						,    __A			) else {self.split: path_or_paths}
     lowerCamelCase			: Union[str, Any]   =    Csv(
         cache_dir=__A						,    data_files=__A						,    features=__A						,    **__A						,    )






   def _snake_case   (		self			):






     """simple docstring"""





     if self.streaming:
       lowerCamelCase			: Optional[int]   =    self.builder.as_streaming_dataset(split=self.split			)
     # Build regular (map-style) dataset
     else:
       lowerCamelCase			: Any   =    None
       lowerCamelCase			: Optional[int]   =    None
       lowerCamelCase			: Tuple   =    None
       lowerCamelCase			: List[str]   =    None

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



class      UpperCAmelCase_ :

   '''simple docstring'''




   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."""			)

     lowerCamelCase			: List[Any]   =    dataset
     lowerCamelCase			: Optional[Any]   =    path_or_buf
     lowerCamelCase			: Any   =    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     lowerCamelCase			: List[str]   =    num_proc
     lowerCamelCase			: Dict   =    "utf-8"
     lowerCamelCase			: Optional[int]   =    to_csv_kwargs






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.to_csv_kwargs.pop("path_or_buf"						,    __A			)
     lowerCamelCase			: Dict   =    self.to_csv_kwargs.pop("header"						,    __A			)
     lowerCamelCase			: int   =    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:
         lowerCamelCase			: Optional[Any]   =    self._write(file_obj=__A						,    header=__A						,    index=__A						,    **self.to_csv_kwargs			)
     else:
       lowerCamelCase			: Any   =    self._write(file_obj=self.path_or_buf						,    header=__A						,    index=__A						,    **self.to_csv_kwargs			)
     return written






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase			: List[str]   =    args

     lowerCamelCase			: Dict   =    query_table(
         table=self.dataset.data						,    key=slice(__A						,    offset + self.batch_size			)						,    indices=self.dataset._indices						,    )
     lowerCamelCase			: Union[str, Any]   =    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 _snake_case   (		self						,    __A						,    __A						,    __A						,    **__A			):






     """simple docstring"""





     lowerCamelCase			: 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"						,    ):
         lowerCamelCase			: int   =    self._batch_csv((offset, header, index, to_csv_kwargs)			)
         written += file_obj.write(__A			)

     else:
       lowerCamelCase   ,     lowerCamelCase			: List[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						,    __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







from typing import TYPE_CHECKING

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


_snake_case					=					{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''HubertForCTC''',
       '''HubertForSequenceClassification''',
       '''HubertModel''',
       '''HubertPreTrainedModel''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case					=					[
       '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFHubertForCTC''',
       '''TFHubertModel''',
       '''TFHubertPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_hubert import (
          HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          HubertForCTC,
          HubertForSequenceClassification,
          HubertModel,
          HubertPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_hubert import (
          TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFHubertForCTC,
          TFHubertModel,
          TFHubertPreTrainedModel,
      )


else:
   import sys

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



