





"""simple docstring"""






import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
		import sqlitea

		import sqlalchemy




class 					__lowercase      (      __SCREAMING_SNAKE_CASE				):







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase						,      _UpperCAmelCase = None						,      _UpperCAmelCase = None						,      _UpperCAmelCase = False						,      **_UpperCAmelCase						,      ):
									super().__init__(features=A_						,      cache_dir=A_						,      keep_in_memory=A_						,      **A_  )
									__a			:    Optional[Any]      							=	Sql(
									    cache_dir=A_						,      features=A_						,      sql=A_						,      con=A_						,      **A_						,      )







					def 			_lowerCamelCase		(    self  ):
									__a			:    Any      							=	None
									__a			:    List[str]      							=	None
									__a			:    int      							=	None
									__a			:    int      							=	None

									self.builder.download_and_prepare(
									    download_config=A_						,      download_mode=A_						,      verification_mode=A_						,      base_path=A_						,      )

									# Build dataset for splits
									__a			:    Union[str, Any]      							=	self.builder.as_dataset(
									    split='''train'''						,      verification_mode=A_						,      in_memory=self.keep_in_memory  )
									return dataset






class 					__lowercase      :







					'''simple docstring'''




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

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

									__a			:    Tuple      							=	dataset
									__a			:    Any      							=	name
									__a			:    str      							=	con
									__a			:    Any      							=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									__a			:    Optional[int]      							=	num_proc
									__a			:    Optional[Any]      							=	to_sql_kwargs







					def 			_lowerCamelCase		(    self  ):
									__a			:    Optional[Any]      							=	self.to_sql_kwargs.pop('''sql'''						,      A_  )
									__a			:    Optional[Any]      							=	self.to_sql_kwargs.pop('''con'''						,      A_  )
									__a			:    List[str]      							=	self.to_sql_kwargs.pop('''index'''						,      A_  )

									__a			:    str      							=	self._write(index=A_						,      **self.to_sql_kwargs  )
									return written







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):
									__a							,    __a							,    __a			:    Union[str, Any]      							=	args
									__a			:    Optional[Any]      							=	{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
									__a			:    List[Any]      							=	query_table(
									    table=self.dataset.data						,      key=slice(A_						,      offset + self.batch_size  )						,      indices=self.dataset._indices						,      )
									__a			:    List[Any]      							=	batch.to_pandas()
									__a			:    Tuple      							=	df.to_sql(self.name						,      self.con						,      index=A_						,      **A_  )
									return num_rows or len(A_  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase						,      **_UpperCAmelCase  ):
									__a			:    Optional[int]      							=	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 SQL from Arrow format'''						,      ):
																	written += self._batch_sql((offset, index, to_sql_kwargs)  )
									else:
													__a							,    __a			:    Union[str, Any]      							=	len(self.dataset  ), self.batch_size
													with multiprocessing.Pool(self.num_proc  ) as pool:
																	for num_rows in logging.tqdm(
																	    pool.imap(
																	        self._batch_sql						,      [(offset, index, to_sql_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 SQL from Arrow format'''						,      ):
																					written += num_rows

									return written






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

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