




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       a					(       _A      ):

     '''simple docstring'''





     def __init__(     self       :      Dict ,   __snake_case       :      Union[str, "sqlalchemy.sql.Selectable"] ,   __snake_case       :      Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] ,   __snake_case       :      Optional[Features] = None ,   __snake_case       :      str = None ,   __snake_case       :      bool = False ,   **__snake_case       :      List[Any] ,   ):
         super().__init__(features=_lowerCAmelCase ,   cache_dir=_lowerCAmelCase ,   keep_in_memory=_lowerCAmelCase ,   **_lowerCAmelCase    )
         UpperCAmelCase_										=					Sql(
             cache_dir=_lowerCAmelCase ,   features=_lowerCAmelCase ,   sql=_lowerCAmelCase ,   con=_lowerCAmelCase ,   **_lowerCAmelCase ,   )




     def   lowerCamelCase_       (     self       :      int    ):
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					None

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

         # Build dataset for splits
         UpperCAmelCase_										=					self.builder.as_dataset(
             split='''train''' ,   verification_mode=_lowerCAmelCase ,   in_memory=self.keep_in_memory    )
         return dataset



class       a					:

     '''simple docstring'''





     def __init__(     self       :      Tuple ,   __snake_case       :      Dataset ,   __snake_case       :      str ,   __snake_case       :      Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"] ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[int] = None ,   **__snake_case       :      Optional[int] ,   ):

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

         UpperCAmelCase_										=					dataset
         UpperCAmelCase_										=					name
         UpperCAmelCase_										=					con
         UpperCAmelCase_										=					batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
         UpperCAmelCase_										=					num_proc
         UpperCAmelCase_										=					to_sql_kwargs
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         UpperCAmelCase_										=					self.to_sql_kwargs.pop('''sql''' ,   _lowerCAmelCase    )
         UpperCAmelCase_										=					self.to_sql_kwargs.pop('''con''' ,   _lowerCAmelCase    )
         UpperCAmelCase_										=					self.to_sql_kwargs.pop('''index''' ,   _lowerCAmelCase    )

         UpperCAmelCase_										=					self._write(index=_lowerCAmelCase ,   **self.to_sql_kwargs    )
         return written
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Dict    ):
         UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					args
         UpperCAmelCase_										=					{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
         UpperCAmelCase_										=					query_table(
             table=self.dataset.data ,   key=slice(_lowerCAmelCase ,   offset + self.batch_size    ) ,   indices=self.dataset._indices ,   )
         UpperCAmelCase_										=					batch.to_pandas()
         UpperCAmelCase_										=					df.to_sql(self.name ,   self.con ,   index=_lowerCAmelCase ,   **_lowerCAmelCase    )
         return num_rows or len(_lowerCAmelCase    )




     def   lowerCamelCase_       (     self       :      Dict ,   __snake_case       :      Any ,   **__snake_case       :      Optional[int]    ):
         UpperCAmelCase_										=					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:
             UpperCAmelCase_			,							UpperCAmelCase_										=					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 ,   _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 SQL from Arrow format''' ,   ):
                     written += num_rows

         return written






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=_A      )
class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  field(default='audio-classification'     ,       metadata={'include_in_asdict_even_if_is_default': True}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'audio': Audio()}      )
     lowerCAmelCase    :	ClassVar[Features]       		=  Features({'labels': ClassLabel}      )
     lowerCAmelCase    :	str       		=  "audio"
     lowerCAmelCase    :	str       		=  "labels"
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      List[Any]    ):
         if self.label_column not in features:
             raise ValueError(F'Column {self.label_column} is not present in features.'    )
         if not isinstance(features[self.label_column] ,   __snake_case    ):
             raise ValueError(F'Column {self.label_column} is not a ClassLabel.'    )
         UpperCAmelCase_										=					copy.deepcopy(self    )
         UpperCAmelCase_										=					self.label_schema.copy()
         UpperCAmelCase_										=					features[self.label_column]
         UpperCAmelCase_										=					label_schema
         return task_template




     @property
     def   lowerCamelCase_       (     self       :      Tuple    ):
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





