


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

      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Union[str, Any]:
             a_      :	Tuple							=						None
             a_      :	str							=						None
             a_      :	Optional[Any]							=						None
             a_      :	Dict							=						None

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

             # Build dataset for splits
             a_      :	Optional[Any]							=						self.builder.as_dataset(
                 split='train'  ,							verification_mode=SCREAMING_SNAKE_CASE__  ,							in_memory=self.keep_in_memory  )
             return dataset
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : str  ,							SCREAMING_SNAKE_CASE__       : Dataset  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							SCREAMING_SNAKE_CASE__       : Optional[int] = None  ,							**SCREAMING_SNAKE_CASE__       : Tuple  ,							)	->    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."""  )

             a_      :	List[str]							=						dataset
             a_      :	Optional[int]							=						name
             a_      :	int							=						con
             a_      :	List[Any]							=						batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
             a_      :	Any							=						num_proc
             a_      :	Union[str, Any]							=						to_sql_kwargs
      def  SCREAMING_SNAKE_CASE							( self       : Dict  )	->    int:
             a_      :	Any							=						self.to_sql_kwargs.pop('sql'  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						self.to_sql_kwargs.pop('con'  ,							SCREAMING_SNAKE_CASE__  )
             a_      :	Tuple							=						self.to_sql_kwargs.pop('index'  ,							SCREAMING_SNAKE_CASE__  )

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

      def  SCREAMING_SNAKE_CASE							( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : str  ,							**SCREAMING_SNAKE_CASE__       : str  )	->    int:
             a_      :	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_      :	str							=						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  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )]  ,							)  ,							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 os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__    )		,					'''Tatoeba directory does not exist.'''    )
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Optional[Any]							=						tempfile.mkdtemp()
             return TatoebaConverter(save_dir=SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             self.resolver.convert_models(['heb-eng']  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             a_							,	a_      :	Dict							=						self.resolver.write_model_card('opus-mt-he-en'  ,							dry_run=SCREAMING_SNAKE_CASE__  )
             assert mmeta["long_pair"] == "heb-eng"


