





"""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						,   _A						,   _A						,   _A = None						,   _A = None						,   _A = False						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(features=_A						,   cache_dir=_A						,   keep_in_memory=_A						,   **_A    )
									UpperCamelCase				:		List[Any]				   =			Sql(
									    cache_dir=_A						,   features=_A						,   sql=_A						,   con=_A						,   **_A						,   )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			None
									UpperCamelCase				:		Optional[int]				   =			None
									UpperCamelCase				:		Dict				   =			None
									UpperCamelCase				:		Union[str, Any]				   =			None

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

									# Build dataset for splits
									UpperCamelCase				:		Optional[int]				   =			self.builder.as_dataset(
									    split="""train"""						,   verification_mode=_A						,   in_memory=self.keep_in_memory    )
									return dataset






class 							lowercase__			:

			"""simple docstring"""







			def __init__(      self						,   _A						,   _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."""    )

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







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		int				   =			self.to_sql_kwargs.pop("""sql"""						,   _A    )
									UpperCamelCase				:		Optional[Any]				   =			self.to_sql_kwargs.pop("""con"""						,   _A    )
									UpperCamelCase				:		List[Any]				   =			self.to_sql_kwargs.pop("""index"""						,   _A    )

									UpperCamelCase				:		int				   =			self._write(index=_A						,   **self.to_sql_kwargs    )
									return written







			def   _a      (      self						,   _A    ):






									'''simple docstring'''







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







			def   _a      (      self						,   _A						,   **_A    ):






									'''simple docstring'''







									UpperCamelCase				:		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 SQL from Arrow format"""						,   ):
																					written += self._batch_sql((offset, index, to_sql_kwargs)    )
									else:
															UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			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






"""simple docstring"""


import re

from filelock import FileLock


try:
  import nltk

  UpperCAmelCase													=True
except (ImportError, ModuleNotFoundError):
  UpperCAmelCase													=False

if NLTK_AVAILABLE:
  with FileLock(".lock") as lock:
    nltk.download("punkt", quiet=True)



def   _A					(	_a	:						str				):



      """simple docstring"""






      re.sub("""<n>"""     ,   """"""     ,   _a				)  # remove pegasus newline char
      assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
      return "\n".join(nltk.sent_tokenize(_a				)				)




