

"""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 lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = False ,**lowerCamelCase_ ,)    ->      List[Any]:
          super().__init__(features=lowerCamelCase_ ,cache_dir=lowerCamelCase_ ,keep_in_memory=lowerCamelCase_ ,**lowerCamelCase_    )
          A				  =		Sql(
              cache_dir=lowerCamelCase_ ,features=lowerCamelCase_ ,sql=lowerCamelCase_ ,con=lowerCamelCase_ ,**lowerCamelCase_ ,)




    def UpperCamelCase__		( self    )    ->      List[Any]:
          A				  =		None
          A				  =		None
          A				  =		None
          A				  =		None

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

          # Build dataset for splits
          A				  =		self.builder.as_dataset(
              split="""train""" ,verification_mode=lowerCamelCase_ ,in_memory=self.keep_in_memory    )
          return dataset





class lowerCamelCase__   :



    '''simple docstring'''




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,**lowerCamelCase_ ,)    ->      List[Any]:
          if num_proc is not None and num_proc <= 0:
                raise ValueError(f'num_proc {num_proc} must be an integer > 0.'    )

          A				  =		dataset
          A				  =		name
          A				  =		con
          A				  =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
          A				  =		num_proc
          A				  =		to_sql_kwargs




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		self.to_sql_kwargs.pop("""sql""" ,lowerCamelCase_    )
          A				  =		self.to_sql_kwargs.pop("""con""" ,lowerCamelCase_    )
          A				  =		self.to_sql_kwargs.pop("""index""" ,lowerCamelCase_    )

          A				  =		self._write(index=lowerCamelCase_ ,**self.to_sql_kwargs    )
          return written




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




    def UpperCamelCase__		( self ,lowerCamelCase_ ,**lowerCamelCase_    )    ->      int:
          A				  =		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				  =		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







"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase													=get_tests_dir("fixtures/test_sentencepiece_bpe.model")





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   unittest.TestCase    ):



    '''simple docstring'''




    _lowerCamelCase					=   BartphoTokenizer
    _lowerCamelCase					=   False
    _lowerCamelCase					=   True




    def UpperCamelCase__		( self    )    ->      str:
          super().setUp()

          A				  =		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
          A				  =		dict(zip(lowerCamelCase_ ,range(len(lowerCamelCase_    )    )    )    )
          A				  =		{"""unk_token""": """<unk>"""}

          A				  =		os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""monolingual_vocab_file"""]    )
          with open(self.monolingual_vocab_file ,"""w""" ,encoding="""utf-8"""    ) as fp:
                for token in vocab_tokens:
                      fp.write(f'{token} {vocab_tokens[token]}\n'    )

          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          tokenizer.save_pretrained(self.tmpdirname    )




    def UpperCamelCase__		( self ,**lowerCamelCase_    )    ->      Tuple:
          kwargs.update(self.special_tokens_map    )
          return BartphoTokenizer.from_pretrained(self.tmpdirname ,**lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_    )    ->      Optional[Any]:
          A				  =		"""This is a là test"""
          A				  =		"""This is a<unk><unk> test"""
          return input_text, output_text




    def UpperCamelCase__		( self    )    ->      int:
          A				  =		BartphoTokenizer(lowerCamelCase_ ,self.monolingual_vocab_file ,**self.special_tokens_map    )
          A				  =		"""This is a là test"""
          A				  =		"""▁This ▁is ▁a ▁l à ▁t est""".split()
          A				  =		tokenizer.tokenize(lowerCamelCase_    )
          self.assertListEqual(lowerCamelCase_ ,lowerCamelCase_    )

          A				  =		tokens + [tokenizer.unk_token]
          A				  =		[4, 5, 6, 3, 3, 7, 8, 3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_    ) ,lowerCamelCase_    )




