
"""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   __a  (      _lowerCAmelCase	):






       def __init__(  self :							Any       ,      UpperCAmelCase_ :							Union[str, "sqlalchemy.sql.Selectable"]       ,      UpperCAmelCase_ :							Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]       ,      UpperCAmelCase_ :							Optional[Features] = None       ,      UpperCAmelCase_ :							str = None       ,      UpperCAmelCase_ :							bool = False       ,      **UpperCAmelCase_ :							Dict       ,      )->  Optional[Any]:







              """simple docstring"""
              super().__init__(features=UpperCAmelCase_       ,      cache_dir=UpperCAmelCase_       ,      keep_in_memory=UpperCAmelCase_       ,      **UpperCAmelCase_			)
              UpperCamelCase					 =		Sql(
                  cache_dir=UpperCAmelCase_       ,      features=UpperCAmelCase_       ,      sql=UpperCAmelCase_       ,      con=UpperCAmelCase_       ,      **UpperCAmelCase_       ,      )






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		None
              UpperCamelCase					 =		None
              UpperCamelCase					 =		None
              UpperCamelCase					 =		None

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

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





class   __a  :






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







              """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					 =		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 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  int:







              """simple docstring"""
              UpperCamelCase					 =		self.to_sql_kwargs.pop("sql"       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		self.to_sql_kwargs.pop("con"       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		self.to_sql_kwargs.pop("index"       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self._write(index=UpperCAmelCase_       ,      **self.to_sql_kwargs			)
              return written






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]       ,      UpperCAmelCase_ :							List[Any]			)->  str:







              """simple docstring"""
              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(UpperCAmelCase_       ,      offset + self.batch_size			)       ,      indices=self.dataset._indices       ,      )
              UpperCamelCase					 =		batch.to_pandas()
              UpperCamelCase					 =		df.to_sql(self.name       ,      self.con       ,      index=UpperCAmelCase_       ,      **UpperCAmelCase_			)
              return num_rows or len(UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      UpperCAmelCase_ :							Union[str, Any]       ,      **UpperCAmelCase_ :							List[str]			)->  int:







              """simple docstring"""
              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       ,      UpperCAmelCase_       ,      UpperCAmelCase_			)]       ,      )       ,      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 import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					str   =			FunnelTokenizer
       UpperCamelCase_		:					Any   =			FunnelTokenizerFast
       UpperCamelCase_		:					Union[str, Any]   =			True
       UpperCamelCase_		:					Tuple   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[int]:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		[
                  "<unk>",
                  "<cls>",
                  "<sep>",
                  "want",
                  "##want",
                  "##ed",
                  "wa",
                  "un",
                  "runn",
                  "##ing",
                  ",",
                  "low",
                  "lowest",
              ]
              UpperCamelCase					 =		os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["vocab_file"]			)
              with open(self.vocab_file       ,      "w"       ,      encoding="utf-8"			) as vocab_writer:
                     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]       ,      **UpperCAmelCase_ :							Optional[Any]			)->  str:







              """simple docstring"""
              return FunnelTokenizer.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      **UpperCAmelCase_ :							Any			)->  Union[str, Any]:







              """simple docstring"""
              return FunnelTokenizerFast.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict       ,      UpperCAmelCase_ :							Tuple			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		"UNwant\u00E9d,running"
              UpperCamelCase					 =		"unwanted, running"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  List[str]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class(self.vocab_file			)

              UpperCamelCase					 =		tokenizer.tokenize("UNwant\u00E9d,running"			)
              self.assertListEqual(UpperCAmelCase_       ,      ["un", "##want", "##ed", ",", "runn", "##ing"]			)
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      [7, 4, 5, 10, 8, 9]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizers(do_lower_case=UpperCAmelCase_			)
              for tokenizer in tokenizers:
                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"			)
                     UpperCamelCase					 =		len(inputs["input_ids"]			) - 1
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len			)

                     UpperCamelCase					 =		tokenizer("UNwant\u00E9d,running"       ,      "UNwant\u00E9d,running"			)
                     self.assertListEqual(inputs["token_type_ids"]       ,      [2] + [0] * sentence_len + [1] * sentence_len			)





