

import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  UpperCamelCase_      (	_snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   MPNetTokenizer
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   MPNetTokenizerFast
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   True
  SCREAMING_SNAKE_CASE__					:			Optional[int]					    =   True



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			str:
      super().setUp()

      SCREAMING_SNAKE_CASE__         =    [
          "[UNK]",
          "[CLS]",
          "[SEP]",
          "[PAD]",
          "[MASK]",
          "want",
          "##want",
          "##ed",
          "wa",
          "un",
          "runn",
          "##ing",
          ",",
          "low",
          "lowest",
      ]
      SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					Any  ,	A_     :					Optional[Any]						)       ->			Dict:
      SCREAMING_SNAKE_CASE__         =    "UNwant\u00E9d,running"
      SCREAMING_SNAKE_CASE__         =    "unwanted, running"
      return input_text, output_text



  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    self.tokenizer_class(self.vocab_file						)

      SCREAMING_SNAKE_CASE__         =    tokenizer.tokenize("UNwant\u00E9d,running"						)
      self.assertListEqual(lowerCamelCase_  ,	["un", "##want", "##ed", ",", "runn", "##ing"]						)
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_						)  ,	[9, 6, 7, 12, 10, 11]						)







  @slow
  def   _UpperCamelCase     (						self     :					Optional[Any]						)       ->			Optional[Any]:
      SCREAMING_SNAKE_CASE__         =    self.tokenizer_class.from_pretrained("microsoft/mpnet-base"						)

      SCREAMING_SNAKE_CASE__         =    tokenizer.encode("sequence builders"  ,	add_special_tokens=lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    tokenizer.encode("multi-sequence build"  ,	add_special_tokens=lowerCamelCase_						)

      SCREAMING_SNAKE_CASE__         =    tokenizer.build_inputs_with_special_tokens(lowerCamelCase_						)
      SCREAMING_SNAKE_CASE__         =    tokenizer.build_inputs_with_special_tokens(lowerCamelCase_  ,	lowerCamelCase_						)

      assert encoded_sentence == [0] + text + [2]
      assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]







import sys
from dataclasses import dataclass
from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


if TYPE_CHECKING:
				import sqlitea

				import sqlalchemy


_lowercase:					Optional[Any]      =						datasets.utils.logging.get_logger(__name__)



@dataclass
class       _lowercase				(  datasets.BuilderConfig      ):





	"""simple docstring"""
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				True
	__A		      =				None
	__A		      =				None
	__A		      =				None
	__A		      =				10000
	__A		      =				None




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if self.sql is None:
											raise ValueError("sql must be specified"						)
						if self.con is None:
											raise ValueError("con must be specified"						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						,			lowerCamelCase_ = None						,			):
						"""simple docstring"""

						a   	=  config_kwargs.copy()
						# We need to stringify the Selectable object to make its hash deterministic

						# The process of stringifying is explained here: http://docs.sqlalchemy.org/en/latest/faq/sqlexpressions.html
						a   	=  config_kwargs["sql"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											if datasets.config.SQLALCHEMY_AVAILABLE and "sqlalchemy" in sys.modules:
																import sqlalchemy

																if isinstance(lowerCamelCase_						,			sqlalchemy.sql.Selectable						):
																					a   	=  sqlalchemy.create_engine(config_kwargs["con"].split("://"						)[0] + "://"						)
																					a   	=  str(sql.compile(dialect=engine.dialect						)						)
																					a   	=  sql_str
																else:
																					raise TypeError(
																					    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
											else:
																raise TypeError(
																    F'''Supported types for \'sql\' are string and sqlalchemy.sql.Selectable but got {type(lowerCamelCase_						)}: {sql}'''						)
						a   	=  config_kwargs["con"]
						if not isinstance(lowerCamelCase_						,			lowerCamelCase_						):
											a   	=  id(lowerCamelCase_						)
											logger.info(
											    F'''SQL connection \'con\' of type {type(lowerCamelCase_						)} couldn\'t be hashed properly. To enable hashing, specify \'con\' as URI string instead.'''						)

						return super().create_config_id(lowerCamelCase_						,			custom_features=lowerCamelCase_						)




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  {
						    "index_col": self.index_col,
						    "columns": self.columns,
						    "params": self.params,
						    "coerce_float": self.coerce_float,
						    "parse_dates": self.parse_dates,
						}
						return pd_read_sql_kwargs




class       _lowercase				(  datasets.ArrowBasedBuilder      ):





	"""simple docstring"""
	__A		      =				SqlConfig




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						return datasets.DatasetInfo(features=self.config.features						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						return [datasets.SplitGenerator(name=datasets.Split.TRAIN						,			gen_kwargs={}						)]




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						if self.config.features is not None:
											a   	=  self.config.features.arrow_schema
											if all(not require_storage_cast(lowerCamelCase_						) for feature in self.config.features.values()						):
																# cheaper cast
																a   	=  pa.Table.from_arrays([pa_table[field.name] for field in schema]						,			schema=lowerCamelCase_						)
											else:
																# more expensive cast; allows str <-> int/float or str to Audio for example
																a   	=  table_cast(lowerCamelCase_						,			lowerCamelCase_						)
						return pa_table




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  self.config.chunksize
						a   	=  pd.read_sql(
						    self.config.sql						,			self.config.con						,			chunksize=lowerCamelCase_						,			**self.config.pd_read_sql_kwargs						)
						a   	=  [sql_reader] if chunksize is None else sql_reader
						for chunk_idx, df in enumerate(lowerCamelCase_						):
											a   	=  pa.Table.from_pandas(lowerCamelCase_						)
											yield chunk_idx, self._cast_table(lowerCamelCase_						)
