



'''simple docstring'''

import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {'''vocab_file''': '''spiece.model'''}

__lowerCAmelCase									=   {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       List[Any]=False	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Optional[int]=False	,_UpperCAmelCase					:       Dict="<s>"	,_UpperCAmelCase					:       Tuple="</s>"	,_UpperCAmelCase					:       Dict="<unk>"	,_UpperCAmelCase					:       Dict="<sep>"	,_UpperCAmelCase					:       List[str]="<pad>"	,_UpperCAmelCase					:       List[Any]="<cls>"	,_UpperCAmelCase					:       Union[str, Any]="<mask>"	,_UpperCAmelCase					:       Optional[Any]=["<eop>", "<eod>"]	,_UpperCAmelCase					:       Optional[Dict[str, Any]] = None	,**_UpperCAmelCase					:       Union[str, Any]	,):
									_a     :      List[Any]        =							AddedToken(_UpperCAmelCase	,lstrip=_UpperCAmelCase	,rstrip=_UpperCAmelCase		) if isinstance(_UpperCAmelCase	,_UpperCAmelCase		) else mask_token

									_a     :      int        =							{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    do_lower_case=_UpperCAmelCase	,remove_space=_UpperCAmelCase	,keep_accents=_UpperCAmelCase	,bos_token=_UpperCAmelCase	,eos_token=_UpperCAmelCase	,unk_token=_UpperCAmelCase	,sep_token=_UpperCAmelCase	,pad_token=_UpperCAmelCase	,cls_token=_UpperCAmelCase	,mask_token=_UpperCAmelCase	,additional_special_tokens=_UpperCAmelCase	,sp_model_kwargs=self.sp_model_kwargs	,**_UpperCAmelCase	,)

									_a     :      List[Any]        =							3

									_a     :      Optional[int]        =							do_lower_case
									_a     :      Dict        =							remove_space
									_a     :      Union[str, Any]        =							keep_accents
									_a     :      Any        =							vocab_file

									_a     :      Optional[int]        =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(_UpperCAmelCase		)

									try:
											import jieba
									except ModuleNotFoundError as error:
											raise error.__class__(
											    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
											    'See https://pypi.org/project/jieba/ for installation.'		)
									_a     :      Optional[int]        =							jieba
									_a     :      Union[str, Any]        =							str.maketrans(' \n'	,'\u2582\u2583'		)







							@property
							# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
							def 							__lowercase			(							self					:       Dict		):
									return len(self.sp_model		)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							{self.convert_ids_to_tokens(_UpperCAmelCase		): i for i in range(self.vocab_size		)}
									vocab.update(self.added_tokens_encoder		)
									return vocab







							def __getstate__(							self					:       int		):
									_a     :      Tuple        =							self.__dict__.copy()
									_a     :      Any        =							None
									return state







							def __setstate__(							self					:       Any	,_UpperCAmelCase					:       Optional[int]		):
									_a     :      Any        =							d

									# for backward compatibility
									if not hasattr(self	,'sp_model_kwargs'		):
											_a     :      Optional[int]        =							{}

									_a     :      Tuple        =							spm.SentencePieceProcessor(**self.sp_model_kwargs		)
									self.sp_model.Load(self.vocab_file		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       str		):
									if self.remove_space:
											_a     :      List[str]        =							' '.join(inputs.strip().split()		)
									else:
											_a     :      int        =							inputs
									_a     :      str        =							outputs.replace('``'	,'"'		).replace('\'\''	,'"'		)

									if not self.keep_accents:
											_a     :      Dict        =							unicodedata.normalize('NFKD'	,_UpperCAmelCase		)
											_a     :      List[str]        =							''.join([c for c in outputs if not unicodedata.combining(_UpperCAmelCase		)]		)
									if self.do_lower_case:
											_a     :      Dict        =							outputs.lower()

									return outputs







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       str		):
									_a     :      int        =							self.preprocess_text(_UpperCAmelCase		)
									_a     :      Dict        =							self.sp_model.encode(_UpperCAmelCase	,out_type=_UpperCAmelCase		)
									_a     :      Optional[Any]        =							[]
									for piece in pieces:
											if len(_UpperCAmelCase		) > 1 and piece[-1] == str(','		) and piece[-2].isdigit():
													_a     :      Dict        =							self.sp_model.EncodeAsPieces(piece[:-1].replace(_UpperCAmelCase	,''		)		)
													if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
															if len(cur_pieces[0]		) == 1:
																	_a     :      Optional[int]        =							cur_pieces[1:]
															else:
																	_a     :      Optional[int]        =							cur_pieces[0][1:]
													cur_pieces.append(piece[-1]		)
													new_pieces.extend(_UpperCAmelCase		)
											else:
													new_pieces.append(_UpperCAmelCase		)

									return new_pieces







							def 							__lowercase			(							self					:       Optional[int]	,_UpperCAmelCase					:       Union[str, Any]		):
									return self.sp_model.PieceToId(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Union[str, Any]	,_UpperCAmelCase					:       List[str]		):
									return self.sp_model.IdToPiece(_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       Any		):
									_a     :      Dict        =							''.join(_UpperCAmelCase		).replace(_UpperCAmelCase	,' '		).strip()
									return out_string







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									_a     :      List[str]        =							[self.sep_token_id]
									_a     :      int        =							[self.cls_token_id]
									if token_ids_a is None:
											return token_ids_a + sep + cls
									return token_ids_a + sep + token_ids_a + sep + cls







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None	,_UpperCAmelCase					:       bool = False		):

									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=_UpperCAmelCase	,token_ids_a=_UpperCAmelCase	,already_has_special_tokens=_UpperCAmelCase		)

									if token_ids_a is not None:
											return ([0] * len(_UpperCAmelCase		)) + [1] + ([0] * len(_UpperCAmelCase		)) + [1, 1]
									return ([0] * len(_UpperCAmelCase		)) + [1, 1]







							def 							__lowercase			(							self					:       Optional[Any]	,_UpperCAmelCase					:       List[int]	,_UpperCAmelCase					:       Optional[List[int]] = None		):
									_a     :      Union[str, Any]        =							[self.sep_token_id]
									_a     :      List[str]        =							[2]

									if token_ids_a is None:
											return len(token_ids_a + sep		) * [0] + cls_segment_id
									return len(token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1] + cls_segment_id







							def 							__lowercase			(							self					:       List[str]	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Optional[str] = None		):
									if not os.path.isdir(_UpperCAmelCase		):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""		)
											return
									_a     :      List[Any]        =							os.path.join(
									    _UpperCAmelCase	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_UpperCAmelCase		) and os.path.isfile(self.vocab_file		):
											copyfile(self.vocab_file	,_UpperCAmelCase		)
									elif not os.path.isfile(self.vocab_file		):
											with open(_UpperCAmelCase	,'wb'		) as fi:
													_a     :      Optional[Any]        =							self.sp_model.serialized_model_proto()
													fi.write(_UpperCAmelCase		)

									return (out_vocab_file,)







							def 							__lowercase			(							self					:       int	,*_UpperCAmelCase					:       Dict	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      List[str]        =							super()._decode(*_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[str]        =							text.replace(' '	,''		).replace('\u2582'	,' '		).replace('\u2583'	,'\n'		)
									return text









'''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 	__magic_name__						(    _UpperCamelCase   ):







							def __init__(							self					:       Optional[int]	,_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	,):
									super().__init__(features=_UpperCAmelCase	,cache_dir=_UpperCAmelCase	,keep_in_memory=_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      Tuple        =							Sql(
									    cache_dir=_UpperCAmelCase	,features=_UpperCAmelCase	,sql=_UpperCAmelCase	,con=_UpperCAmelCase	,**_UpperCAmelCase	,)







							def 							__lowercase			(							self					:       Dict		):
									_a     :      Optional[Any]        =							None
									_a     :      Dict        =							None
									_a     :      Dict        =							None
									_a     :      Optional[int]        =							None

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

									# Build dataset for splits
									_a     :      List[str]        =							self.builder.as_dataset(
									    split='train'	,verification_mode=_UpperCAmelCase	,in_memory=self.keep_in_memory		)
									return dataset

class 	__magic_name__						:







							def __init__(							self					:       Optional[int]	,_UpperCAmelCase					:       Dataset	,_UpperCAmelCase					:       str	,_UpperCAmelCase					:       Union[str, "sqlalchemy.engine.Connection", "sqlalchemy.engine.Engine", "sqlite3.Connection"]	,_UpperCAmelCase					:       Optional[int] = None	,_UpperCAmelCase					:       Optional[int] = None	,**_UpperCAmelCase					:       Dict	,):
									if num_proc is not None and num_proc <= 0:
											raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""		)

									_a     :      Dict        =							dataset
									_a     :      List[Any]        =							name
									_a     :      Tuple        =							con
									_a     :      Union[str, Any]        =							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
									_a     :      List[Any]        =							num_proc
									_a     :      Tuple        =							to_sql_kwargs







							def 							__lowercase			(							self					:       List[Any]		):
									_a     :      Tuple        =							self.to_sql_kwargs.pop('sql'	,_UpperCAmelCase		)
									_a     :      str        =							self.to_sql_kwargs.pop('con'	,_UpperCAmelCase		)
									_a     :      Optional[Any]        =							self.to_sql_kwargs.pop('index'	,_UpperCAmelCase		)

									_a     :      Any        =							self._write(index=_UpperCAmelCase	,**self.to_sql_kwargs		)
									return written







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







							def 							__lowercase			(							self					:       int	,_UpperCAmelCase					:       Optional[int]	,**_UpperCAmelCase					:       List[Any]		):
									_a     :      Union[str, 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:
											_a       ,					_a     :      List[Any]        =							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




