



"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_UpperCamelCase: int										=     logging.get_logger(__name__)

_UpperCamelCase: List[str]										=     {'vocab_file': 'spiece.model'}

_UpperCamelCase: Dict										=     {
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

_UpperCamelCase: int										=     {'bert_for_seq_generation': 5_1_2}


class      a__						(  _UpperCamelCase   ):
   _lowerCamelCase    							= VOCAB_FILES_NAMES
   _lowerCamelCase    							= PRETRAINED_VOCAB_FILES_MAP
   _lowerCamelCase    							= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _lowerCamelCase    							= []
   _lowerCamelCase    							= ['input_ids', 'attention_mask']


   def __init__(							self					:							Union[str, Any],   lowerCAmelCase					:							Any,   lowerCAmelCase					:							Optional[int]="<s>",   lowerCAmelCase					:							Optional[Any]="</s>",   lowerCAmelCase					:							Optional[Any]="<unk>",   lowerCAmelCase					:							Dict="<pad>",   lowerCAmelCase					:							str="<::::>",   lowerCAmelCase					:							Optional[Dict[str, Any]] = None,   **lowerCAmelCase					:							Any,   )			->				str:
         lowercase     :					int 		=      {} if sp_model_kwargs is None else sp_model_kwargs

         # Add extra_ids to the special token list
         super().__init__(
             bos_token=_UpperCAmelCase,   eos_token=_UpperCAmelCase,   unk_token=_UpperCAmelCase,   pad_token=_UpperCAmelCase,   sep_token=_UpperCAmelCase,   sp_model_kwargs=self.sp_model_kwargs,   **_UpperCAmelCase,   )

         lowercase     :					Dict 		=      vocab_file

         lowercase     :					List[str] 		=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
         self.sp_model.Load(_UpperCAmelCase	)


   @property
   def 				lowercase (							self					:							Tuple	)			->				Dict:
         return self.sp_model.get_piece_size()


   def 				lowercase (							self					:							str	)			->				str:
         lowercase     :					List[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					:							Optional[int]	)			->				List[Any]:
         lowercase     :					Tuple 		=      self.__dict__.copy()
         lowercase     :					str 		=      None
         return state


   def __setstate__(							self					:							Optional[Any],   lowerCAmelCase					:							Optional[int]	)			->				List[str]:
         lowercase     :					Optional[int] 		=      d

         # for backward compatibility
         if not hasattr(self,   'sp_model_kwargs'	):
               lowercase     :					Dict 		=      {}

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


   def 				lowercase (							self					:							Any,   lowerCAmelCase					:							str	)			->				List[Any]:
         return self.sp_model.encode(_UpperCAmelCase,   out_type=_UpperCAmelCase	)


   def 				lowercase (							self					:							Optional[int],   lowerCAmelCase					:							Tuple	)			->				List[Any]:
         return self.sp_model.piece_to_id(_UpperCAmelCase	)


   def 				lowercase (							self					:							Optional[int],   lowerCAmelCase					:							str	)			->				Union[str, Any]:
         lowercase     :					Union[str, Any] 		=      self.sp_model.IdToPiece(_UpperCAmelCase	)
         return token


   def 				lowercase (							self					:							str,   lowerCAmelCase					:							Tuple	)			->				str:
         lowercase     :					Any 		=      []
         lowercase     :					Any 		=      ''
         for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                     out_string += self.sp_model.decode(_UpperCAmelCase	) + token
                     lowercase     :					Any 		=      []
               else:
                     current_sub_tokens.append(_UpperCAmelCase	)
         out_string += self.sp_model.decode(_UpperCAmelCase	)
         return out_string.strip()






   def 				lowercase (							self					:							Any,   lowerCAmelCase					:							str,   lowerCAmelCase					:							Optional[str] = None	)			->				Optional[Any]:
         if not os.path.isdir(_UpperCAmelCase	):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
               return
         lowercase     :					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:
                     lowercase     :					Tuple 		=      self.sp_model.serialized_model_proto()
                     fi.write(_UpperCAmelCase	)

         return (out_vocab_file,)





'''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




