
import os
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 logging


lowerCamelCase__     :		Optional[int]        = logging.get_logger(__name__)

lowerCamelCase__     :		Any        = {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCamelCase__     :		Tuple        = {
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    }
}

lowerCamelCase__     :		int        = {
    """camembert-base""": 5_1_2,
}

lowerCamelCase__     :		int        = """▁"""



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  List[Any]	   =      VOCAB_FILES_NAMES
 __lowerCAmelCase	:  Optional[Any]	   =      PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase	:  Optional[Any]	   =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase	:  str	   =      ['input_ids', 'attention_mask']


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="</s>"       ,	SCREAMING_SNAKE_CASE_="<s>"       ,	SCREAMING_SNAKE_CASE_="<unk>"       ,	SCREAMING_SNAKE_CASE_="<pad>"       ,	SCREAMING_SNAKE_CASE_="<mask>"       ,	SCREAMING_SNAKE_CASE_=["<s>NOTUSED", "</s>NOTUSED"]       ,	SCREAMING_SNAKE_CASE_ = None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   lowercase__	: Dict     		=   AddedToken(SCREAMING_SNAKE_CASE_       ,	lstrip=SCREAMING_SNAKE_CASE_       ,	rstrip=SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_) else mask_token

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

   super().__init__(
       bos_token=SCREAMING_SNAKE_CASE_       ,	eos_token=SCREAMING_SNAKE_CASE_       ,	unk_token=SCREAMING_SNAKE_CASE_       ,	sep_token=SCREAMING_SNAKE_CASE_       ,	cls_token=SCREAMING_SNAKE_CASE_       ,	pad_token=SCREAMING_SNAKE_CASE_       ,	mask_token=SCREAMING_SNAKE_CASE_       ,	additional_special_tokens=SCREAMING_SNAKE_CASE_       ,	sp_model_kwargs=self.sp_model_kwargs       ,	**SCREAMING_SNAKE_CASE_       ,	)
   lowercase__	: Tuple     		=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(str(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   vocab_file
   # HACK: These tokens were added by fairseq but don't seem to be actually used when duplicated in the actual
   # sentencepiece vocabulary (this is the case for <s> and </s>
   lowercase__	: int     		=   {"""<s>NOTUSED""": 0, """<pad>""": 1, """</s>NOTUSED""": 2, """<unk>""": 3}
   lowercase__	: Union[str, Any]     		=   len(self.fairseq_tokens_to_ids)
   lowercase__	: Any     		=   len(self.sp_model) + len(self.fairseq_tokens_to_ids)
   lowercase__	: Union[str, Any]     		=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   lowercase__	: Tuple     		=   [self.cls_token_id]
   lowercase__	: Dict     		=   [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None       ,	SCREAMING_SNAKE_CASE_ = False):


   '''simple docstring'''




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

   if token_ids_a is None:
     return [1] + ([0] * len(SCREAMING_SNAKE_CASE_)) + [1]
   return [1] + ([0] * len(SCREAMING_SNAKE_CASE_)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE_)) + [1]


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   [self.sep_token_id]
   lowercase__	: Dict     		=   [self.cls_token_id]

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


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return len(self.fairseq_tokens_to_ids) + len(self.sp_model)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Any     		=   {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.sp_model.encode(SCREAMING_SNAKE_CASE_       ,	out_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
   elif self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_) == 0:
     # Convert sentence piece unk token to fairseq unk token index
     return self.unk_token_id
   return self.fairseq_offset + self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if index in self.fairseq_ids_to_tokens:
     return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset)


 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   []
   lowercase__	: Optional[Any]     		=   """"""
   lowercase__	: List[Any]     		=   False
   for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
       if not prev_is_special:
         out_string += " "
       out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_) + token
       lowercase__	: List[str]     		=   True
       lowercase__	: List[Any]     		=   []
     else:
       current_sub_tokens.append(SCREAMING_SNAKE_CASE_)
       lowercase__	: Optional[int]     		=   False
   out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_)
   return out_string.strip()


 def __getstate__(       self):


   '''simple docstring'''




   lowercase__	: List[str]     		=   self.__dict__.copy()
   lowercase__	: int     		=   None
   return state


 def __setstate__(       self       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   d

   # for backward compatibility
   if not hasattr(self       ,	"""sp_model_kwargs"""):
     lowercase__	: Union[str, Any]     		=   {}

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



 def   lowercase__  (       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_ = None):


   '''simple docstring'''




   if not os.path.isdir(SCREAMING_SNAKE_CASE_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   lowercase__	: Optional[int]     		=   os.path.join(
       SCREAMING_SNAKE_CASE_       ,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

   if os.path.abspath(self.vocab_file) != os.path.abspath(SCREAMING_SNAKE_CASE_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file       ,	SCREAMING_SNAKE_CASE_)
   elif not os.path.isfile(self.vocab_file):
     with open(SCREAMING_SNAKE_CASE_       ,	"""wb""") as fi:
       lowercase__	: Tuple     		=   self.sp_model.serialized_model_proto()
       fi.write(SCREAMING_SNAKE_CASE_)

   return (out_vocab_file,)













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     (SCREAMING_SNAKE_CASE       ):
			'''simple docstring'''




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







					"""simple docstring"""






					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   a_			(     self  :       Optional[int]  )      ->      Optional[int]:







					"""simple docstring"""






					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     A     :
			'''simple docstring'''




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







					"""simple docstring"""






					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   a_			(     self  :       Any  )      ->      int:







					"""simple docstring"""






					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   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Optional[Any]:







					"""simple docstring"""






					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   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       Tuple				,      **__lowerCAmelCase  :       Dict  )      ->      int:







					"""simple docstring"""






					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



