





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    lowerCAmelCase							(					A__			):
 def __init__(      self  :      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  :      Union[str, Any]			,     )			->	Optional[int]:
     super().__init__(features=lowerCAmelCase__			,     cache_dir=lowerCAmelCase__			,     keep_in_memory=lowerCAmelCase__			,     **lowerCAmelCase__	)
     lowerCamelCase__    :      Any					 =		Sql(
         cache_dir=lowerCAmelCase__			,     features=lowerCAmelCase__			,     sql=lowerCAmelCase__			,     con=lowerCAmelCase__			,     **lowerCAmelCase__			,     )







 def      A_	(      self  :      List[Any]	)			->	Union[str, Any]:
     lowerCamelCase__    :      Optional[int]					 =		None
     lowerCamelCase__    :      int					 =		None
     lowerCamelCase__    :      Optional[Any]					 =		None
     lowerCamelCase__    :      Dict					 =		None

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

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


class    lowerCAmelCase							:
 def __init__(      self  :      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  :      Union[str, Any]			,     )			->	int:
     if num_proc is not None and num_proc <= 0:
         raise ValueError(F"""num_proc {num_proc} must be an integer > 0."""	)

     lowerCamelCase__    :      List[Any]					 =		dataset
     lowerCamelCase__    :      Tuple					 =		name
     lowerCamelCase__    :      Optional[Any]					 =		con
     lowerCamelCase__    :      Union[str, Any]					 =		batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
     lowerCamelCase__    :      List[Any]					 =		num_proc
     lowerCamelCase__    :      Optional[int]					 =		to_sql_kwargs
 def      A_	(      self  :      Tuple	)			->	int:
     lowerCamelCase__    :      Optional[int]					 =		self.to_sql_kwargs.pop('sql'			,     lowerCAmelCase__	)
     lowerCamelCase__    :      Optional[int]					 =		self.to_sql_kwargs.pop('con'			,     lowerCAmelCase__	)
     lowerCamelCase__    :      int					 =		self.to_sql_kwargs.pop('index'			,     lowerCAmelCase__	)

     lowerCamelCase__    :      int					 =		self._write(index=lowerCAmelCase__			,     **self.to_sql_kwargs	)
     return written
 def      A_	(      self  :      Dict			,     UpperCAmelCase  :      List[Any]	)			->	Optional[Any]:
     lowerCamelCase__    :      List[str]					 =		args
     lowerCamelCase__    :      Union[str, Any]					 =		{**to_sql_kwargs, '''if_exists''': '''append'''} if offset > 0 else to_sql_kwargs
     lowerCamelCase__    :      List[str]					 =		query_table(
         table=self.dataset.data			,     key=slice(lowerCAmelCase__			,     offset + self.batch_size	)			,     indices=self.dataset._indices			,     )
     lowerCamelCase__    :      Union[str, Any]					 =		batch.to_pandas()
     lowerCamelCase__    :      Union[str, Any]					 =		df.to_sql(self.name			,     self.con			,     index=lowerCAmelCase__			,     **lowerCAmelCase__	)
     return num_rows or len(lowerCAmelCase__	)







 def      A_	(      self  :      str			,     UpperCAmelCase  :      str			,     **UpperCAmelCase  :      Tuple	)			->	int:
     lowerCamelCase__    :      str					 =		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:
         lowerCamelCase__    :      Optional[int]					 =		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









import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


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

__lowerCAmelCase	: Optional[Any]  				={'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__lowerCAmelCase	: List[str]  				={
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

__lowerCAmelCase	: Optional[int]  				={
    'gpt-neox-20b': 2_0_4_8,
}
class        _lowercase      (			A__       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			str						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Any						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		['''input_ids''', '''attention_mask''']


				def __init__(       self			:int			,			lowerCAmelCase__			:Any=None			,			lowerCAmelCase__			:Optional[Any]=None			,			lowerCAmelCase__			:List[Any]=None			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:str="<|endoftext|>"			,			lowerCAmelCase__			:Dict="<|endoftext|>"			,			lowerCAmelCase__			:Union[str, Any]=False			,			**lowerCAmelCase__			:List[str]			,			)		->			Any:
										super().__init__(
										    lowerCAmelCase__			,			lowerCAmelCase__			,			tokenizer_file=lowerCAmelCase__			,			unk_token=lowerCAmelCase__			,			bos_token=lowerCAmelCase__			,			eos_token=lowerCAmelCase__			,			add_prefix_space=lowerCAmelCase__			,			**lowerCAmelCase__			,			)

										__SCREAMING_SNAKE_CASE						:     List[Any]					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
										if pre_tok_state.get('''add_prefix_space'''			,			lowerCAmelCase__	) != add_prefix_space:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		getattr(lowerCAmelCase__			,			pre_tok_state.pop('''type'''	)	)
																__SCREAMING_SNAKE_CASE						:     str					=		add_prefix_space
																__SCREAMING_SNAKE_CASE						:     Any					=		pre_tok_class(**lowerCAmelCase__	)

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		add_prefix_space


				def 		__magic_name__(       self			:Union[str, Any]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple[str]:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self._tokenizer.model.save(lowerCAmelCase__			,			name=lowerCAmelCase__	)
										return tuple(lowerCAmelCase__	)



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:"Conversation"	)		->			List[int]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		[]
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(lowerCAmelCase__			,			add_special_tokens=lowerCAmelCase__	) + [self.eos_token_id]	)

										if len(lowerCAmelCase__	) > self.model_max_length:
																__SCREAMING_SNAKE_CASE						:     List[str]					=		input_ids[-self.model_max_length :]
										return input_ids
