
'''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 		__UpperCAmelCase   (      _lowerCamelCase		):


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = False					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(features=lowerCAmelCase_					,     cache_dir=lowerCAmelCase_					,     keep_in_memory=lowerCAmelCase_					,     **lowerCAmelCase_						)
								_snake_case     		=	Sql(
								    cache_dir=lowerCAmelCase_					,     features=lowerCAmelCase_					,     sql=lowerCAmelCase_					,     con=lowerCAmelCase_					,     **lowerCAmelCase_					,     )




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	None
								_snake_case     		=	None
								_snake_case     		=	None
								_snake_case     		=	None

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

								# Build dataset for splits
								_snake_case     		=	self.builder.as_dataset(
								    split='train'					,     verification_mode=lowerCAmelCase_					,     in_memory=self.keep_in_memory						)
								return dataset


class 		__UpperCAmelCase   :


							def __init__( self					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_ = None					,     lowerCAmelCase_ = None					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								if num_proc is not None and num_proc <= 0:
									raise ValueError(F'num_proc {num_proc} must be an integer > 0.'						)

								_snake_case     		=	dataset
								_snake_case     		=	name
								_snake_case     		=	con
								_snake_case     		=	batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
								_snake_case     		=	num_proc
								_snake_case     		=	to_sql_kwargs


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.to_sql_kwargs.pop('sql'					,     lowerCAmelCase_						)
								_snake_case     		=	self.to_sql_kwargs.pop('con'					,     lowerCAmelCase_						)
								_snake_case     		=	self.to_sql_kwargs.pop('index'					,     lowerCAmelCase_						)

								_snake_case     		=	self._write(index=lowerCAmelCase_					,     **self.to_sql_kwargs						)
								return written


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case		,				_snake_case		,				_snake_case     		=	args
								_snake_case     		=	{**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
								_snake_case     		=	query_table(
								    table=self.dataset.data					,     key=slice(lowerCAmelCase_					,     offset + self.batch_size						)					,     indices=self.dataset._indices					,     )
								_snake_case     		=	batch.to_pandas()
								_snake_case     		=	df.to_sql(self.name					,     self.con					,     index=lowerCAmelCase_					,     **lowerCAmelCase_						)
								return num_rows or len(lowerCAmelCase_						)




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	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:
									_snake_case		,				_snake_case     		=	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



'''simple docstring'''







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowercase					:							Optional[Any]   =      logging.get_logger(__name__)

lowercase					:							str   =      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase					:							Optional[Any]   =      {
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

lowercase					:							List[Any]   =      {
    "facebook/blenderbot_small-90M": 512,
}


class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= VOCAB_FILES_NAMES
							__lowercase   	= PRETRAINED_VOCAB_FILES_MAP
							__lowercase   	= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase   	= BlenderbotSmallTokenizer


							def __init__( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_="<|endoftext|>"					,     lowerCAmelCase_=False					,     lowerCAmelCase_=True					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(
								    ByteLevelBPETokenizer(
								        vocab=lowerCAmelCase_					,     merges=lowerCAmelCase_					,     add_prefix_space=lowerCAmelCase_					,     trim_offsets=lowerCAmelCase_					,     )					,     bos_token=lowerCAmelCase_					,     eos_token=lowerCAmelCase_					,     unk_token=lowerCAmelCase_					,     **lowerCAmelCase_					,     )
								_snake_case     		=	add_prefix_space


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								_snake_case     		=	[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
									return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_ = None						):





								"""simple docstring"""






								_snake_case     		=	[self.sep_token_id]
								_snake_case     		=	[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]

