





from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

A       :   int					= _symbol_database.Default()


A       :   Union[str, Any]					= _descriptor_pool.Default().AddSerializedFile(
    b'''\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'''
)

A       :   List[Any]					= globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, '''sentencepiece_model_pb2''', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					A       :   Any					= None
					A       :   Union[str, Any]					= b'''H\003'''
					# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
					A       :   str					= 4_5
					A       :   str					= 1_5_8_1
					A       :   Optional[int]					= 1_5_1_7
					A       :   Any					= 1_5_7_0
					A       :   Union[str, Any]					= 1_5_8_4
					A       :   List[Any]					= 1_7_9_3
					A       :   Optional[int]					= 1_7_9_5
					A       :   Optional[int]					= 1_9_1_6
					A       :   Tuple					= 1_8_6_4
					A       :   Optional[int]					= 1_9_0_5
					A       :   Any					= 1_9_1_9
					A       :   str					= 2_4_2_9
					A       :   List[Any]					= 2_2_0_8
					A       :   List[str]					= 2_4_1_8
					A       :   Dict					= 2_3_2_3
					A       :   Union[str, Any]					= 2_4_0_7
# @@protoc_insertion_point(module_scope)










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



