

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_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	'▁'

lowerCAmelCase_       	=	{'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

lowerCAmelCase_       	=	{
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

lowerCAmelCase_       	=	{'vinai/bartpho-syllable': 10_24}





class 	snake_case_   (		_lowerCamelCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE     :   Tuple									=				PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE     :   Optional[int]									=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE     :   List[str]									=				["input_ids", "attention_mask"]







			def __init__(     self :				Optional[int]							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				Optional[int]="<s>"							,   _UpperCamelCase :				Dict="</s>"							,   _UpperCamelCase :				str="</s>"							,   _UpperCamelCase :				Union[str, Any]="<s>"							,   _UpperCamelCase :				Optional[int]="<unk>"							,   _UpperCamelCase :				Optional[Any]="<pad>"							,   _UpperCamelCase :				Optional[Any]="<mask>"							,   _UpperCamelCase :				Optional[Dict[str, Any]] = None							,   **_UpperCamelCase :				List[Any]							,   )      ->Optional[Any]:
									# Mask token behave like a normal word, i.e. include the space before it
									snake_case_   =	AddedToken(lowercase_							,   lstrip=lowercase_							,   rstrip=lowercase_							) if isinstance(lowercase_							,   lowercase_							) else mask_token

									snake_case_   =	{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    bos_token=lowercase_							,   eos_token=lowercase_							,   unk_token=lowercase_							,   sep_token=lowercase_							,   cls_token=lowercase_							,   pad_token=lowercase_							,   mask_token=lowercase_							,   sp_model_kwargs=self.sp_model_kwargs							,   **lowercase_							,   )

									snake_case_   =	vocab_file
									snake_case_   =	monolingual_vocab_file
									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.Load(str(lowercase_							)							)

									# Load the reduced vocab

									# Keep order of special tokens for backward compatibility
									snake_case_   =	{}
									snake_case_   =	0
									for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
															if str(lowercase_							) not in self.fairseq_tokens_to_ids:
																					snake_case_   =	cnt
																					cnt += 1
									with open(lowercase_							,   '''r'''							,   encoding='''utf-8'''							) as f:
															for line in f.readlines():
																					snake_case_   =	line.strip().split()[0]
																					snake_case_   =	len(self.fairseq_tokens_to_ids							)
									if str(lowercase_							) not in self.fairseq_tokens_to_ids:
															snake_case_   =	len(self.fairseq_tokens_to_ids							)

									snake_case_   =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def __getstate__(     self :				Tuple							)      ->Dict:
									snake_case_   =	self.__dict__.copy()
									snake_case_   =	None
									snake_case_   =	self.sp_model.serialized_model_proto()
									return state







			def __setstate__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]							)      ->Optional[int]:
									snake_case_   =	d

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

									snake_case_   =	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)







			def 				snake_case__(     self :				Tuple							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->Any:

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







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							,   _UpperCamelCase :				bool = False							)      ->List[str]:

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

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







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[int]							,   _UpperCamelCase :				Optional[List[int]] = None							)      ->Dict:
									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]







			@property
			def 				snake_case__(     self :				Any							)      ->List[str]:
									return len(self.fairseq_ids_to_tokens							)







			def 				snake_case__(     self :				Dict							)      ->Union[str, Any]:
									snake_case_   =	{self.convert_ids_to_tokens(lowercase_							): i for i in range(self.vocab_size							)}
									vocab.update(self.added_tokens_encoder							)
									return vocab







			def 				snake_case__(     self :				Dict							,   _UpperCamelCase :				str							)      ->List[str]:
									return self.sp_model.encode(lowercase_							,   out_type=lowercase_							)







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				List[Any]							)      ->Dict:
									if token in self.fairseq_tokens_to_ids:
															return self.fairseq_tokens_to_ids[token]
									else:
															return self.unk_token_id







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				int							)      ->str:
									return self.fairseq_ids_to_tokens[index]







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				Optional[Any]							)      ->int:
									snake_case_   =	''''''.join(lowercase_							).replace(lowercase_							,   ''' '''							).strip()
									return out_string







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				Optional[str] = None							)      ->Tuple:
									if not os.path.isdir(lowercase_							):
															logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
															return
									snake_case_   =	os.path.join(
									    lowercase_							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
									snake_case_   =	os.path.join(
									    lowercase_							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']							,   )

									if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase_							) and os.path.isfile(self.vocab_file							):
															copyfile(self.vocab_file							,   lowercase_							)
									elif not os.path.isfile(self.vocab_file							):
															with open(lowercase_							,   '''wb'''							) as fi:
																					snake_case_   =	self.sp_model.serialized_model_proto()
																					fi.write(lowercase_							)

									if os.path.abspath(self.monolingual_vocab_file							) != os.path.abspath(
									    lowercase_							) and os.path.isfile(self.monolingual_vocab_file							):
															copyfile(self.monolingual_vocab_file							,   lowercase_							)
									elif not os.path.isfile(self.monolingual_vocab_file							):
															with open(lowercase_							,   '''w'''							,   encoding='''utf-8'''							) as fp:
																					for token in self.fairseq_tokens_to_ids:
																											if token not in self.all_special_tokens:
																																	fp.write(f'''{str(lowercase_							)} \n'''							)

									return out_vocab_file, out_monolingual_vocab_file





'''simple docstring'''

import contextlib
import os
import sqlitea

import pytest

from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					str:

					'''simple docstring'''

					assert isinstance(__UpperCAmelCase,      __UpperCAmelCase	)
					assert dataset.num_rows == 4
					assert dataset.num_columns == 3
					assert dataset.column_names == ["col_1", "col_2", "col_3"]
					for feature, expected_dtype in expected_features.items():
										assert dataset.features[feature].dtype == expected_dtype


@require_sqlalchemy
@pytest.mark.parametrize('''keep_in_memory''',      [False, True]	)
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
										snake_case_									=				SqlDatasetReader(
										    '''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase,      keep_in_memory=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


@require_sqlalchemy
@pytest.mark.parametrize(
    '''features''',      [
        None,
        {'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''},
        {'''col_1''': '''string''', '''col_2''': '''string''', '''col_3''': '''string'''},
        {'''col_1''': '''int32''', '''col_2''': '''int32''', '''col_3''': '''int32'''},
        {'''col_1''': '''float32''', '''col_2''': '''float32''', '''col_3''': '''float32'''},
    ],      )
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				{'''col_1''': '''string''', '''col_2''': '''int64''', '''col_3''': '''float64'''}
					snake_case_									=				features.copy() if features else default_expected_features
					snake_case_									=				(
					    Features({feature: Value(__UpperCAmelCase	) for feature, dtype in features.items()}	) if features is not None else None
					)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      features=__UpperCAmelCase,      cache_dir=__UpperCAmelCase	).read()
					_check_sql_dataset(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					with contextlib.closing(sqlitea.connect(__UpperCAmelCase	)	) as con:
										snake_case_									=				con.cursor()
										cur.execute('''SELECT * FROM dataset'''	)
										for row in cur:
															yield row


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=1	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=2	).write()

					snake_case_									=				iter_sql_file(__UpperCAmelCase	)
					snake_case_									=				iter_sql_file(__UpperCAmelCase	)

					for rowa, rowa in zip(__UpperCAmelCase,      __UpperCAmelCase	):
										assert rowa == rowa


@require_sqlalchemy
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				tmp_path / '''cache'''
					snake_case_									=				os.path.join(__UpperCAmelCase,      '''tmp.sql'''	)
					snake_case_									=				SqlDatasetReader('''dataset''',      '''sqlite:///''' + sqlite_path,      cache_dir=__UpperCAmelCase	).read()
					with pytest.raises(__UpperCAmelCase	):
										SqlDatasetWriter(__UpperCAmelCase,      '''dataset''',      '''sqlite:///''' + output_sqlite_path,      num_proc=0	).write()

