
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


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

SCREAMING_SNAKE_CASE							:Any							 =				'▁'

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

SCREAMING_SNAKE_CASE							:str							 =				{
    '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',
    },
}

SCREAMING_SNAKE_CASE							:List[Any]							 =				{'vinai/bartpho-syllable': 1024}





class 							UpperCAmelCase      (  snake_case_  ):




	'''simple docstring'''





	snake_case_					    =     VOCAB_FILES_NAMES
	snake_case_					    =     PRETRAINED_VOCAB_FILES_MAP
	snake_case_					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_					    =     ["input_ids", "attention_mask"]






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

					__A   			= {} if sp_model_kwargs is None else sp_model_kwargs

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

					__A   			= vocab_file
					__A   			= monolingual_vocab_file
					__A   			= spm.SentencePieceProcessor(**self.sp_model_kwargs )
					self.sp_model.Load(str(A ) )

					# Load the reduced vocab

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

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






	def __getstate__(       self					: Tuple ):
					__A   			= self.__dict__.copy()
					__A   			= None
					__A   			= self.sp_model.serialized_model_proto()
					return state






	def __setstate__(       self					: Dict							,A					: int ):
					__A   			= d

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

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






	def 				UpperCamelCase_  (       self					: int							,A					: List[Any]							,A					: str = None ):

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






	def 				UpperCamelCase_  (       self					: int							,A					: List[Any]							,A					: Any = None							,A					: int = False ):

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

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






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: List[str]							,A					: Union[str, Any] = None ):
					__A   			= [self.sep_token_id]
					__A   			= [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 				UpperCamelCase_  (       self					: List[str] ):
					return len(self.fairseq_ids_to_tokens )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
					vocab.update(self.added_tokens_encoder )
					return vocab






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Tuple ):
					return self.sp_model.encode(A							,out_type=A )






	def 				UpperCamelCase_  (       self					: str							,A					: Any ):
					if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
					else:
									return self.unk_token_id






	def 				UpperCamelCase_  (       self					: str							,A					: List[Any] ):
					return self.fairseq_ids_to_tokens[index]






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: List[Any] ):
					__A   			= "".join(A ).replace(A							," " ).strip()
					return out_string







	def 				UpperCamelCase_  (       self					: List[str]							,A					: List[str]							,A					: Optional[Any] = None ):
					if not os.path.isdir(A ):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
									return
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
					__A   			= os.path.join(
					    A							,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]							,)

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

					if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
					    A ) and os.path.isfile(self.monolingual_vocab_file ):
									copyfile(self.monolingual_vocab_file							,A )
					elif not os.path.isfile(self.monolingual_vocab_file ):
									with open(A							,"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(A )} \n''' )

					return out_vocab_file, out_monolingual_vocab_file







'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Tuple:
        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
        return Dataset.from_dict(A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertListEqual(dset.column_names ,		["""col_1""", """col_2"""]       )
        for i, r in enumerate(A       ):
            self.assertDictEqual(A ,		example_records[i]       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        _SCREAMING_SNAKE_CASE									=			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
        self.assertEqual(dset.info ,		dset_from_dict.info       )


    def 			snake_case_(		self       )		->   Union[str, Any]:  # checks what happens with missing columns
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": 1}, {"""col_2""": """x"""}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertDictEqual(dset[0] ,		{"""col_1""": 1}       )
        self.assertDictEqual(dset[1] ,		{"""col_1""": None}       )  # NB: first record is used for columns


    def 			snake_case_(		self       )		->   Optional[Any]:  # checks if the type can be inferred from the second record
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": []}, {"""col_1""": [1, 2]}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertEqual(dset.info.features["""col_1"""] ,		Sequence(Value("""int64"""       )       )       )





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list([]       )
        self.assertEqual(len(A       ) ,		0       )
        self.assertListEqual(dset.column_names ,		[]       )



