






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


snake_case    :						Tuple  =						logging.get_logger(__name__)

snake_case    :						Tuple  =						"""▁"""

snake_case    :						Optional[Any]  =						{"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}

snake_case    :						List[Any]  =						{
    """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""",
    },
}

snake_case    :						Dict  =						{"""vinai/bartpho-syllable""": 1_0_2_4}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Tuple           =						VOCAB_FILES_NAMES
					__A     :    Optional[Any]           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    Optional[Any]           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    Optional[int]           =						["input_ids", "attention_mask"]







					def __init__(       self      :     Union[str, Any]			,		_A      :     List[str]			,		_A      :     Any			,		_A      :     Dict="<s>"			,		_A      :     Union[str, Any]="</s>"			,		_A      :     List[Any]="</s>"			,		_A      :     Union[str, Any]="<s>"			,		_A      :     Tuple="<unk>"			,		_A      :     Optional[Any]="<pad>"			,		_A      :     List[str]="<mask>"			,		_A      :     Any = None			,		**_A      :     Dict			,		):
											A__	:							Optional[int]						    = AddedToken(UpperCamelCase__			,		lstrip=UpperCamelCase__			,		rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__			,		UpperCamelCase__) else mask_token

											A__	:							List[str]						    = {} if sp_model_kwargs is None else sp_model_kwargs

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

											A__	:							Optional[int]						    = vocab_file
											A__	:							str						    = monolingual_vocab_file
											A__	:							Any						    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.Load(str(UpperCamelCase__))

											# Load the reduced vocab

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

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







					def __getstate__(       self      :     Optional[Any]):
											A__	:							List[Any]						    = self.__dict__.copy()
											A__	:							List[Any]						    = None
											A__	:							Tuple						    = self.sp_model.serialized_model_proto()
											return state







					def __setstate__(       self      :     List[Any]			,		_A      :     Any):
											A__	:							List[str]						    = d

											# for backward compatibility
											if not hasattr(self			,		"sp_model_kwargs"):
																	A__	:							Optional[Any]						    = {}

											A__	:							str						    = spm.SentencePieceProcessor(**self.sp_model_kwargs)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto)







					def  _lowercase			(       self      :     List[str]			,		_A      :     str			,		_A      :     Tuple = None):





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







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Tuple			,		_A      :     Tuple = None			,		_A      :     List[str] = False):





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

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







					def  _lowercase			(       self      :     Dict			,		_A      :     Any			,		_A      :     Optional[int] = None):
											A__	:							str						    = [self.sep_token_id]
											A__	:							Dict						    = [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  _lowercase			(       self      :     List[Any]):
											return len(self.fairseq_ids_to_tokens)







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							Any						    = {self.convert_ids_to_tokens(UpperCamelCase__): i for i in range(self.vocab_size)}
											vocab.update(self.added_tokens_encoder)
											return vocab







					def  _lowercase			(       self      :     List[Any]			,		_A      :     List[str]):
											return self.sp_model.encode(UpperCamelCase__			,		out_type=UpperCamelCase__)







					def  _lowercase			(       self      :     Dict			,		_A      :     List[str]):





											if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
											else:
																	return self.unk_token_id







					def  _lowercase			(       self      :     Any			,		_A      :     List[str]):
											return self.fairseq_ids_to_tokens[index]







					def  _lowercase			(       self      :     int			,		_A      :     Any):
											A__	:							List[Any]						    = "".join(UpperCamelCase__).replace(UpperCamelCase__			,		" ").strip()
											return out_string







					def  _lowercase			(       self      :     str			,		_A      :     Optional[int]			,		_A      :     Dict = None):





											if not os.path.isdir(UpperCamelCase__):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory')
																	return
											A__	:							Dict						    = os.path.join(
											    UpperCamelCase__			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
											A__	:							Any						    = os.path.join(
											    UpperCamelCase__			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]			,		)

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

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

											return out_vocab_file, out_monolingual_vocab_file




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		from transformers import AutoModelForSeqaSeqLM, AutoTokenizer




@require_torch
@require_sentencepiece
@require_tokenizers
class 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)