




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 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						="▁"

__SCREAMING_SNAKE_CASE 						={"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

__SCREAMING_SNAKE_CASE 						={
    "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 						={"vinai/bartpho-syllable": 1_024}







class  __magic_name__						(  _UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Dict	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					["input_ids", "attention_mask"]


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

											SCREAMING_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_		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    monolingual_vocab_file
											SCREAMING_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
											SCREAMING_SNAKE_CASE_   	=    {}
											SCREAMING_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:
																									SCREAMING_SNAKE_CASE_   	=    cnt
																									cnt += 1
											with open(lowercase_		,					'''r'''		,					encoding='''utf-8'''		) as f:
																		for line in f.readlines():
																									SCREAMING_SNAKE_CASE_   	=    line.strip().split()[0]
																									SCREAMING_SNAKE_CASE_   	=    len(self.fairseq_tokens_to_ids		)
											if str(lowercase_		) not in self.fairseq_tokens_to_ids:
																		SCREAMING_SNAKE_CASE_   	=    len(self.fairseq_tokens_to_ids		)

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


				def __getstate__(							self:   str		):
											SCREAMING_SNAKE_CASE_   	=    self.__dict__.copy()
											SCREAMING_SNAKE_CASE_   	=    None
											SCREAMING_SNAKE_CASE_   	=    self.sp_model.serialized_model_proto()
											return state


				def __setstate__(							self:   Optional[int]		,					_lowerCamelCase:   List[str]		):
											SCREAMING_SNAKE_CASE_   	=    d

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

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


				def     _A							(							self:   Any		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):

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


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		,					_lowerCamelCase:   bool = False		):

											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     _A							(							self:   Optional[int]		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):
											SCREAMING_SNAKE_CASE_   	=    [self.sep_token_id]
											SCREAMING_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     _A							(							self:   List[str]		):
											return len(self.fairseq_ids_to_tokens		)


				def     _A							(							self:   List[Any]		):
											SCREAMING_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     _A							(							self:   Tuple		,					_lowerCamelCase:   str		):
											return self.sp_model.encode(lowercase_		,					out_type=lowercase_		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   List[Any]		):
											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											else:
																		return self.unk_token_id


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   str		):
											return self.fairseq_ids_to_tokens[index]


				def     _A							(							self:   List[Any]		,					_lowerCamelCase:   Tuple		):
											SCREAMING_SNAKE_CASE_   	=    """""".join(lowercase_		).replace(lowercase_		,					''' '''		).strip()
											return out_string





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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__SCREAMING_SNAKE_CASE 						={
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__SCREAMING_SNAKE_CASE 						=[
			    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """XLMRobertaXLForCausalLM""",
			    """XLMRobertaXLForMaskedLM""",
			    """XLMRobertaXLForMultipleChoice""",
			    """XLMRobertaXLForQuestionAnswering""",
			    """XLMRobertaXLForSequenceClassification""",
			    """XLMRobertaXLForTokenClassification""",
			    """XLMRobertaXLModel""",
			    """XLMRobertaXLPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_xlm_roberta_xl import (
			    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    XLMRobertaXLConfig,
			    XLMRobertaXLOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlm_roberta_xl import (
						    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLMRobertaXLForCausalLM,
						    XLMRobertaXLForMaskedLM,
						    XLMRobertaXLForMultipleChoice,
						    XLMRobertaXLForQuestionAnswering,
						    XLMRobertaXLForSequenceClassification,
						    XLMRobertaXLForTokenClassification,
						    XLMRobertaXLModel,
						    XLMRobertaXLPreTrainedModel,
						)

else:
			import sys

			__SCREAMING_SNAKE_CASE 						=_LazyModule(__name__, globals()["""__file__"""], _import_structure)


