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


lowercase_     :   Tuple 				=				logging.get_logger(__name__)

lowercase_     :   int 				=				"▁"

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

lowercase_     :   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",
    },
}

lowercase_     :   Any 				=				{"vinai/bartpho-syllable": 1_0_2_4}






class 		_lowerCamelCase		(   __lowerCAmelCase    ):
				__a  =      VOCAB_FILES_NAMES
				__a  =      PRETRAINED_VOCAB_FILES_MAP
				__a  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a  =      ["input_ids", "attention_mask"]
				def __init__(  self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase="<s>" , lowerCAmelCase="</s>" , lowerCAmelCase="</s>" , lowerCAmelCase="<s>" , lowerCAmelCase="<unk>" , lowerCAmelCase="<pad>" , lowerCAmelCase="<mask>" , lowerCAmelCase = None , **lowerCAmelCase , )       ->					None:
					SCREAMING_SNAKE_CASE__:   List[Any]=      AddedToken(_UpperCamelCase , lstrip=_UpperCamelCase , rstrip=_UpperCamelCase   ) if isinstance(_UpperCamelCase , _UpperCamelCase   ) else mask_token

					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      {} 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 , )

					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      vocab_file
					SCREAMING_SNAKE_CASE__:   Optional[int]=      monolingual_vocab_file
					SCREAMING_SNAKE_CASE__:   Optional[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
					SCREAMING_SNAKE_CASE__:   str=      {}
					SCREAMING_SNAKE_CASE__:   str=      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:
							SCREAMING_SNAKE_CASE__:   Union[str, Any]=      cnt
							cnt += 1
					with open(_UpperCamelCase , '''r''' , encoding='''utf-8'''   ) as f:
						for line in f.readlines():
							SCREAMING_SNAKE_CASE__:   Union[str, Any]=      line.strip().split()[0]
							SCREAMING_SNAKE_CASE__:   Union[str, Any]=      len(self.fairseq_tokens_to_ids   )
					if str(_UpperCamelCase   ) not in self.fairseq_tokens_to_ids:
						SCREAMING_SNAKE_CASE__:   List[str]=      len(self.fairseq_tokens_to_ids   )

					SCREAMING_SNAKE_CASE__:   List[str]=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
				def __getstate__(  self   )       ->					Tuple:
					SCREAMING_SNAKE_CASE__:   int=      self.__dict__.copy()
					SCREAMING_SNAKE_CASE__:   Any=      None
					SCREAMING_SNAKE_CASE__:   str=      self.sp_model.serialized_model_proto()
					return state
				def __setstate__(  self , lowerCAmelCase   )       ->					Union[str, Any]:
					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      d

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

					SCREAMING_SNAKE_CASE__:   Union[str, Any]=      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
					self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )
				def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					List[int]:


					if token_ids_a is None:
						return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					SCREAMING_SNAKE_CASE__:   Dict=      [self.cls_token_id]
					SCREAMING_SNAKE_CASE__:   List[Any]=      [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
				def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = False   )       ->					List[int]:


					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 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					List[int]:
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      [self.sep_token_id]
					SCREAMING_SNAKE_CASE__:   List[Any]=      [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   )       ->					Tuple:
					return len(self.fairseq_ids_to_tokens   )
				def 						UpperCamelCase_				(  self   )       ->					int:
					SCREAMING_SNAKE_CASE__:   Optional[Any]=      {self.convert_ids_to_tokens(_UpperCamelCase   ): i for i in range(self.vocab_size   )}
					vocab.update(self.added_tokens_encoder   )
					return vocab
				def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
					return self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase   )
				def 						UpperCamelCase_				(  self , 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 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
					return self.fairseq_ids_to_tokens[index]
				def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Dict:
					SCREAMING_SNAKE_CASE__:   Dict=      """""".join(_UpperCamelCase   ).replace(_UpperCamelCase , ''' '''   ).strip()
					return out_string



				def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					Tuple[str]:


					if not os.path.isdir(_UpperCamelCase   ):
						logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
						return
					SCREAMING_SNAKE_CASE__:   Dict=      os.path.join(
					    _UpperCamelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
					SCREAMING_SNAKE_CASE__:   str=      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:
							SCREAMING_SNAKE_CASE__:   int=      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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase_     :   List[Any] 				=				logging.get_logger(__name__)

lowercase_     :   List[Any] 				=				{
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      "xlm-roberta-xl"
    def __init__(  self , lowerCAmelCase=250880 , lowerCAmelCase=2560 , lowerCAmelCase=36 , lowerCAmelCase=32 , lowerCAmelCase=10240 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=514 , lowerCAmelCase=1 , lowerCAmelCase=0.02 , lowerCAmelCase=1e-05 , lowerCAmelCase=1 , lowerCAmelCase=0 , lowerCAmelCase=2 , lowerCAmelCase="absolute" , lowerCAmelCase=True , lowerCAmelCase=None , **lowerCAmelCase , )       ->					Union[str, Any]:
     super().__init__(pad_token_id=lowerCAmelCase , bos_token_id=lowerCAmelCase , eos_token_id=lowerCAmelCase , **lowerCAmelCase   )
     SCREAMING_SNAKE_CASE__:   List[Any]=      vocab_size
     SCREAMING_SNAKE_CASE__:   Dict=      hidden_size
     SCREAMING_SNAKE_CASE__:   Tuple=      num_hidden_layers
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      num_attention_heads
     SCREAMING_SNAKE_CASE__:   int=      hidden_act
     SCREAMING_SNAKE_CASE__:   Any=      intermediate_size
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      hidden_dropout_prob
     SCREAMING_SNAKE_CASE__:   str=      attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__:   List[Any]=      max_position_embeddings
     SCREAMING_SNAKE_CASE__:   Dict=      type_vocab_size
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      initializer_range
     SCREAMING_SNAKE_CASE__:   Dict=      layer_norm_eps
     SCREAMING_SNAKE_CASE__:   Dict=      position_embedding_type
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      use_cache
     SCREAMING_SNAKE_CASE__:   str=      classifier_dropout






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    @property
    def 						UpperCamelCase_				(  self   )       ->					Mapping[str, Mapping[int, str]]:
     if self.task == "multiple-choice":
      SCREAMING_SNAKE_CASE__:   Dict=      {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
      SCREAMING_SNAKE_CASE__:   List[str]=      {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]   )

