


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


_A		       =						logging.get_logger(__name__)

_A		       =						'▁'

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

_A		       =						{
    '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',
    },
}

_A		       =						{'vinai/bartpho-syllable': 1024}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Any      =  VOCAB_FILES_NAMES
    UpperCAmelCase__						:		int      =  PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__						:		Tuple      =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__						:		str      =  ["input_ids", "attention_mask"]






    def __init__(			self					,   A_					,   A_					,   A_="<s>"					,   A_="</s>"					,   A_="</s>"					,   A_="<s>"					,   A_="<unk>"					,   A_="<pad>"					,   A_="<mask>"					,   A_ = None					,   **A_					,   )  ->       None:
         # Mask token behave like a normal word, i.e. include the space before it
         __UpperCamelCase    							=AddedToken(A_					,   lstrip=A_					,   rstrip=A_    ) if isinstance(A_					,   A_    ) else mask_token

         __UpperCamelCase    							={} 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_					,   )

         __UpperCamelCase    							=vocab_file
         __UpperCamelCase    							=monolingual_vocab_file
         __UpperCamelCase    							=spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(A_    )    )

         # Load the reduced vocab

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

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






    def __getstate__(			self    )  ->       Any:
         __UpperCamelCase    							=self.__dict__.copy()
         __UpperCamelCase    							=None
         __UpperCamelCase    							=self.sp_model.serialized_model_proto()
         return state






    def __setstate__(			self					,   A_    )  ->       List[str]:
         __UpperCamelCase    							=d

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

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






    def _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:

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






    def _a					(			self					,   A_					,   A_ = None					,   A_ = False    )  ->       List[int]:

         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 _a					(			self					,   A_					,   A_ = None    )  ->       List[int]:
         __UpperCamelCase    							=[self.sep_token_id]
         __UpperCamelCase    							=[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    )  ->       Any:
         return len(self.fairseq_ids_to_tokens    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							={self.convert_ids_to_tokens(A_    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab






    def _a					(			self					,   A_    )  ->       List[str]:
         return self.sp_model.encode(A_					,   out_type=A_    )






    def _a					(			self					,   A_    )  ->       str:
         if token in self.fairseq_tokens_to_ids:
              return self.fairseq_tokens_to_ids[token]
         else:
              return self.unk_token_id






    def _a					(			self					,   A_    )  ->       int:
         return self.fairseq_ids_to_tokens[index]






    def _a					(			self					,   A_    )  ->       List[Any]:
         __UpperCamelCase    							=''.join(A_    ).replace(A_					,   ' '    ).strip()
         return out_string






    def _a					(			self					,   A_					,   A_ = None    )  ->       Tuple[str]:
         if not os.path.isdir(A_    ):
              logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
              return
         __UpperCamelCase    							=os.path.join(
             A_					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=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:
                   __UpperCamelCase    							=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









import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





