






"""simple docstring"""





from dataclasses import asdict, dataclass
from typing import Optional

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


UpperCamelCase_	:				int										= logging.get_logger(__name__)

# TODO Update this
UpperCamelCase_	:				Optional[int]										= {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}


class      _lowercase (     lowerCAmelCase						):
      _a   :     List[str]								=   '''esm'''


      def __init__(    self				:	Optional[int]							,   a				:	int=None							,   a				:	List[str]=None							,   a				:	str=None							,   a				:	Union[str, Any]=7_6_8							,   a				:	Dict=1_2							,   a				:	Union[str, Any]=1_2							,   a				:	List[str]=3_0_7_2							,   a				:	Optional[Any]=0.1							,   a				:	int=0.1							,   a				:	str=1_0_2_6							,   a				:	Any=0.0_2							,   a				:	Optional[int]=1e-12							,   a				:	Union[str, Any]="absolute"							,   a				:	List[str]=True							,   a				:	List[str]=None							,   a				:	Any=False							,   a				:	Union[str, Any]=False							,   a				:	Optional[int]=None							,   a				:	Any=None							,   **a				:	str							,   ):





            """simple docstring"""







            super().__init__(pad_token_id=a							,   mask_token_id=a							,   **a						)

            __snake_case      :    Any  			=vocab_size
            __snake_case      :    List[Any]  			=hidden_size
            __snake_case      :    Any  			=num_hidden_layers
            __snake_case      :    Any  			=num_attention_heads
            __snake_case      :    Union[str, Any]  			=intermediate_size
            __snake_case      :    Any  			=hidden_dropout_prob
            __snake_case      :    Union[str, Any]  			=attention_probs_dropout_prob
            __snake_case      :    Dict  			=max_position_embeddings
            __snake_case      :    Union[str, Any]  			=initializer_range
            __snake_case      :    Optional[Any]  			=layer_norm_eps
            __snake_case      :    List[Any]  			=position_embedding_type
            __snake_case      :    Any  			=use_cache
            __snake_case      :    Optional[int]  			=emb_layer_norm_before
            __snake_case      :    Any  			=token_dropout
            __snake_case      :    Tuple  			=is_folding_model
            if is_folding_model:
                  if esmfold_config is None:
                        logger.info('''No esmfold_config supplied for folding model, using default values.'''						)
                        __snake_case      :    int  			=EsmFoldConfig()
                  elif isinstance(a							,   a						):
                        __snake_case      :    Union[str, Any]  			=EsmFoldConfig(**a						)
                  __snake_case      :    Union[str, Any]  			=esmfold_config
                  if vocab_list is None:
                        logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''						)
                        __snake_case      :    List[Any]  			=get_default_vocab_list()
                  else:
                        __snake_case      :    str  			=vocab_list
            else:
                  __snake_case      :    Optional[Any]  			=None
                  __snake_case      :    List[str]  			=None
            if self.esmfold_config is not None and getattr(self.esmfold_config							,   '''use_esm_attn_map'''							,   a						):
                  raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''						)







      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=super().to_dict()
            if isinstance(self.esmfold_config							,   a						):
                  __snake_case      :    Tuple  			=self.esmfold_config.to_dict()
            return output



@dataclass
class      _lowercase :
      _a   :     str								=   None
      _a   :     bool								=   True
      _a   :     bool								=   False
      _a   :     bool								=   False
      _a   :     bool								=   False
      _a   :     float								=   0

      _a   :     bool								=   True
      _a   :     bool								=   False

      _a   :     int								=   128
      _a   :     "TrunkConfig"								=   None


      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            if self.trunk is None:
                  __snake_case      :    int  			=TrunkConfig()
            elif isinstance(self.trunk							,   a						):
                  __snake_case      :    int  			=TrunkConfig(**self.trunk						)







      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    List[str]  			=asdict(self						)
            __snake_case      :    Union[str, Any]  			=self.trunk.to_dict()
            return output



@dataclass
class      _lowercase :
      _a   :     int								=   48
      _a   :     int								=   1_024
      _a   :     int								=   128
      _a   :     int								=   32
      _a   :     int								=   32
      _a   :     int								=   32
      _a   :     float								=   0
      _a   :     float								=   0
      _a   :     bool								=   False
      _a   :     int								=   4
      _a   :     Optional[int]								=   128
      _a   :     "StructureModuleConfig"								=   None


      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            if self.structure_module is None:
                  __snake_case      :    Optional[Any]  			=StructureModuleConfig()
            elif isinstance(self.structure_module							,   a						):
                  __snake_case      :    Dict  			=StructureModuleConfig(**self.structure_module						)

            if self.max_recycles <= 0:
                  raise ValueError(f'''`max_recycles` should be positive, got {self.max_recycles}.'''						)
            if self.sequence_state_dim % self.sequence_state_dim != 0:
                  raise ValueError(
                      '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
                      f''' {self.sequence_state_dim} and {self.sequence_state_dim}.'''						)
            if self.pairwise_state_dim % self.pairwise_state_dim != 0:
                  raise ValueError(
                      '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
                      f''' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'''						)

            __snake_case      :    List[str]  			=self.sequence_state_dim // self.sequence_head_width
            __snake_case      :    List[str]  			=self.pairwise_state_dim // self.pairwise_head_width

            if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
                  raise ValueError(
                      '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
                      f''' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'''						)
            if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
                  raise ValueError(
                      '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
                      f''' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'''						)
            if self.pairwise_state_dim % 2 != 0:
                  raise ValueError(f'''`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'''						)

            if self.dropout >= 0.4:
                  raise ValueError(f'''`dropout` should not be greater than 0.4, got {self.dropout}.'''						)







      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            __snake_case      :    str  			=asdict(self						)
            __snake_case      :    Dict  			=self.structure_module.to_dict()
            return output



@dataclass
class      _lowercase :
      _a   :     int								=   384
      _a   :     int								=   128
      _a   :     int								=   16
      _a   :     int								=   128
      _a   :     int								=   12
      _a   :     int								=   4
      _a   :     int								=   8
      _a   :     float								=   0.1
      _a   :     int								=   8
      _a   :     int								=   1
      _a   :     int								=   2
      _a   :     int								=   7
      _a   :     int								=   10
      _a   :     float								=   1e-8
      _a   :     float								=   1e5


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







            return asdict(self						)



def     __lowercase					(    )					->					int:
      return (
          "<cls>",
          "<pad>",
          "<eos>",
          "<unk>",
          "L",
          "A",
          "G",
          "V",
          "S",
          "E",
          "R",
          "T",
          "I",
          "D",
          "P",
          "K",
          "Q",
          "N",
          "F",
          "Y",
          "M",
          "H",
          "W",
          "C",
          "X",
          "B",
          "U",
          "Z",
          "O",
          ".",
          "-",
          "<null_1>",
          "<mask>",
      )










"""simple docstring"""





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


UpperCamelCase_	:				Optional[Any]										= logging.get_logger(__name__)

UpperCamelCase_	:				Union[str, Any]										= {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase_	:				Optional[Any]										= {
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

UpperCamelCase_	:				int										= {
    """moussaKam/mbarthez""": 1024,
    """moussaKam/barthez""": 1024,
    """moussaKam/barthez-orangesum-title""": 1024,
}

UpperCamelCase_	:				Optional[int]										= """▁"""


class      _lowercase (     lowerCAmelCase						):
      _a   :     int								=   VOCAB_FILES_NAMES
      _a   :     int								=   PRETRAINED_VOCAB_FILES_MAP
      _a   :     Union[str, Any]								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a   :     Optional[int]								=   ['''input_ids''', '''attention_mask''']


      def __init__(    self				:	str							,   a				:	Optional[Any]							,   a				:	Optional[Any]="<s>"							,   a				:	Dict="</s>"							,   a				:	Any="</s>"							,   a				:	Optional[int]="<s>"							,   a				:	Optional[Any]="<unk>"							,   a				:	int="<pad>"							,   a				:	Tuple="<mask>"							,   a				:	Optional[Dict[str, Any]] = None							,   **a				:	List[Any]							,   ):





            """simple docstring"""







            __snake_case      :    Dict  			=AddedToken(a							,   lstrip=a							,   rstrip=a						) if isinstance(a							,   a						) else mask_token

            __snake_case      :    List[str]  			={} 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							,   )

            __snake_case      :    Optional[int]  			=vocab_file
            __snake_case      :    Dict  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(str(a						)						)

            __snake_case      :    Optional[Any]  			={'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

            __snake_case      :    Optional[int]  			=len(self.sp_model						) - 1
            __snake_case      :    Union[str, Any]  			={v: k for k, v in self.fairseq_tokens_to_ids.items()}


      def _UpperCamelCase						(    self				:	Tuple							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







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


      def _UpperCamelCase						(    self				:	str							,   a				:	List[int]							,   a				:	Optional[List[int]] = None							,   a				:	bool = False						):





            """simple docstring"""







            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				:	Dict							,   a				:	List[int]							,   a				:	Optional[List[int]] = None						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			=[self.sep_token_id]
            __snake_case      :    int  			=[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[Any]						):





            """simple docstring"""







            return len(self.sp_model						)


      def _UpperCamelCase						(    self				:	Union[str, Any]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			={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				:	str							,   a				:	str						):





            """simple docstring"""







            return self.sp_model.encode(a							,   out_type=a						)


      def _UpperCamelCase						(    self				:	int							,   a				:	Union[str, Any]						):





            """simple docstring"""







            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            __snake_case      :    List[Any]  			=self.sp_model.PieceToId(a						)

            return spm_id if spm_id else self.unk_token_id


      def _UpperCamelCase						(    self				:	List[str]							,   a				:	Dict						):





            """simple docstring"""







            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(a						)


      def _UpperCamelCase						(    self				:	Optional[int]							,   a				:	int						):





            """simple docstring"""







            __snake_case      :    int  			=[]
            __snake_case      :    Optional[int]  			=''''''
            __snake_case      :    Optional[int]  			=False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special:
                              out_string += " "
                        out_string += self.sp_model.decode(a						) + token
                        __snake_case      :    str  			=True
                        __snake_case      :    int  			=[]
                  else:
                        current_sub_tokens.append(a						)
                        __snake_case      :    Tuple  			=False
            out_string += self.sp_model.decode(a						)
            return out_string.strip()


      def __getstate__(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    List[Any]  			=self.__dict__.copy()
            __snake_case      :    Optional[Any]  			=None
            return state


      def __setstate__(    self				:	Optional[int]							,   a				:	Optional[Any]						):





            """simple docstring"""







            __snake_case      :    Any  			=d

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

            __snake_case      :    int  			=spm.SentencePieceProcessor(**self.sp_model_kwargs						)
            self.sp_model.Load(self.vocab_file						)







      def _UpperCamelCase						(    self				:	Optional[Any]							,   a				:	str							,   a				:	Optional[str] = None						):





            """simple docstring"""







            if not os.path.isdir(a						):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                  return
            __snake_case      :    int  			=os.path.join(
                a							,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''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:
                        __snake_case      :    List[Any]  			=self.sp_model.serialized_model_proto()
                        fi.write(a						)

            return (out_vocab_file,)


