






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{"""vocab_file""": """vocab.txt"""}

snake_case    				=		{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

snake_case    				=		{
    """facebook/esm2_t6_8M_UR50D""": 1_024,
    """facebook/esm2_t12_35M_UR50D""": 1_024,
}


def   lowerCamelCase__			(     lowercase				):

  """simple docstring"""






  with open(lowercase						,     "r"				) as f:
    SCREAMING_SNAKE_CASE       :      Dict							=    f.read().splitlines()
    return [l.strip() for l in lines]


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	VOCAB_FILES_NAMES
     UpperCamelCase_							:		int			=	PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_							:		List[Any]			=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_							:		List[str]			=	['''input_ids''', '''attention_mask''']


     def __init__(				self       :	int					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[str]="<unk>"					, UpperCAmelCase_       :	Optional[int]="<cls>"					, UpperCAmelCase_       :	Tuple="<pad>"					, UpperCAmelCase_       :	List[Any]="<mask>"					, UpperCAmelCase_       :	Optional[int]="<eos>"					, **UpperCAmelCase_       :	int					, ):
       super().__init__(**UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    load_vocab_file(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    dict(enumerate(self.all_tokens      )      )
       SCREAMING_SNAKE_CASE       :      List[str]							=    {tok: ind for ind, tok in enumerate(self.all_tokens      )}
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    unk_token
       SCREAMING_SNAKE_CASE       :      Dict							=    cls_token
       SCREAMING_SNAKE_CASE       :      Dict							=    pad_token
       SCREAMING_SNAKE_CASE       :      Any							=    mask_token
       SCREAMING_SNAKE_CASE       :      Tuple							=    eos_token
       SCREAMING_SNAKE_CASE       :      int							=    self.all_tokens
       self._create_trie(self.unique_no_split_tokens      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	int      ):
       return self._id_to_token.get(UpperCAmelCase_					, self.unk_token      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	str      ):
       return self._token_to_id.get(UpperCAmelCase_					, self._token_to_id.get(self.unk_token      )      )


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Any					, **UpperCAmelCase_       :	Union[str, Any]      ):
       return text.split()


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	Union[str, Any]=False      ):
       return len(self._id_to_token      )


     def 				_A       (				self       :	str      ):
       return {token: i for i, token in enumerate(self.all_tokens      )}


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	str      ):
       return self._token_to_id.get(UpperCAmelCase_					, self._token_to_id.get(self.unk_token      )      )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	int      ):
       return self._id_to_token.get(UpperCAmelCase_					, self.unk_token      )


     def 				_A       (				self       :	List[str]					, UpperCAmelCase_       :	List[int]					, UpperCAmelCase_       :	Optional[List[int]] = None      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    [self.cls_token_id]
       SCREAMING_SNAKE_CASE       :      Dict							=    [self.eos_token_id]  # No sep token in ESM vocabulary
       if token_ids_a is None:
         if self.eos_token_id is None:
           return cls + token_ids_a
         else:
           return cls + token_ids_a + sep
       elif self.eos_token_id is None:
         raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"      )
       return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	List					, UpperCAmelCase_       :	Optional[List] = None					, UpperCAmelCase_       :	bool = False      ):
       if already_has_special_tokens:
         if token_ids_a is not None:
           raise ValueError(
               "You should not supply a second sequence if the provided sequence of "
               "ids is already formatted with special tokens for the model."      )

         return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
       SCREAMING_SNAKE_CASE       :      List[Any]							=    [1] + ([0] * len(UpperCAmelCase_      )) + [1]
       if token_ids_a is not None:
         mask += [0] * len(UpperCAmelCase_      ) + [1]
       return mask


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    os.path.join(UpperCAmelCase_					, (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"      )
       with open(UpperCAmelCase_					, "w"      ) as f:
         f.write("\n".join(self.all_tokens      )      )
       return (vocab_file,)


     @property
     def 				_A       (				self       :	Optional[Any]      ):
       return self.get_vocab_size(with_added_tokens=UpperCAmelCase_      )






     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Union[List[str], List[AddedToken]]					, UpperCAmelCase_       :	bool = False      ):
       return super()._add_tokens(UpperCAmelCase_					, special_tokens=UpperCAmelCase_      )














from collections import OrderedDict
from typing import Mapping

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


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Dict							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache
       SCREAMING_SNAKE_CASE       :      str							=    classifier_dropout





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






