



'''simple docstring'''






import os
from typing import List, Optional, Union

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


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

UpperCamelCase__:     Tuple										=    {"vocab_file": "vocab.txt"}

UpperCamelCase__:     Dict										=    {
    "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",
    },
}

UpperCamelCase__:     List[Any]										=    {
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}


def 				snake_case_  (      _lowerCAmelCase						:		Tuple						)   ->     str:
							with open(_lowerCAmelCase     ,   '''r'''						) as f:
														UpperCAmelCase				: Optional[int]											=	f.read().splitlines()
														return [l.strip() for l in lines]




class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= VOCAB_FILES_NAMES
	lowerCamelCase__    				= PRETRAINED_VOCAB_FILES_MAP
	lowerCamelCase__    				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCamelCase__    				= ["""input_ids""", """attention_mask"""]







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







	def   A			(		self    :  Dict	,    __snake_case    :  int	)    ->	str:
								return self._id_to_token.get(__snake_case	,    self.unk_token	)







	def   A			(		self    :  Any	,    __snake_case    :  str	)    ->	int:
								return self._token_to_id.get(__snake_case	,    self._token_to_id.get(self.unk_token	)	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  Tuple	,    **__snake_case    :  List[str]	)    ->	List[Any]:
								return text.split()







	def   A			(		self    :  List[str]	,    __snake_case    :  Any=False	)    ->	Dict:
								return len(self._id_to_token	)







	def   A			(		self    :  int	)    ->	Dict:
								return {token: i for i, token in enumerate(self.all_tokens	)}







	def   A			(		self    :  str	,    __snake_case    :  str	)    ->	int:
								return self._token_to_id.get(__snake_case	,    self._token_to_id.get(self.unk_token	)	)







	def   A			(		self    :  Optional[Any]	,    __snake_case    :  int	)    ->	str:
								return self._id_to_token.get(__snake_case	,    self.unk_token	)







	def   A			(		self    :  Union[str, Any]	,    __snake_case    :  List[int]	,    __snake_case    :  Optional[List[int]] = None	)    ->	List[int]:
								UpperCAmelCase				: Any											=	[self.cls_token_id]
								UpperCAmelCase				: str											=	[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]	,    __snake_case    :  List	,    __snake_case    :  Optional[List] = None	,    __snake_case    :  bool = False	)    ->	List[int]:
								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]
								UpperCAmelCase				: Dict											=	[1] + ([0] * len(__snake_case	)) + [1]
								if token_ids_a is not None:
															mask += [0] * len(__snake_case	) + [1]
								return mask







	def   A			(		self    :  List[str]	,    __snake_case    :  List[Any]	,    __snake_case    :  Dict	)    ->	List[str]:
								UpperCAmelCase				: Any											=	os.path.join(__snake_case	,    (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''	)
								with open(__snake_case	,    '''w'''	) as f:
															f.write('''\n'''.join(self.all_tokens	)	)
								return (vocab_file,)







	@property
	def   A			(		self    :  Optional[Any]	)    ->	int:
								return self.get_vocab_size(with_added_tokens=__snake_case	)







	def   A			(		self    :  List[Any]	,    __snake_case    :  Union[List[str], List[AddedToken]]	,    __snake_case    :  bool = False	)    ->	int:
								return super()._add_tokens(__snake_case	,    special_tokens=__snake_case	)










'''simple docstring'''






from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate

@dataclass(frozen=A__    )
class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	lowerCamelCase__    				= field(default="""language-modeling"""							,	metadata={"""include_in_asdict_even_if_is_default""": True}    )

	lowerCamelCase__    				= Features({"""text""": Value("""string"""    )}    )
	lowerCamelCase__    				= Features({}    )
	lowerCamelCase__    				= "text"







	@property
	def   A			(		self    :  Optional[Any]	)    ->	Dict[str, str]:
								return {self.text_column: "text"}





