




"""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


a							      =     logging.get_logger(__name__)

a							      =     {'''vocab_file''': '''vocab.txt'''}

a							      =     {
    '''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''',
    },
}

a							      =     {
    '''facebook/esm2_t6_8M_UR50D''': 1_024,
    '''facebook/esm2_t12_35M_UR50D''': 1_024,
}






def 		_snake_case	(   _snake_case				:   Optional[int]				)       ->   Union[str, Any]:






      '''simple docstring'''


      with open(_snake_case							,    'r'				) as f:
            _A     =  f.read().splitlines()
            return [l.strip() for l in lines]







class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      VOCAB_FILES_NAMES
 UpperCAmelCase       :	Tuple     =      PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase       :	str     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase       :	int     =      ['''input_ids''', '''attention_mask''']

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

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int						):
       return self._id_to_token.get(_UpperCAmelCase       ,				self.unk_token						)

 def      lowerCAmelCase_				(					self			:			List[Any]       ,				_UpperCAmelCase			:			str						):
       return self._token_to_id.get(_UpperCAmelCase       ,				self._token_to_id.get(self.unk_token						)						)

 def      lowerCAmelCase_				(					self			:			Optional[int]       ,				_UpperCAmelCase			:			int       ,				**_UpperCAmelCase			:			List[str]						):
       return text.split()

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			List[str]=False						):
       return len(self._id_to_token						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       return {token: i for i, token in enumerate(self.all_tokens						)}

 def      lowerCAmelCase_				(					self			:			Optional[Any]       ,				_UpperCAmelCase			:			str						):
       return self._token_to_id.get(_UpperCAmelCase       ,				self._token_to_id.get(self.unk_token						)						)

 def      lowerCAmelCase_				(					self			:			Any       ,				_UpperCAmelCase			:			int						):
       return self._id_to_token.get(_UpperCAmelCase       ,				self.unk_token						)

 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			List[int]       ,				_UpperCAmelCase			:			Optional[List[int]] = None						):
       _A     =  [self.cls_token_id]
       _A     =  [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      lowerCAmelCase_				(					self			:			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]
       _A     =  [1] + ([0] * len(_UpperCAmelCase						)) + [1]
       if token_ids_a is not None:
             mask += [0] * len(_UpperCAmelCase						) + [1]
       return mask

 def      lowerCAmelCase_				(					self			:			Tuple       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Any						):
       _A     =  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      lowerCAmelCase_				(					self			:			int						):
       return self.get_vocab_size(with_added_tokens=_UpperCAmelCase						)



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





'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d