



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       :	Optional[int]            =logging.get_logger(__name__)

__snake_case       :	Union[str, Any]            ={'vocab_file': 'vocab.txt'}

__snake_case       :	int            ={
    '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       :	Optional[Any]            ={
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Tuple):





				'''simple docstring'''

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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



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



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase="<unk>"	,__lowerCamelCase="<cls>"	,__lowerCamelCase="<pad>"	,__lowerCamelCase="<mask>"	,__lowerCamelCase="<eos>"	,**__lowerCamelCase	,)  ->    Dict:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  load_vocab_file(__lowerCamelCase				)
						lowerCAmelCase__  :       List[Any]				=  dict(enumerate(self.all_tokens				)				)
						lowerCAmelCase__  :       List[str]				=  {tok: ind for ind, tok in enumerate(self.all_tokens				)}
						lowerCAmelCase__  :       int				=  unk_token
						lowerCAmelCase__  :       str				=  cls_token
						lowerCAmelCase__  :       List[Any]				=  pad_token
						lowerCAmelCase__  :       int				=  mask_token
						lowerCAmelCase__  :       str				=  eos_token
						lowerCAmelCase__  :       Optional[int]				=  self.all_tokens
						self._create_trie(self.unique_no_split_tokens				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    str:

						"""simple docstring"""






						return self._id_to_token.get(__lowerCamelCase	,self.unk_token				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						return self._token_to_id.get(__lowerCamelCase	,self._token_to_id.get(self.unk_token				)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,**__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						return text.split()



		def 	lowerCAmelCase__   (self	,__lowerCamelCase=False				)  ->    Optional[int]:

						"""simple docstring"""






						return len(self._id_to_token				)



		def 	lowerCAmelCase__   (self				)  ->    str:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    int:

						"""simple docstring"""






						return self._token_to_id.get(__lowerCamelCase	,self._token_to_id.get(self.unk_token				)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    str:

						"""simple docstring"""






						return self._id_to_token.get(__lowerCamelCase	,self.unk_token				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    List[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  [self.cls_token_id]
						lowerCAmelCase__  :       Tuple				=  [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	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = False				)  ->    List[int]:

						"""simple docstring"""






						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]
						lowerCAmelCase__  :       Tuple				=  [1] + ([0] * len(__lowerCamelCase				)) + [1]
						if token_ids_a is not None:
										mask += [0] * len(__lowerCamelCase				) + [1]
						return mask



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Tuple:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  os.path.join(__lowerCamelCase	,(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt'''				)
						with open(__lowerCamelCase	,'''w'''				) as f:
										f.write('''\n'''.join(self.all_tokens				)				)
						return (vocab_file,)



		@property
		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						return self.get_vocab_size(with_added_tokens=__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = False				)  ->    int:

						"""simple docstring"""






						return super()._add_tokens(__lowerCamelCase	,special_tokens=__lowerCamelCase				)






import warnings

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


__snake_case       :	List[Any]            =logging.get_logger(__name__)

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



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

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

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  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.'''				)

