
import os
from typing import List, Optional, Union

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


lowerCamelCase_  :	Dict 				=					logging.get_logger(__name__)

lowerCamelCase_  :	List[Any] 				=					{"""vocab_file""": """vocab.txt"""}

lowerCamelCase_  :	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""",
    },
}

lowerCamelCase_  :	Optional[int] 				=					{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}







def     A__   (  lowerCamelCase  )					->		Union[str, Any]:
				with open(lowerCamelCase       ,      """r"""  ) as f:
								UpperCamelCase_:							Optional[Any]		=  f.read().splitlines()
								return [l.strip() for l in lines]





class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  List[str]    =	VOCAB_FILES_NAMES
			__UpperCamelCase :  Tuple    =	PRETRAINED_VOCAB_FILES_MAP
			__UpperCamelCase :  Optional[int]    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__UpperCamelCase :  Any    =	["""input_ids""", """attention_mask"""]

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

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    int ):
							return self._id_to_token.get(snake_case_  ,		self.unk_token )

			def  lowerCAmelCase__		(    self	:    Tuple  ,		snake_case_	:    str ):
							return self._token_to_id.get(snake_case_  ,		self._token_to_id.get(self.unk_token ) )

			def  lowerCAmelCase__		(    self	:    str  ,		snake_case_	:    Union[str, Any]  ,		**snake_case_	:    Any ):
							return text.split()

			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Union[str, Any]=False ):
							return len(self._id_to_token )

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

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

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

			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    List[int]  ,		snake_case_	:    Optional[List[int]] = None ):
							UpperCamelCase_:							List[str]		=  [self.cls_token_id]
							UpperCamelCase_:							Any		=  [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	:    Optional[int]  ,		snake_case_	:    List  ,		snake_case_	:    Optional[List] = None  ,		snake_case_	:    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]
							UpperCamelCase_:							Optional[Any]		=  [1] + ([0] * len(snake_case_ )) + [1]
							if token_ids_a is not None:
											mask += [0] * len(snake_case_ ) + [1]
							return mask

			def  lowerCAmelCase__		(    self	:    Optional[Any]  ,		snake_case_	:    Dict  ,		snake_case_	:    int ):
							UpperCamelCase_:							Tuple		=  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  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return self.get_vocab_size(with_added_tokens=snake_case_ )




			def  lowerCAmelCase__		(    self	:    Optional[int]  ,		snake_case_	:    Union[List[str], List[AddedToken]]  ,		snake_case_	:    bool = False ):
							return super()._add_tokens(snake_case_  ,		special_tokens=snake_case_ )








from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Union[str, Any]    =	["""image_processor""", """tokenizer"""]
			__UpperCamelCase :  List[Any]    =	"""AutoImageProcessor"""
			__UpperCamelCase :  Tuple    =	"""AutoTokenizer"""

			def __init__(    self	:    Any  ,		snake_case_	:    Optional[Any]  ,		snake_case_	:    Any ):
							super().__init__(snake_case_  ,		snake_case_ )
							UpperCamelCase_:							int		=  self.image_processor

			def __call__(    self	:    str  ,		snake_case_	:    Optional[int]=None  ,		snake_case_	:    int=None  ,		snake_case_	:    Dict=None  ,		**snake_case_	:    Optional[int] ):

							if text is None and images is None:
											raise ValueError("""You have to specify either text or images. Both cannot be none.""" )

							if text is not None:
											UpperCamelCase_:							List[Any]		=  self.tokenizer(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if images is not None:
											UpperCamelCase_:							Any		=  self.image_processor(snake_case_  ,		return_tensors=snake_case_  ,		**snake_case_ )

							if text is not None and images is not None:
											UpperCamelCase_:							Tuple		=  image_features.pixel_values
											return encoding
							elif text is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**snake_case_ )  ,		tensor_type=snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		*snake_case_	:    int  ,		**snake_case_	:    Optional[Any] ):
							return self.tokenizer.batch_decode(*snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		*snake_case_	:    Optional[Any]  ,		**snake_case_	:    str ):
							return self.tokenizer.decode(*snake_case_  ,		**snake_case_ )




			@property
			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							return ["input_ids", "attention_mask", "pixel_values"]






