


'''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_      :		Tuple          =		logging.get_logger(__name__)

UpperCAmelCase_      :		str          =		{"vocab_file": "vocab.txt"}

UpperCAmelCase_      :		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",
    },
}

UpperCAmelCase_      :		Union[str, Any]          =		{
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





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







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Optional[int]							  =		VOCAB_FILES_NAMES
 __lowerCAmelCase						: Dict							  =		PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase						: Dict							  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase						: int							  =		["""input_ids""", """attention_mask"""]




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    str:
    return self._id_to_token.get(lowerCamelCase_		,      self.unk_token      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    int:
    return self._token_to_id.get(lowerCamelCase_		,      self._token_to_id.get(self.unk_token      )      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      **lowerCamelCase_      )      ->    str:
    return text.split()




 def   __UpperCamelCase   (    self		,      lowerCamelCase_=False      )      ->    int:
    return len(self._id_to_token      )




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




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    int:
    return self._token_to_id.get(lowerCamelCase_		,      self._token_to_id.get(self.unk_token      )      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_      )      ->    str:
    return self._id_to_token.get(lowerCamelCase_		,      self.unk_token      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None      )      ->    List[int]:
    _a					:  Any						=  [self.cls_token_id]
    _a					:  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   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = None		,      lowerCamelCase_ = 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]
    _a					:  int						=  [1] + ([0] * len(lowerCamelCase_      )) + [1]
    if token_ids_a is not None:
       mask += [0] * len(lowerCamelCase_      ) + [1]
    return mask




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    List[Any]:
    _a					:  Optional[int]						=  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   __UpperCamelCase   (    self      )      ->    int:
    return self.get_vocab_size(with_added_tokens=lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_ = False      )      ->    int:
    return super()._add_tokens(lowerCamelCase_		,      special_tokens=lowerCamelCase_      )






'''simple docstring'''




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


UpperCAmelCase_      :		Dict          =		logging.get_logger(__name__)

UpperCAmelCase_      :		List[str]          =		{
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		"""visual_bert"""




 def __init__(    self		,      lowerCamelCase_=3_0_5_2_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-12		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=1		,      lowerCamelCase_=0		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Optional[Any]:
    super().__init__(pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_		,      **lowerCamelCase_      )

    _a					:  List[str]						=  vocab_size
    _a					:  Union[str, Any]						=  max_position_embeddings
    _a					:  Tuple						=  hidden_size
    _a					:  Tuple						=  visual_embedding_dim
    _a					:  Optional[Any]						=  num_hidden_layers
    _a					:  str						=  num_attention_heads
    _a					:  Union[str, Any]						=  intermediate_size
    _a					:  List[str]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  List[Any]						=  attention_probs_dropout_prob
    _a					:  Optional[Any]						=  initializer_range
    _a					:  List[Any]						=  type_vocab_size
    _a					:  Any						=  layer_norm_eps
    _a					:  Tuple						=  bypass_transformer
    _a					:  str						=  special_visual_initialize


