

import os
from typing import List, Optional, Union

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


lowercase_             =     logging.get_logger(__name__)

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

lowercase_             =     {
    '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',
    },
}

lowercase_             =     {
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}
def   a   (    A__			:     Dict  )					->   int:





      """simple docstring"""



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







class 				__lowerCAmelCase  (      SCREAMING_SNAKE_CASE							):
  _a		    =						VOCAB_FILES_NAMES
  _a		    =						PRETRAINED_VOCAB_FILES_MAP
  _a		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a		    =						["""input_ids""", """attention_mask"""]




  def __init__(       self							,     lowerCAmelCase							,     lowerCAmelCase="<unk>"							,     lowerCAmelCase="<cls>"							,     lowerCAmelCase="<pad>"							,     lowerCAmelCase="<mask>"							,     lowerCAmelCase="<eos>"							,     **lowerCAmelCase							,     )						->				Union[str, Any]:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase      )
        _lowercase			 =load_vocab_file(lowerCAmelCase      )
        _lowercase			 =dict(enumerate(self.all_tokens      )      )
        _lowercase			 ={tok: ind for ind, tok in enumerate(self.all_tokens      )}
        _lowercase			 =unk_token
        _lowercase			 =cls_token
        _lowercase			 =pad_token
        _lowercase			 =mask_token
        _lowercase			 =eos_token
        _lowercase			 =self.all_tokens
        self._create_trie(self.unique_no_split_tokens      )




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase							,     **lowerCAmelCase      )						->				Union[str, Any]:
        '''simple docstring'''






        return text.split()




  def 						A__  (       self							,     lowerCAmelCase=False      )						->				Union[str, Any]:
        '''simple docstring'''






        return len(self._id_to_token      )




  def 						A__  (       self      )						->				Tuple:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase      )						->				int:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase      )						->				str:
        '''simple docstring'''






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




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = None      )						->				List[int]:
        '''simple docstring'''






        _lowercase			 =[self.cls_token_id]
        _lowercase			 =[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							,     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]
        _lowercase			 =[1] + ([0] * len(lowerCAmelCase      )) + [1]
        if token_ids_a is not None:
              mask += [0] * len(lowerCAmelCase      ) + [1]
        return mask




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase      )						->				Any:
        '''simple docstring'''






        _lowercase			 =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 						A__  (       self      )						->				int:
        '''simple docstring'''






        return self.get_vocab_size(with_added_tokens=lowerCAmelCase      )




  def 						A__  (       self							,     lowerCAmelCase							,     lowerCAmelCase = False      )						->				int:
        '''simple docstring'''






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





import math

import tensorflow as tf
from packaging import version
def   a   (    A__			:     Tuple  )					->   List[Any]:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )   ,     x.dtype  )  ))

      return x * cdf
def   a   (    A__			:     Any  )					->   int:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(math.pi   ,     x.dtype  )
      _lowercase			 =tf.cast(0.044715   ,     x.dtype  )
      _lowercase			 =0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(A__   ,     3  ))  ))

      return x * cdf
def   a   (    A__			:     Optional[int]  )					->   int:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )

      return x * tf.tanh(tf.math.softplus(A__  )  )
def   a   (    A__			:     Tuple  )					->   List[str]:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(0.044715   ,     x.dtype  )
      _lowercase			 =tf.cast(0.7978845608   ,     x.dtype  )

      return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))
def   a   (    A__			:     int  )					->   Any:





      """simple docstring"""



      _lowercase			 =tf.convert_to_tensor(A__  )
      _lowercase			 =tf.cast(1.702   ,     x.dtype  )
      return x * tf.math.sigmoid(coeff * x  )
def   a   (    A__			:     int  )					->   Dict:





      """simple docstring"""



      return tf.clip_by_value(_gelu(A__  )   ,     -10   ,     10  )
def   a   (    A__			:     Optional[int]   ,     A__			:     int=-1  )					->   Optional[Any]:





      """simple docstring"""



      _lowercase , _lowercase			 =tf.split(A__   ,     2   ,     axis=A__  )
      return a * tf.math.sigmoid(A__  )


if version.parse(tf.version.VERSION) >= version.parse('2.4'):
  def   a   (    A__			:     Any  )					->   Dict:





        """simple docstring"""



        return tf.keras.activations.gelu(A__   ,     approximate=A__  )

  lowercase_             =     tf.keras.activations.gelu







  lowercase_             =     approximate_gelu_wrap
else:
  lowercase_             =     _gelu
  lowercase_             =     _gelu_new


lowercase_             =     {
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def   a   (    A__			:     Optional[int]  )					->   str:





      """simple docstring"""



      if activation_string in ACTaFN:
            return ACTaFN[activation_string]
      else:
            raise KeyError(F'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}'''  )


