



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": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}



def 		lowerCAmelCase__    (					UpperCamelCase_				:       Dict	)->  List[str]:
    with open(UpperCamelCase_ ,       '''r'''	) as f:
        A__		 =					f.read().splitlines()
        return [l.strip() for l in lines]



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				['''input_ids''', '''attention_mask''']




     def __init__(				self ,						a__ ,						a__="<unk>" ,						a__="<cls>" ,						a__="<pad>" ,						a__="<mask>" ,						a__="<eos>" ,						**a__ ,						):
         super().__init__(**a__)
         A__		 =					load_vocab_file(a__)
         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 		snake_case_		(				self ,						a__):
         return self._id_to_token.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__):
         return self._token_to_id.get(a__ ,						self._token_to_id.get(self.unk_token))




     def 		snake_case_		(				self ,						a__ ,						**a__):
         return text.split()




     def 		snake_case_		(				self ,						a__=False):
         return len(self._id_to_token)




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




     def 		snake_case_		(				self ,						a__):
         return self._token_to_id.get(a__ ,						self._token_to_id.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self._id_to_token.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__ ,						a__ = 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 		snake_case_		(				self ,						a__ ,						a__ = None ,						a__ = 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(a__)) + [1]
         if token_ids_a is not None:
             mask += [0] * len(a__) + [1]
         return mask




     def 		snake_case_		(				self ,						a__ ,						a__):
         A__		 =					os.path.join(a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''')
         with open(a__ ,						'''w''') as f:
             f.write('''\n'''.join(self.all_tokens))
         return (vocab_file,)




     @property
     def 		snake_case_		(				self):
         return self.get_vocab_size(with_added_tokens=a__)




     def 		snake_case_		(				self ,						a__ ,						a__ = False):
         return super()._add_tokens(a__ ,						special_tokens=a__)





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


_lowercase						  =		{
    "albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/config.json",
    "albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/config.json",
    "albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/config.json",
    "albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json",
    "albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/config.json",
    "albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/config.json",
    "albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/config.json",
    "albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json",
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				'''albert'''




     def __init__(				self ,						a__=3_0_0_0_0 ,						a__=1_2_8 ,						a__=4_0_9_6 ,						a__=1_2 ,						a__=1 ,						a__=6_4 ,						a__=1_6_3_8_4 ,						a__=1 ,						a__="gelu_new" ,						a__=0 ,						a__=0 ,						a__=5_1_2 ,						a__=2 ,						a__=0.0_2 ,						a__=1e-12 ,						a__=0.1 ,						a__="absolute" ,						a__=0 ,						a__=2 ,						a__=3 ,						**a__ ,						):
         super().__init__(pad_token_id=a__ ,						bos_token_id=a__ ,						eos_token_id=a__ ,						**a__)

         A__		 =					vocab_size
         A__		 =					embedding_size
         A__		 =					hidden_size
         A__		 =					num_hidden_layers
         A__		 =					num_hidden_groups
         A__		 =					num_attention_heads
         A__		 =					inner_group_num
         A__		 =					hidden_act
         A__		 =					intermediate_size
         A__		 =					hidden_dropout_prob
         A__		 =					attention_probs_dropout_prob
         A__		 =					max_position_embeddings
         A__		 =					type_vocab_size
         A__		 =					initializer_range
         A__		 =					layer_norm_eps
         A__		 =					classifier_dropout_prob
         A__		 =					position_embedding_type







class     _UpperCAmelCase			(       A__     ):




     @property
     def 		snake_case_		(				self):
         if self.task == "multiple-choice":
             A__		 =					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A__		 =					{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ])
