



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		=  logging.get_logger(__name__)

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

_snake_case		=  {
    '''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		=  {
    '''facebook/esm2_t6_8M_UR50D''': 1024,
    '''facebook/esm2_t12_35M_UR50D''': 1024,
}






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							with open(__lowerCamelCase				, "r"  ) as f:
														_lowerCAmelCase     :							List[Any]    =    f.read().splitlines()
														return [l.strip() for l in lines]



class   UpperCAmelCase_    (       _lowerCAmelCase):
							lowerCamelCase__   			=		VOCAB_FILES_NAMES
							lowerCamelCase__   			=		PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase__   			=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase__   			=		['input_ids', 'attention_mask']







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

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			**__a):

														'''simple docstring'''

														return text.split()







							def    snake_case__      (   self,			__a=False):

														'''simple docstring'''

														return len(self._id_to_token)







							def    snake_case__      (   self):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			__a,			__a = None):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    [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    snake_case__      (   self,			__a,			__a = None,			__a = False):

														'''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     :							str    =    [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):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    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):

														'''simple docstring'''

														return self.get_vocab_size(with_added_tokens=__a)







							def    snake_case__      (   self,			__a,			__a = False):

														'''simple docstring'''

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







'''simple docstring'''


import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 from transformers.models.bert.modeling_flax_bert import (
     FlaxBertForMaskedLM,
     FlaxBertForMultipleChoice,
     FlaxBertForNextSentencePrediction,
     FlaxBertForPreTraining,
     FlaxBertForQuestionAnswering,
     FlaxBertForSequenceClassification,
     FlaxBertForTokenClassification,
     FlaxBertModel,
 )
class       a_   (				unittest.TestCase							):





     def __init__( self		:					List[str]	,      lowercase		:					str	,      lowercase		:					Union[str, Any]=13	,      lowercase		:					int=7	,      lowercase		:					List[str]=True	,      lowercase		:					int=True	,      lowercase		:					str=True	,      lowercase		:					Any=True	,      lowercase		:					List[str]=99	,      lowercase		:					Union[str, Any]=32	,      lowercase		:					Optional[Any]=5	,      lowercase		:					Dict=4	,      lowercase		:					Dict=37	,      lowercase		:					Dict="gelu"	,      lowercase		:					Optional[int]=0.1	,      lowercase		:					str=0.1	,      lowercase		:					List[Any]=512	,      lowercase		:					str=16	,      lowercase		:					Dict=2	,      lowercase		:					Any=0.02	,      lowercase		:					Any=4	,      ):





        """simple docstring"""




        lowercase_			:List[str]    			= parent
        lowercase_			:Any    			= batch_size
        lowercase_			:Dict    			= seq_length
        lowercase_			:Union[str, Any]    			= is_training
        lowercase_			:Optional[int]    			= use_attention_mask
        lowercase_			:Any    			= use_token_type_ids
        lowercase_			:Union[str, Any]    			= use_labels
        lowercase_			:Dict    			= vocab_size
        lowercase_			:Tuple    			= hidden_size
        lowercase_			:Tuple    			= num_hidden_layers
        lowercase_			:Optional[int]    			= num_attention_heads
        lowercase_			:Optional[Any]    			= intermediate_size
        lowercase_			:str    			= hidden_act
        lowercase_			:Tuple    			= hidden_dropout_prob
        lowercase_			:Optional[Any]    			= attention_probs_dropout_prob
        lowercase_			:Tuple    			= max_position_embeddings
        lowercase_			:Any    			= type_vocab_size
        lowercase_			:int    			= type_sequence_label_size
        lowercase_			:Tuple    			= initializer_range
        lowercase_			:Optional[Any]    			= num_choices





     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:int    			= ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size						)

        lowercase_			:Union[str, Any]    			= None
        if self.use_attention_mask:
           lowercase_			:Dict    			= random_attention_mask([self.batch_size, self.seq_length]						)

        lowercase_			:List[str]    			= None
        if self.use_token_type_ids:
           lowercase_			:str    			= ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size						)

        lowercase_			:Optional[Any]    			= BertConfig(
            vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      is_decoder=lowercase	,      initializer_range=self.initializer_range	,      )

        return config, input_ids, token_type_ids, attention_mask





     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:int    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Tuple    			= config_and_inputs
        lowercase_			:Tuple    			= {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict







     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        lowercase_			:Any    			= self.prepare_config_and_inputs()
        lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			:Union[str, Any]    			= config_and_inputs

        lowercase_			:Dict    			= True
        lowercase_			:Optional[int]    			= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
        lowercase_			:str    			= ids_tensor([self.batch_size, self.seq_length]	,      vocab_size=2						)

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )







@require_flax
class       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			True

     __A      =			(
         (
             FlaxBertModel,
             FlaxBertForPreTraining,
             FlaxBertForMaskedLM,
             FlaxBertForMultipleChoice,
             FlaxBertForQuestionAnswering,
             FlaxBertForNextSentencePrediction,
             FlaxBertForSequenceClassification,
             FlaxBertForTokenClassification,
             FlaxBertForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= FlaxBertModelTester(self						)







     @slow
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        lowercase_			:List[str]    			= FlaxBertModel.from_pretrained("bert-base-cased"						)
        lowercase_			:str    			= model(np.ones((1, 1)						)						)
        self.assertIsNotNone(lowercase						)


