import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__  =					logging.get_logger(__name__)

lowerCamelCase__  =					{'vocab_file': 'spiece.model'}

lowerCamelCase__  =					{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

lowerCamelCase__  =					{'bert_for_seq_generation': 512}




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					VOCAB_FILES_NAMES
 __A			       =					PRETRAINED_VOCAB_FILES_MAP
 __A			       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __A			       =					[]
 __A			       =					['''input_ids''', '''attention_mask''']







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[Any]							, lowercase_				:   Optional[Any]="<s>"							, lowercase_				:   Any="</s>"							, lowercase_				:   Dict="<unk>"							, lowercase_				:   Tuple="<pad>"							, lowercase_				:   str="<::::>"							, lowercase_				:   Optional[Any] = None							, **lowercase_				:   Optional[int]							, )						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {} if sp_model_kwargs is None else sp_model_kwargs

   # Add extra_ids to the special token list
   super().__init__(
       bos_token=A_							, eos_token=A_							, unk_token=A_							, pad_token=A_							, sep_token=A_							, sp_model_kwargs=self.sp_model_kwargs							, **A_							, )

   _UpperCamelCase           =    vocab_file

   _UpperCamelCase           =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(A_)







 @property
 def 		__UpperCAmelCase				(		self				:   str)						->	List[str]:


   """simple docstring"""







   return self.sp_model.get_piece_size()







 def 		__UpperCAmelCase				(		self				:   Dict)						->	int:


   """simple docstring"""







   _UpperCamelCase           =    {self.convert_ids_to_tokens(A_): i for i in range(self.vocab_size)}
   vocab.update(self.added_tokens_encoder)
   return vocab







 def __getstate__(		self				:   int)						->	Union[str, Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.__dict__.copy()
   _UpperCamelCase           =    None
   return state







 def __setstate__(		self				:   List[str]							, lowercase_				:   Union[str, Any])						->	int:


   """simple docstring"""







   _UpperCamelCase           =    d

   # for backward compatibility
   if not hasattr(self							, "sp_model_kwargs"):
     _UpperCamelCase           =    {}

   _UpperCamelCase           =    spm.SentencePieceProcessor(**self.sp_model_kwargs)
   self.sp_model.Load(self.vocab_file)







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   str)						->	Any:


   """simple docstring"""







   return self.sp_model.encode(A_							, out_type=A_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	Tuple:


   """simple docstring"""







   return self.sp_model.piece_to_id(A_)







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    self.sp_model.IdToPiece(A_)
   return token







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   List[str])						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    []
   _UpperCamelCase           =    """"""
   for token in tokens:
     # make sure that special tokens are not decoded using sentencepiece model
     if token in self.all_special_tokens:
       out_string += self.sp_model.decode(A_) + token
       _UpperCamelCase           =    []
     else:
       current_sub_tokens.append(A_)
   out_string += self.sp_model.decode(A_)
   return out_string.strip()







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   Optional[Any]							, lowercase_				:   str = None)						->	str:


   """simple docstring"""







   if not os.path.isdir(A_):
     logger.error(f'Vocabulary path ({save_directory}) should be a directory')
     return
   _UpperCamelCase           =    os.path.join(
       A_							, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

   if os.path.abspath(self.vocab_file) != os.path.abspath(A_) and os.path.isfile(self.vocab_file):
     copyfile(self.vocab_file							, A_)
   elif not os.path.isfile(self.vocab_file):
     with open(A_							, "wb") as fi:
       _UpperCamelCase           =    self.sp_model.serialized_model_proto()
       fi.write(A_)

   return (out_vocab_file,)








from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


SCREAMING_SNAKE_CASE__					:     List[Any]       =     {
    'susnato/ernie-m-base_pytorch': 'https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json',
    'susnato/ernie-m-large_pytorch': 'https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json',
}


class 		_SCREAMING_SNAKE_CASE     (						A     ):
     __SCREAMING_SNAKE_CASE     					=    '''ernie_m'''
     __SCREAMING_SNAKE_CASE     					=    {"dropout": "classifier_dropout", "num_classes": "num_labels"}


     def __init__(  self							,					A_ = 25_00_02							,					A_ = 7_68							,					A_ = 12							,					A_ = 12							,					A_ = 30_72							,					A_ = "gelu"							,					A_ = 0.1							,					A_ = 0.1							,					A_ = 5_14							,					A_ = 0.0_2							,					A_ = 1							,					A_ = 1e-05							,					A_=None							,					A_=False							,					A_=0.0							,					**A_							,					):
       super().__init__(pad_token_id=A_							,					**A_    )
       _UpperCAmelCase		:							Tuple							=     vocab_size
       _UpperCAmelCase		:							List[str]							=     hidden_size
       _UpperCAmelCase		:							Optional[int]							=     num_hidden_layers
       _UpperCAmelCase		:							Tuple							=     num_attention_heads
       _UpperCAmelCase		:							Dict							=     intermediate_size
       _UpperCAmelCase		:							List[Any]							=     hidden_act
       _UpperCAmelCase		:							Any							=     hidden_dropout_prob
       _UpperCAmelCase		:							Any							=     attention_probs_dropout_prob
       _UpperCAmelCase		:							Tuple							=     max_position_embeddings
       _UpperCAmelCase		:							Dict							=     initializer_range
       _UpperCAmelCase		:							Optional[Any]							=     layer_norm_eps
       _UpperCAmelCase		:							str							=     classifier_dropout
       _UpperCAmelCase		:							Any							=     is_decoder
       _UpperCAmelCase		:							Union[str, Any]							=     act_dropout





