



import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


__snake_case						:Any						=					'''bart'''
__snake_case						:int						=					True





@st.cache(allow_output_mutation=lowerCAmelCase_    )
def 					__snake_case     (      ):
    if LOAD_DENSE_INDEX:
        __a							=    AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''    )
        __a							=    AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''    ).to('''cuda:0'''    )
        __a							=    qar_model.eval()
    else:
        __a							=    (None, None)
    if MODEL_TYPE == "bart":
        __a							=    AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''    )
        __a							=    AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''    ).to('''cuda:0'''    )
        __a							=    torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''    )
        sas_model.load_state_dict(save_dict['''model''']    )
        __a							=    sas_model.eval()
    else:
        __a							=    make_qa_sas_model(
            model_name='''t5-small'''       ,			from_file='''seq2seq_models/eli5_t5_model_1024_4.pth'''       ,			device='''cuda:0'''    )
    return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)





@st.cache(allow_output_mutation=lowerCAmelCase_    )
def 					__snake_case     (      ):
    if LOAD_DENSE_INDEX:
        __a							=    faiss.StandardGpuResources()
        __a							=    datasets.load_dataset(path='''wiki_snippets'''       ,			name='''wiki40b_en_100_0'''    )['train']
        __a							=    np.memmap(
            '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''       ,			dtype='''float32'''       ,			mode='''r'''       ,			shape=(wikiaab_passages.num_rows, 128)       ,			)
        __a							=    faiss.IndexFlatIP(128    )
        __a							=    faiss.index_cpu_to_gpu(lowerCAmelCase_       ,			1       ,			lowerCAmelCase_    )
        wikiaab_gpu_index_flat.add(lowerCAmelCase_    )  # TODO fix for larger GPU
    else:
        __a							=    (None, None)
    __a							=    Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]    )
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)





@st.cache(allow_output_mutation=lowerCAmelCase_    )
def 					__snake_case     (      ):
    __a							=    datasets.load_dataset('''eli5'''       ,			name='''LFQA_reddit'''    )
    __a							=    elia['train_eli5']
    __a							=    np.memmap(
        '''eli5_questions_reps.dat'''       ,			dtype='''float32'''       ,			mode='''r'''       ,			shape=(elia_train.num_rows, 128)    )
    __a							=    faiss.IndexFlatIP(128    )
    eli5_train_q_index.add(lowerCAmelCase_    )
    return (elia_train, eli5_train_q_index)


__snake_case		,__snake_case		,__snake_case						:Any						=					load_indexes()
__snake_case		,__snake_case		,__snake_case		,__snake_case						:Union[str, Any]						=					load_models()
__snake_case		,__snake_case						:List[Any]						=					load_train_data()





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase=10    ):
    __a							=    embed_questions_for_retrieval([question]       ,			lowerCAmelCase_       ,			lowerCAmelCase_    )
    __a							=    eli5_train_q_index.search(lowerCAmelCase_       ,			lowerCAmelCase_    )
    __a							=    [elia_train[int(lowerCAmelCase_    )] for i in I[0]]
    return nn_examples





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase="wiki40b"       ,			_UpperCAmelCase="dense"       ,			_UpperCAmelCase=10    ):
    if source == "none":
        __a							=    (' <P> '.join(['''''' for _ in range(11    )]    ).strip(), [])
    else:
        if method == "dense":
            __a							=    query_qa_dense_index(
                lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_    )
        else:
            __a							=    query_es_index(
                lowerCAmelCase_       ,			lowerCAmelCase_       ,			index_name='''english_wiki40b_snippets_100w'''       ,			n_results=lowerCAmelCase_       ,			)
    __a							=    [
        (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
    ]
    __a							=    'question: {} context: {}'.format(lowerCAmelCase_       ,			lowerCAmelCase_    )
    return question_doc, support_list





@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _UpperCAmelCase    : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _UpperCAmelCase    : None),
    }    )
def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase=64       ,			_UpperCAmelCase=256       ,			_UpperCAmelCase=False       ,			_UpperCAmelCase=2       ,			_UpperCAmelCase=0.95       ,			_UpperCAmelCase=0.8    ):
    with torch.no_grad():
        __a							=    qa_sas_generate(
            lowerCAmelCase_       ,			lowerCAmelCase_       ,			lowerCAmelCase_       ,			num_answers=1       ,			num_beams=lowerCAmelCase_       ,			min_len=lowerCAmelCase_       ,			max_len=lowerCAmelCase_       ,			do_sample=lowerCAmelCase_       ,			temp=lowerCAmelCase_       ,			top_p=lowerCAmelCase_       ,			top_k=lowerCAmelCase_       ,			max_input_length=1024       ,			device='''cuda:0'''       ,			)[0]
    return (answer, support_list)


st.title('''Long Form Question Answering with ELI5''')

# Start sidebar
__snake_case						:Union[str, Any]						=					'''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__snake_case						:Any						=					'''
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class="img-container"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
''' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
__snake_case						:List[str]						=					'''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

__snake_case						:Optional[Any]						=					[
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
__snake_case						:str						=					st.sidebar.checkbox('''Demo options''')
if demo_options:
 __snake_case						:Optional[Any]						=					st.sidebar.selectbox(
     '''''',
     action_list,
     index=3,
 )
 __snake_case						:int						=					action_list.index(action_st)
 __snake_case						:List[Any]						=					st.sidebar.selectbox(
     '''''',
     ['''Show full text of passages''', '''Show passage section titles'''],
     index=0,
 )
 __snake_case						:List[str]						=					show_type == '''Show full text of passages'''
else:
 __snake_case						:Tuple						=					3
 __snake_case						:int						=					True

__snake_case						:Union[str, Any]						=					st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 __snake_case						:Union[str, Any]						=					'''
    ### Information retriever options

    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
    '''
 st.sidebar.markdown(retriever_info)
 __snake_case						:Optional[Any]						=					st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
 __snake_case						:Optional[int]						=					st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
 __snake_case						:List[Any]						=					'''wiki40b'''
 __snake_case						:Optional[int]						=					'''dense'''

__snake_case						:Optional[int]						=					'''beam'''
__snake_case						:List[Any]						=					2
__snake_case						:List[Any]						=					64
__snake_case						:Any						=					256
__snake_case						:Tuple						=					None
__snake_case						:int						=					None
__snake_case						:List[str]						=					st.sidebar.checkbox('''Generation options''')
if generate_options:
 __snake_case						:int						=					'''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
 st.sidebar.markdown(generate_info)
 __snake_case						:List[str]						=					st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
 __snake_case						:Optional[Any]						=					st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 __snake_case						:List[Any]						=					st.sidebar.slider(
     '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  __snake_case						:Any						=					st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  __snake_case						:Dict						=					st.sidebar.slider(
      '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.9_5, step=0.0_1, format=None, key=None
  )
  __snake_case						:Optional[Any]						=					st.sidebar.slider(
      '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
  )
  __snake_case						:Optional[int]						=					None

# start main text
__snake_case						:Dict						=					[
    '''<MY QUESTION>''',
    '''How do people make chocolate?''',
    '''Why do we get a fever when we are sick?''',
    '''How can different animals perceive different colors?''',
    '''What is natural language processing?''',
    '''What\'s the best way to treat a sunburn?''',
    '''What exactly are vitamins ?''',
    '''How does nuclear energy provide electricity?''',
    '''What\'s the difference between viruses and bacteria?''',
    '''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
    '''Why do people like drinking coffee even though it tastes so bad?''',
    '''What happens when wine ages? How does it make the wine taste better?''',
    '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
    '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
    '''How does New Zealand have so many large bird predators?''',
]
__snake_case						:List[Any]						=					st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
 __snake_case						:Optional[int]						=					st.text_input('''Enter your question here:''', '''''')
else:
 __snake_case						:Tuple						=					question_s

if st.button('''Show me!'''):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   __snake_case		,__snake_case						:Dict						=					make_support(question, source=wiki_source, method='''dense''', n_results=10)
   __snake_case		,__snake_case						:Optional[int]						=					make_support(question, source=wiki_source, method='''sparse''', n_results=10)
   __snake_case						:int						=					[]
   for res_d, res_s in zip(support_list_dense, support_list_sparse):
    if tuple(res_d) not in support_list:
     support_list += [tuple(res_d)]
    if tuple(res_s) not in support_list:
     support_list += [tuple(res_s)]
   __snake_case						:str						=					support_list[:10]
   __snake_case						:Union[str, Any]						=					'''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
  else:
   __snake_case		,__snake_case						:List[str]						=					make_support(question, source=wiki_source, method=index_type, n_results=10)
 if action in [0, 3]:
  __snake_case		,__snake_case						:Any						=					answer_question(
      question_doc,
      sas_model,
      sas_tokenizer,
      min_len=min_len,
      max_len=int(max_len),
      sampling=(sampled == '''sampled'''),
      n_beams=n_beams,
      top_p=top_p,
      temp=temp,
  )
  st.markdown('''### The model generated answer is:''')
  st.write(answer)
 if action in [0, 1, 3] and wiki_source != "none":
  st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
  for i, res in enumerate(support_list):
   __snake_case						:Union[str, Any]						=					'''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
   __snake_case						:str						=					res[1].strip()
   if sec_titles == "":
    __snake_case						:Optional[Any]						=					'''[{}]({})'''.format(res[0], wiki_url)
   else:
    __snake_case						:int						=					sec_titles.split(''' & ''')
    __snake_case						:int						=					''' & '''.join(
        ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
    )
   st.markdown(
       '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
       unsafe_allow_html=True,
   )
   if show_passages:
    st.write(
        '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
    )
 if action in [2, 3]:
  __snake_case						:List[str]						=					find_nearest_training(question)
  __snake_case						:Tuple						=					nn_train_list[0]
  st.markdown(
      '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
  )
  __snake_case						:Tuple						=					[
      '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
      for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
      if i == 0 or sc > 2
  ]
  st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


__snake_case						:Tuple						=					'''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)







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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


__snake_case						:Dict						=					logging.get_logger(__name__)

__snake_case						:List[str]						=					{'''vocab_file''': '''spiece.model'''}

__snake_case						:Dict						=					{
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=False					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=False					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<s>"					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]="</s>"					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]="<unk>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<sep>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<pad>"					,		__SCREAMING_SNAKE_CASE							:					List[Any]="<cls>"					,		__SCREAMING_SNAKE_CASE							:					Optional[int]="<mask>"					,		__SCREAMING_SNAKE_CASE							:					Any=["<eop>", "<eod>"]					,		__SCREAMING_SNAKE_CASE							:					Optional[Dict[str, Any]] = None					,		**__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		):


           '''simple docstring'''

           __a							=    AddedToken(__SCREAMING_SNAKE_CASE					,		lstrip=__SCREAMING_SNAKE_CASE					,		rstrip=__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else mask_token

           __a							=    {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               do_lower_case=__SCREAMING_SNAKE_CASE					,		remove_space=__SCREAMING_SNAKE_CASE					,		keep_accents=__SCREAMING_SNAKE_CASE					,		bos_token=__SCREAMING_SNAKE_CASE					,		eos_token=__SCREAMING_SNAKE_CASE					,		unk_token=__SCREAMING_SNAKE_CASE					,		sep_token=__SCREAMING_SNAKE_CASE					,		pad_token=__SCREAMING_SNAKE_CASE					,		cls_token=__SCREAMING_SNAKE_CASE					,		mask_token=__SCREAMING_SNAKE_CASE					,		additional_special_tokens=__SCREAMING_SNAKE_CASE					,		sp_model_kwargs=self.sp_model_kwargs					,		**__SCREAMING_SNAKE_CASE					,		)

           __a							=    3

           __a							=    do_lower_case
           __a							=    remove_space
           __a							=    keep_accents
           __a							=    vocab_file

           __a							=    spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(__SCREAMING_SNAKE_CASE)

           try:
               import jieba
           except ModuleNotFoundError as error:
               raise error.__class__(
                   '''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
                   '''See https://pypi.org/project/jieba/ for installation.''')
           __a							=    jieba
           __a							=    str.maketrans(''' \n'''					,		'''\u2582\u2583''')



       @property
       # Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           return len(self.sp_model)



       def 		_lowerCamelCase      (		self							:					str):


           '''simple docstring'''

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



       def __getstate__(		self							:					Any):


           '''simple docstring'''

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



       def __setstate__(		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					List[str]):


           '''simple docstring'''

           __a							=    d

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

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



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           if self.remove_space:
               __a							=    ''' '''.join(inputs.strip().split())
           else:
               __a							=    inputs
           __a							=    outputs.replace('''``'''					,		'''"''').replace('''\'\''''					,		'''"''')

           if not self.keep_accents:
               __a							=    unicodedata.normalize('''NFKD'''					,		__SCREAMING_SNAKE_CASE)
               __a							=    ''''''.join([c for c in outputs if not unicodedata.combining(__SCREAMING_SNAKE_CASE)])
           if self.do_lower_case:
               __a							=    outputs.lower()

           return outputs



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a							=    self.preprocess_text(__SCREAMING_SNAKE_CASE)
           __a							=    self.sp_model.encode(__SCREAMING_SNAKE_CASE					,		out_type=__SCREAMING_SNAKE_CASE)
           __a							=    []
           for piece in pieces:
               if len(__SCREAMING_SNAKE_CASE) > 1 and piece[-1] == str(''',''') and piece[-2].isdigit():
                   __a							=    self.sp_model.EncodeAsPieces(piece[:-1].replace(__SCREAMING_SNAKE_CASE					,		''''''))
                   if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                       if len(cur_pieces[0]) == 1:
                           __a							=    cur_pieces[1:]
                       else:
                           __a							=    cur_pieces[0][1:]
                   cur_pieces.append(piece[-1])
                   new_pieces.extend(__SCREAMING_SNAKE_CASE)
               else:
                   new_pieces.append(__SCREAMING_SNAKE_CASE)

           return new_pieces



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					List[Any]):


           '''simple docstring'''

           return self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Optional[int]):


           '''simple docstring'''

           return self.sp_model.IdToPiece(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    ''''''.join(__SCREAMING_SNAKE_CASE).replace(__SCREAMING_SNAKE_CASE					,		''' ''').strip()
           return out_string



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [self.cls_token_id]
           if token_ids_a is None:
               return token_ids_a + sep + cls
           return token_ids_a + sep + token_ids_a + sep + cls



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None					,		__SCREAMING_SNAKE_CASE							:					bool = False):


           '''simple docstring'''

           if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__SCREAMING_SNAKE_CASE					,		token_ids_a=__SCREAMING_SNAKE_CASE					,		already_has_special_tokens=__SCREAMING_SNAKE_CASE)

           if token_ids_a is not None:
               return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1] + ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]
           return ([0] * len(__SCREAMING_SNAKE_CASE)) + [1, 1]



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					List[int]					,		__SCREAMING_SNAKE_CASE							:					Optional[List[int]] = None):


           '''simple docstring'''

           __a							=    [self.sep_token_id]
           __a							=    [2]

           if token_ids_a is None:
               return len(token_ids_a + sep) * [0] + cls_segment_id
           return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id



       def 		_lowerCamelCase      (		self							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					str					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None):


           '''simple docstring'''

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

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

           return (out_vocab_file,)





       def 		_lowerCamelCase      (		self							:					Any					,		*__SCREAMING_SNAKE_CASE							:					List[str]					,		**__SCREAMING_SNAKE_CASE							:					Any):


           '''simple docstring'''

           __a							=    super()._decode(*__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           __a							=    text.replace(''' '''					,		'''''').replace('''\u2582'''					,		''' ''').replace('''\u2583'''					,		'''\n''')
           return text





