

"""simple docstring"""






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


_lowerCAmelCase						=		"""bart"""
_lowerCAmelCase						=		True

@st.cache(allow_output_mutation=_lowerCamelCase   )
def 		lowerCamelCase__		(					):

    '''simple docstring'''



    if LOAD_DENSE_INDEX:
        _lowerCAmelCase       :   str  =    AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased'   )
        _lowerCAmelCase       :   int  =    AutoModel.from_pretrained('yjernite/retribert-base-uncased'   ).to('cuda:0'   )
        _lowerCAmelCase       :   Union[str, Any]  =    qar_model.eval()
    else:
        _lowerCAmelCase,					_lowerCAmelCase       :   Optional[Any]  =    (None, None)
    if MODEL_TYPE == "bart":
        _lowerCAmelCase       :   List[Any]  =    AutoTokenizer.from_pretrained('yjernite/bart_eli5'   )
        _lowerCAmelCase       :   List[str]  =    AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5'   ).to('cuda:0'   )
        _lowerCAmelCase       :   List[str]  =    torch.load('seq2seq_models/eli5_bart_model_blm_2.pth'   )
        sas_model.load_state_dict(save_dict['model']   )
        _lowerCAmelCase       :   List[str]  =    sas_model.eval()
    else:
        _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    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 		lowerCamelCase__		(					):

    '''simple docstring'''



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

@st.cache(allow_output_mutation=_lowerCamelCase   )
def 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   List[Any]  =    datasets.load_dataset('eli5' ,   name='LFQA_reddit'   )
    _lowerCAmelCase       :   Optional[int]  =    elia['train_eli5']
    _lowerCAmelCase       :   Union[str, Any]  =    np.memmap(
        'eli5_questions_reps.dat' ,   dtype='float32' ,   mode='r' ,   shape=(elia_train.num_rows, 128)   )
    _lowerCAmelCase       :   Dict  =    faiss.IndexFlatIP(128   )
    eli5_train_q_index.add(_lowerCamelCase   )
    return (elia_train, eli5_train_q_index)


_lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						=		load_indexes()
_lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase						=		load_models()
_lowerCAmelCase	,   _lowerCAmelCase						=		load_train_data()

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=10   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    embed_questions_for_retrieval([question] ,   _lowerCamelCase ,   _lowerCamelCase   )
    _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    eli5_train_q_index.search(_lowerCamelCase ,   _lowerCamelCase   )
    _lowerCAmelCase       :   Optional[Any]  =    [elia_train[int(_lowerCamelCase   )] for i in I[0]]
    return nn_examples

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase="wiki40b" ,   _lowerCamelCase="dense" ,   _lowerCamelCase=10   ):

    '''simple docstring'''



    if source == "none":
        _lowerCAmelCase,					_lowerCAmelCase       :   str  =    (' <P> '.join(['' for _ in range(11   )]   ).strip(), [])
    else:
        if method == "dense":
            _lowerCAmelCase,					_lowerCAmelCase       :   Dict  =    query_qa_dense_index(
                _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   )
        else:
            _lowerCAmelCase,					_lowerCAmelCase       :   Union[str, Any]  =    query_es_index(
                _lowerCamelCase ,   _lowerCamelCase ,   index_name='english_wiki40b_snippets_100w' ,   n_results=_lowerCamelCase ,   )
    _lowerCAmelCase       :   List[Any]  =    [
        (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
    ]
    _lowerCAmelCase       :   Any  =    'question: {} context: {}'.format(_lowerCamelCase ,   _lowerCamelCase   )
    return question_doc, support_list

@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _lowerCamelCase   : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowerCamelCase   : None),
    }   )
def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=64 ,   _lowerCamelCase=256 ,   _lowerCamelCase=False ,   _lowerCamelCase=2 ,   _lowerCamelCase=0.95 ,   _lowerCamelCase=0.8   ):

    '''simple docstring'''



    with torch.no_grad():
        _lowerCAmelCase       :   Optional[Any]  =    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
_lowerCAmelCase						=		"""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_lowerCAmelCase						=		"""
<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
_lowerCAmelCase						=		"""
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)

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

_lowerCAmelCase						=		st.sidebar.checkbox("""Retrieval options""")
if retrieval_options:
 _lowerCAmelCase						=		"""
    ### 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)
 _lowerCAmelCase						=		st.sidebar.selectbox("""Which Wikipedia format should the model use?""", ["""wiki40b""", """none"""])
 _lowerCAmelCase						=		st.sidebar.selectbox("""Which Wikipedia indexer should the model use?""", ["""dense""", """sparse""", """mixed"""])
else:
 _lowerCAmelCase						=		"""wiki40b"""
 _lowerCAmelCase						=		"""dense"""

_lowerCAmelCase						=		"""beam"""
_lowerCAmelCase						=		2
_lowerCAmelCase						=		6_4
_lowerCAmelCase						=		2_5_6
_lowerCAmelCase						=		None
_lowerCAmelCase						=		None
_lowerCAmelCase						=		st.sidebar.checkbox("""Generation options""")
if generate_options:
 _lowerCAmelCase						=		"""
    ### 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)
 _lowerCAmelCase						=		st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
 _lowerCAmelCase						=		st.sidebar.slider(
     """Minimum generation length""", min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
 )
 _lowerCAmelCase						=		st.sidebar.slider(
     """Maximum generation length""", min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
 )
 if sampled == "beam":
  _lowerCAmelCase						=		st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  _lowerCAmelCase						=		st.sidebar.slider(
      """Nucleus sampling p""", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
  )
  _lowerCAmelCase						=		st.sidebar.slider(
      """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
  )
  _lowerCAmelCase						=		None

# start main text
_lowerCAmelCase						=		[
    """<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?""",
]
_lowerCAmelCase						=		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>":
 _lowerCAmelCase						=		st.text_input("""Enter your question here:""", """""")
else:
 _lowerCAmelCase						=		question_s

if st.button("""Show me!"""):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   _lowerCAmelCase	,   _lowerCAmelCase						=		make_support(question, source=wiki_source, method="""dense""", n_results=1_0)
   _lowerCAmelCase	,   _lowerCAmelCase						=		make_support(question, source=wiki_source, method="""sparse""", n_results=1_0)
   _lowerCAmelCase						=		[]
   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)]
   _lowerCAmelCase						=		support_list[:1_0]
   _lowerCAmelCase						=		"""<P> """ + """ <P> """.join([res[-1] for res in support_list])
  else:
   _lowerCAmelCase	,   _lowerCAmelCase						=		make_support(question, source=wiki_source, method=index_type, n_results=1_0)
 if action in [0, 3]:
  _lowerCAmelCase	,   _lowerCAmelCase						=		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):
   _lowerCAmelCase						=		"""https://en.wikipedia.org/wiki/{}""".format(res[0].replace(""" """, """_"""))
   _lowerCAmelCase						=		res[1].strip()
   if sec_titles == "":
    _lowerCAmelCase						=		"""[{}]({})""".format(res[0], wiki_url)
   else:
    _lowerCAmelCase						=		sec_titles.split(""" & """)
    _lowerCAmelCase						=		""" & """.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]:
  _lowerCAmelCase						=		find_nearest_training(question)
  _lowerCAmelCase						=		nn_train_list[0]
  st.markdown(
      """--- \n ### The most similar question in the ELI5 training set was: \n\n {}""".format(train_exple["""title"""])
  )
  _lowerCAmelCase						=		[
      """{}. {}""".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)))


_lowerCAmelCase						=		"""
---

**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)








"""simple docstring"""






import json
import os
from typing import Dict, List, Optional, Tuple

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


_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		{
    """vocab_file""": """vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
    """merges_file""": """merges.txt""",
}

_lowerCAmelCase						=		{
    """vocab_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json"""
        ),
    },
    """tokenizer_config_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json"""
        ),
    },
    """merges_file""": {
        """facebook/s2t-wav2vec2-large-en-de""": (
            """https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt"""
        ),
    },
}

_lowerCAmelCase						=		"""</w>"""
_lowerCAmelCase						=		"""@@ """

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    set()
    _lowerCAmelCase       :   Dict  =    word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)   )
        _lowerCAmelCase       :   Any  =    char
    return pairs


# Speech2Text2 has no max input length
_lowerCAmelCase						=		{"""facebook/s2t-wav2vec2-large-en-de""": 1_0_2_4}







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="<s>"    ,_A="<pad>"    ,_A="</s>"    ,_A="<unk>"    ,_A=False    ,_A=None    ,**_A    ,):
      '''simple docstring'''

      super().__init__(
          unk_token=_A    ,bos_token=_A    ,eos_token=_A    ,pad_token=_A    ,do_lower_case=_A    ,**_A    ,)

      _lowerCAmelCase       :   List[Any]  =    do_lower_case

      with open(_A    ,encoding='utf-8'      ) as vocab_handle:
          _lowerCAmelCase       :   Optional[int]  =    json.load(_A      )
      _lowerCAmelCase       :   Tuple  =    {v: k for k, v in self.encoder.items()}

      if merges_file is None:
          logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding."""      )

          _lowerCAmelCase       :   Optional[Any]  =    None
          _lowerCAmelCase       :   Tuple  =    None
      else:
          with open(_A    ,encoding='utf-8'      ) as merges_handle:
              _lowerCAmelCase       :   Optional[Any]  =    merges_handle.read().split('\n'      )[:-1]

          _lowerCAmelCase       :   List[str]  =    [tuple(merge.split()[:2]      ) for merge in merges]
          _lowerCAmelCase       :   List[Any]  =    dict(zip(_A    ,range(len(_A      )      )      )      )
          _lowerCAmelCase       :   Union[str, Any]  =    {}

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.decoder      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return dict(self.encoder    ,**self.added_tokens_encoder      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    tuple(token[:-1]      ) + (token[-1] + BPE_TOKEN_MERGES,)
      if token in self.cache:
          return self.cache[token]
      _lowerCAmelCase       :   str  =    get_pairs(_A      )

      if not pairs:
          return token

      while True:
          _lowerCAmelCase       :   List[str]  =    min(_A    ,key=lambda _A      : self.bpe_ranks.get(_A    ,float('inf'      )      )      )
          if bigram not in self.bpe_ranks:
              break
          _lowerCAmelCase,					_lowerCAmelCase       :   Optional[int]  =    bigram
          _lowerCAmelCase       :   Union[str, Any]  =    []
          _lowerCAmelCase       :   Dict  =    0
          while i < len(_A      ):
              try:
                  _lowerCAmelCase       :   Dict  =    word.index(_A    ,_A      )
              except ValueError:
                  new_word.extend(word[i:]      )
                  break
              else:
                  new_word.extend(word[i:j]      )
                  _lowerCAmelCase       :   Optional[Any]  =    j

              if word[i] == first and i < len(_A      ) - 1 and word[i + 1] == second:
                  new_word.append(first + second      )
                  i += 2
              else:
                  new_word.append(word[i]      )
                  i += 1
          _lowerCAmelCase       :   Optional[Any]  =    tuple(_A      )
          _lowerCAmelCase       :   List[str]  =    new_word
          if len(_A      ) == 1:
              break
          else:
              _lowerCAmelCase       :   List[str]  =    get_pairs(_A      )
      _lowerCAmelCase       :   Any  =    ' '.join(_A      )
      if word == "\n  " + BPE_TOKEN_MERGES:
          _lowerCAmelCase       :   str  =    '\n' + BPE_TOKEN_MERGES

      if word.endswith(_A      ):
          _lowerCAmelCase       :   Dict  =    word.replace(_A    ,''      )

      _lowerCAmelCase       :   str  =    word.replace(' '    ,_A      )
      _lowerCAmelCase       :   str  =    word
      return word

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      if self.bpe_ranks is None:
          raise ValueError(
              'This tokenizer was instantiated without a `merges.txt` file, so'
              ' that it can only be used for decoding, not for encoding.'
              'Make sure to provide `merges.txt` file at instantiation to enable '
              'encoding.'      )

      if self.do_lower_case:
          _lowerCAmelCase       :   Optional[Any]  =    text.lower()

      _lowerCAmelCase       :   Tuple  =    text.split()

      _lowerCAmelCase       :   Union[str, Any]  =    []
      for token in text:
          if token:
              split_tokens.extend(list(self.bpe(_A      ).split(' '      )      )      )

      return split_tokens

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return self.encoder.get(_A    ,self.encoder.get(self.unk_token      )      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.decoder.get(_A    ,self.unk_token      )
      return result

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    ' '.join(_A      )

      # make sure @@ tokens are concatenated
      _lowerCAmelCase       :   int  =    ''.join(string.split(_A      )      )

      return string







  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return
      _lowerCAmelCase       :   List[Any]  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )
      _lowerCAmelCase       :   str  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']      )

      with open(_A    ,'w'    ,encoding='utf-8'      ) as f:
          f.write(json.dumps(self.encoder    ,indent=2    ,sort_keys=_A    ,ensure_ascii=_A      ) + '\n'      )

      _lowerCAmelCase       :   str  =    0
      if self.bpe_ranks is None:
          return (vocab_file,)

      with open(_A    ,'w'    ,encoding='utf-8'      ) as writer:
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,key=lambda _A      : kv[1]      ):
              if index != token_index:
                  logger.warning(
                      F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                      ' Please check that the tokenizer is not corrupted!'      )
                  _lowerCAmelCase       :   Dict  =    token_index
              writer.write(' '.join(_A      ) + '\n'      )
              index += 1

      return (vocab_file, merges_file)





