





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

import sentencepiece as spm

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


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							'▁'

_UpperCAmelCase             =							{'vocab_file': 'sentencepiece.bpe.model'}

_UpperCAmelCase             =							{
    'vocab_file': {
        'facebook/nllb-200-distilled-600M': (
            'https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model'
        ),
    }
}

_UpperCAmelCase             =							{
    'facebook/nllb-200-distilled-600M': 1024,
}

# fmt: off
_UpperCAmelCase             =							['ace_Arab', 'ace_Latn', 'acm_Arab', 'acq_Arab', 'aeb_Arab', 'afr_Latn', 'ajp_Arab', 'aka_Latn', 'amh_Ethi', 'apc_Arab', 'arb_Arab', 'ars_Arab', 'ary_Arab', 'arz_Arab', 'asm_Beng', 'ast_Latn', 'awa_Deva', 'ayr_Latn', 'azb_Arab', 'azj_Latn', 'bak_Cyrl', 'bam_Latn', 'ban_Latn', 'bel_Cyrl', 'bem_Latn', 'ben_Beng', 'bho_Deva', 'bjn_Arab', 'bjn_Latn', 'bod_Tibt', 'bos_Latn', 'bug_Latn', 'bul_Cyrl', 'cat_Latn', 'ceb_Latn', 'ces_Latn', 'cjk_Latn', 'ckb_Arab', 'crh_Latn', 'cym_Latn', 'dan_Latn', 'deu_Latn', 'dik_Latn', 'dyu_Latn', 'dzo_Tibt', 'ell_Grek', 'eng_Latn', 'epo_Latn', 'est_Latn', 'eus_Latn', 'ewe_Latn', 'fao_Latn', 'pes_Arab', 'fij_Latn', 'fin_Latn', 'fon_Latn', 'fra_Latn', 'fur_Latn', 'fuv_Latn', 'gla_Latn', 'gle_Latn', 'glg_Latn', 'grn_Latn', 'guj_Gujr', 'hat_Latn', 'hau_Latn', 'heb_Hebr', 'hin_Deva', 'hne_Deva', 'hrv_Latn', 'hun_Latn', 'hye_Armn', 'ibo_Latn', 'ilo_Latn', 'ind_Latn', 'isl_Latn', 'ita_Latn', 'jav_Latn', 'jpn_Jpan', 'kab_Latn', 'kac_Latn', 'kam_Latn', 'kan_Knda', 'kas_Arab', 'kas_Deva', 'kat_Geor', 'knc_Arab', 'knc_Latn', 'kaz_Cyrl', 'kbp_Latn', 'kea_Latn', 'khm_Khmr', 'kik_Latn', 'kin_Latn', 'kir_Cyrl', 'kmb_Latn', 'kon_Latn', 'kor_Hang', 'kmr_Latn', 'lao_Laoo', 'lvs_Latn', 'lij_Latn', 'lim_Latn', 'lin_Latn', 'lit_Latn', 'lmo_Latn', 'ltg_Latn', 'ltz_Latn', 'lua_Latn', 'lug_Latn', 'luo_Latn', 'lus_Latn', 'mag_Deva', 'mai_Deva', 'mal_Mlym', 'mar_Deva', 'min_Latn', 'mkd_Cyrl', 'plt_Latn', 'mlt_Latn', 'mni_Beng', 'khk_Cyrl', 'mos_Latn', 'mri_Latn', 'zsm_Latn', 'mya_Mymr', 'nld_Latn', 'nno_Latn', 'nob_Latn', 'npi_Deva', 'nso_Latn', 'nus_Latn', 'nya_Latn', 'oci_Latn', 'gaz_Latn', 'ory_Orya', 'pag_Latn', 'pan_Guru', 'pap_Latn', 'pol_Latn', 'por_Latn', 'prs_Arab', 'pbt_Arab', 'quy_Latn', 'ron_Latn', 'run_Latn', 'rus_Cyrl', 'sag_Latn', 'san_Deva', 'sat_Beng', 'scn_Latn', 'shn_Mymr', 'sin_Sinh', 'slk_Latn', 'slv_Latn', 'smo_Latn', 'sna_Latn', 'snd_Arab', 'som_Latn', 'sot_Latn', 'spa_Latn', 'als_Latn', 'srd_Latn', 'srp_Cyrl', 'ssw_Latn', 'sun_Latn', 'swe_Latn', 'swh_Latn', 'szl_Latn', 'tam_Taml', 'tat_Cyrl', 'tel_Telu', 'tgk_Cyrl', 'tgl_Latn', 'tha_Thai', 'tir_Ethi', 'taq_Latn', 'taq_Tfng', 'tpi_Latn', 'tsn_Latn', 'tso_Latn', 'tuk_Latn', 'tum_Latn', 'tur_Latn', 'twi_Latn', 'tzm_Tfng', 'uig_Arab', 'ukr_Cyrl', 'umb_Latn', 'urd_Arab', 'uzn_Latn', 'vec_Latn', 'vie_Latn', 'war_Latn', 'wol_Latn', 'xho_Latn', 'ydd_Hebr', 'yor_Latn', 'yue_Hant', 'zho_Hans', 'zho_Hant', 'zul_Latn']






class        snake_case_    (		_UpperCamelCase						):
  A_      							=   VOCAB_FILES_NAMES
  A_      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  A_      							=   PRETRAINED_VOCAB_FILES_MAP
  A_      							=   ['input_ids', 'attention_mask']

  A_      							=   []
  A_      							=   []






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






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       AddedToken(_UpperCAmelCase       ,  lstrip=_UpperCAmelCase       ,  rstrip=_UpperCAmelCase      ) if isinstance(_UpperCAmelCase       ,  _UpperCAmelCase      ) else mask_token

   __lowerCAmelCase					:	Tuple      =       {} if sp_model_kwargs is None else sp_model_kwargs
   __lowerCAmelCase					:	Union[str, Any]      =       legacy_behaviour

   super().__init__(
       bos_token=_UpperCAmelCase       ,  eos_token=_UpperCAmelCase       ,  unk_token=_UpperCAmelCase       ,  sep_token=_UpperCAmelCase       ,  cls_token=_UpperCAmelCase       ,  pad_token=_UpperCAmelCase       ,  mask_token=_UpperCAmelCase       ,  tokenizer_file=_UpperCAmelCase       ,  src_lang=_UpperCAmelCase       ,  tgt_lang=_UpperCAmelCase       ,  additional_special_tokens=_UpperCAmelCase       ,  sp_model_kwargs=self.sp_model_kwargs       ,  legacy_behaviour=_UpperCAmelCase       ,  **_UpperCAmelCase       ,  )

   __lowerCAmelCase					:	List[Any]      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
   self.sp_model.Load(str(_UpperCAmelCase      )      )
   __lowerCAmelCase					:	Tuple      =       vocab_file
   # Original fairseq vocab and spm vocab must be "aligned":
   # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
   # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
   # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
   # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

   # Mimic fairseq token-to-id alignment for the first 4 token
   __lowerCAmelCase					:	Optional[Any]      =       {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

   # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
   __lowerCAmelCase					:	Optional[int]      =       1

   __lowerCAmelCase					:	Optional[int]      =       len(self.sp_model      )
   __lowerCAmelCase					:	Any      =       {
       code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCAmelCase      )
   }
   __lowerCAmelCase					:	Optional[Any]      =       {v: k for k, v in self.lang_code_to_id.items()}
   __lowerCAmelCase					:	Union[str, Any]      =       len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset

   self.fairseq_tokens_to_ids.update(self.lang_code_to_id      )
   __lowerCAmelCase					:	int      =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
   __lowerCAmelCase					:	Optional[int]      =       list(self.lang_code_to_id.keys()      )

   if additional_special_tokens is not None:
    # Only add those special tokens if they are not already there.
    self._additional_special_tokens.extend(
        [t for t in additional_special_tokens if t not in self._additional_special_tokens]      )

   __lowerCAmelCase					:	Optional[int]      =       src_lang if src_lang is not None else 'eng_Latn'
   __lowerCAmelCase					:	int      =       self.lang_code_to_id[self._src_lang]
   __lowerCAmelCase					:	Optional[Any]      =       tgt_lang
   self.set_src_lang_special_tokens(self._src_lang      )






  def __getstate__(							self			:					Any      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       self.__dict__.copy()
   __lowerCAmelCase					:	Optional[int]      =       None
   __lowerCAmelCase					:	List[Any]      =       self.sp_model.serialized_model_proto()
   return state






  def __setstate__(							self			:					List[str]       ,  _snake_case			:					Any      )->Dict:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       d

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

   __lowerCAmelCase					:	str      =       spm.SentencePieceProcessor(**self.sp_model_kwargs      )
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto      )






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->List[Any]:






   '''simple docstring'''

   return len(self.sp_model      ) + len(self.lang_code_to_id      ) + self.fairseq_offset + 1  # Plus 1 for the mask token






  @property
  def 				UpperCAmelCase__						(							self			:					Any      )->Any:






   '''simple docstring'''

   return self._src_lang






  @src_lang.setter
  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					str      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[Any]      =       new_src_lang
   self.set_src_lang_special_tokens(self._src_lang      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[int]       ,  _snake_case			:					Optional[List[int]] = None       ,  _snake_case			:					bool = False      )->int:






   '''simple docstring'''

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

   __lowerCAmelCase					:	str      =       [1] * len(self.prefix_tokens      )
   __lowerCAmelCase					:	str      =       [1] * len(self.suffix_tokens      )
   if token_ids_a is None:
    return prefix_ones + ([0] * len(_UpperCAmelCase      )) + suffix_ones
   return prefix_ones + ([0] * len(_UpperCAmelCase      )) + ([0] * len(_UpperCAmelCase      )) + suffix_ones






  def 				UpperCAmelCase__						(							self			:					List[str]       ,  _snake_case			:					List[int]       ,  _snake_case			:					Optional[List[int]] = None      )->Tuple:






   '''simple docstring'''

   if token_ids_a is None:
    return self.prefix_tokens + token_ids_a + self.suffix_tokens
   # We don't expect to process pairs, but leave the pair logic for API consistency
   return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					List[int]       ,  _snake_case			:					Optional[List[int]] = None      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Dict      =       [self.sep_token_id]
   __lowerCAmelCase					:	str      =       [self.cls_token_id]

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






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					List[str]       ,  _snake_case			:					str       ,  _snake_case			:					Optional[str]       ,  _snake_case			:					Optional[str]       ,  **_snake_case			:					Tuple      )->Tuple:






   '''simple docstring'''

   if src_lang is None or tgt_lang is None:
    raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""      )
   __lowerCAmelCase					:	str      =       src_lang
   __lowerCAmelCase					:	Tuple      =       self(_UpperCAmelCase       ,  add_special_tokens=_UpperCAmelCase       ,  return_tensors=_UpperCAmelCase       ,  **_UpperCAmelCase      )
   __lowerCAmelCase					:	Dict      =       self.convert_tokens_to_ids(_UpperCAmelCase      )
   __lowerCAmelCase					:	Dict      =       tgt_lang_id
   return inputs






  def 				UpperCAmelCase__						(							self			:					str      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       {self.convert_ids_to_tokens(_UpperCAmelCase      ): i for i in range(self.vocab_size      )}
   vocab.update(self.added_tokens_encoder      )
   return vocab






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					str      )->Tuple:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					Optional[int]      )->Optional[Any]:






   '''simple docstring'''

   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   __lowerCAmelCase					:	Tuple      =       self.sp_model.PieceToId(_UpperCAmelCase      )

   # Need to return unknown token if the SP model returned 0
   return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def 				UpperCAmelCase__						(							self			:					Optional[int]       ,  _snake_case			:					Tuple      )->Optional[Any]:






   '''simple docstring'''

   if index in self.fairseq_ids_to_tokens:
    return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset      )






  def 				UpperCAmelCase__						(							self			:					Dict       ,  _snake_case			:					Dict      )->Optional[int]:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       ''.join(_UpperCAmelCase      ).replace(_UpperCAmelCase       ,  """ """      ).strip()
   return out_string






  def 				UpperCAmelCase__						(							self			:					int       ,  _snake_case			:					str       ,  _snake_case			:					Optional[str] = None      )->Optional[int]:






   '''simple docstring'''

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

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

   return (out_vocab_file,)






  def 				UpperCAmelCase__						(							self			:					Any       ,  _snake_case			:					List[str]       ,  _snake_case			:					str = "eng_Latn"       ,  _snake_case			:					Optional[List[str]] = None       ,  _snake_case			:					str = "fra_Latn"       ,  **_snake_case			:					Union[str, Any]       ,  )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       src_lang
   __lowerCAmelCase					:	Optional[int]      =       tgt_lang
   return super().prepare_seqaseq_batch(_UpperCAmelCase       ,  _UpperCAmelCase       ,  **_UpperCAmelCase      )






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






   '''simple docstring'''

   return self.set_src_lang_special_tokens(self.src_lang      )






  def 				UpperCAmelCase__						(							self			:					int      )->Dict:






   '''simple docstring'''

   return self.set_tgt_lang_special_tokens(self.tgt_lang      )






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					Tuple      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	int      =       self.lang_code_to_id[src_lang]
   if self.legacy_behaviour:
    __lowerCAmelCase					:	Dict      =       []
    __lowerCAmelCase					:	int      =       [self.eos_token_id, self.cur_lang_code]
   else:
    __lowerCAmelCase					:	List[Any]      =       [self.cur_lang_code]
    __lowerCAmelCase					:	int      =       [self.eos_token_id]






  def 				UpperCAmelCase__						(							self			:					str       ,  _snake_case			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.lang_code_to_id[lang]
   if self.legacy_behaviour:
    __lowerCAmelCase					:	Optional[Any]      =       []
    __lowerCAmelCase					:	Any      =       [self.eos_token_id, self.cur_lang_code]
   else:
    __lowerCAmelCase					:	List[str]      =       [self.cur_lang_code]
    __lowerCAmelCase					:	Optional[Any]      =       [self.eos_token_id]






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


_UpperCAmelCase             =							'bart'
_UpperCAmelCase             =							True






@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE   )
def 	_SCREAMING_SNAKE_CASE		(      )      ->   List[Any]:
 if LOAD_DENSE_INDEX:
  __lowerCAmelCase					:	Dict      =       AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""   )
  __lowerCAmelCase					:	str      =       AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""   ).to("""cuda:0"""   )
  __lowerCAmelCase					:	Tuple      =       qar_model.eval()
 else:
  __lowerCAmelCase ,					__lowerCAmelCase					:	str      =       (None, None)
 if MODEL_TYPE == "bart":
  __lowerCAmelCase					:	Optional[Any]      =       AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""   )
  __lowerCAmelCase					:	str      =       AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""   ).to("""cuda:0"""   )
  __lowerCAmelCase					:	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					:	Optional[int]      =       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=SCREAMING_SNAKE_CASE   )
def 	_SCREAMING_SNAKE_CASE		(      )      ->   Tuple:
 if LOAD_DENSE_INDEX:
  __lowerCAmelCase					:	List[str]      =       faiss.StandardGpuResources()
  __lowerCAmelCase					:	Optional[int]      =       datasets.load_dataset(path="""wiki_snippets"""   ,   name="""wiki40b_en_100_0"""   )["""train"""]
  __lowerCAmelCase					:	Optional[int]      =       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					:	int      =       faiss.IndexFlatIP(128   )
  __lowerCAmelCase					:	Optional[int]      =       faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE   ,   1   ,   SCREAMING_SNAKE_CASE   )
  wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE   )  # TODO fix for larger GPU
 else:
  __lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       (None, None)
 __lowerCAmelCase					:	List[Any]      =       Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]   )
 return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)






@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE   )
def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[Any]:
 __lowerCAmelCase					:	Tuple      =       datasets.load_dataset("""eli5"""   ,   name="""LFQA_reddit"""   )
 __lowerCAmelCase					:	int      =       elia["""train_eli5"""]
 __lowerCAmelCase					:	int      =       np.memmap(
     """eli5_questions_reps.dat"""   ,   dtype="""float32"""   ,   mode="""r"""   ,   shape=(elia_train.num_rows, 128)   )
 __lowerCAmelCase					:	List[Any]      =       faiss.IndexFlatIP(128   )
 eli5_train_q_index.add(SCREAMING_SNAKE_CASE   )
 return (elia_train, eli5_train_q_index)


_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase             =							load_indexes()
_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase             =							load_models()
_UpperCAmelCase       ,     _UpperCAmelCase             =							load_train_data()






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[Any]   ,   SCREAMING_SNAKE_CASE		:List[str]=10   )      ->   List[str]:
 __lowerCAmelCase					:	Optional[Any]      =       embed_questions_for_retrieval([question]   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase ,					__lowerCAmelCase					:	Dict      =       eli5_train_q_index.search(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	str      =       [elia_train[int(SCREAMING_SNAKE_CASE   )] for i in I[0]]
 return nn_examples






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Dict="wiki40b"   ,   SCREAMING_SNAKE_CASE		:List[Any]="dense"   ,   SCREAMING_SNAKE_CASE		:List[str]=10   )      ->   str:
 if source == "none":
  __lowerCAmelCase ,					__lowerCAmelCase					:	Any      =       (""" <P> """.join(["""""" for _ in range(11   )]   ).strip(), [])
 else:
  if method == "dense":
   __lowerCAmelCase ,					__lowerCAmelCase					:	Union[str, Any]      =       query_qa_dense_index(
       SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
  else:
   __lowerCAmelCase ,					__lowerCAmelCase					:	Optional[Any]      =       query_es_index(
       SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   index_name="""english_wiki40b_snippets_100w"""   ,   n_results=SCREAMING_SNAKE_CASE   ,   )
 __lowerCAmelCase					:	Union[str, Any]      =       [
     (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
 ]
 __lowerCAmelCase					:	str      =       """question: {} context: {}""".format(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 return question_doc, support_list






@st.cache(
    hash_funcs={
        torch.Tensor: (lambda SCREAMING_SNAKE_CASE   : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE   : None),
    }   )
def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:Optional[int]   ,   SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:Dict   ,   SCREAMING_SNAKE_CASE		:int=64   ,   SCREAMING_SNAKE_CASE		:int=256   ,   SCREAMING_SNAKE_CASE		:Tuple=False   ,   SCREAMING_SNAKE_CASE		:Union[str, Any]=2   ,   SCREAMING_SNAKE_CASE		:int=0.95   ,   SCREAMING_SNAKE_CASE		:Any=0.8   )      ->   str:
 with torch.no_grad():
  __lowerCAmelCase					:	Any      =       qa_sas_generate(
      SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   num_answers=1   ,   num_beams=SCREAMING_SNAKE_CASE   ,   min_len=SCREAMING_SNAKE_CASE   ,   max_len=SCREAMING_SNAKE_CASE   ,   do_sample=SCREAMING_SNAKE_CASE   ,   temp=SCREAMING_SNAKE_CASE   ,   top_p=SCREAMING_SNAKE_CASE   ,   top_k=SCREAMING_SNAKE_CASE   ,   max_input_length=1_024   ,   device="""cuda:0"""   ,   )[0]
 return (answer, support_list)


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

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

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

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

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

_UpperCAmelCase             =							'beam'
_UpperCAmelCase             =							2
_UpperCAmelCase             =							64
_UpperCAmelCase             =							256
_UpperCAmelCase             =							None
_UpperCAmelCase             =							None
_UpperCAmelCase             =							st.sidebar.checkbox('Generation options')
if generate_options:
      _UpperCAmelCase             =							'\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder\'s output probabilities.\n    '
      st.sidebar.markdown(generate_info)
      _UpperCAmelCase             =							st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
      _UpperCAmelCase             =							st.sidebar.slider(
          'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
      )
      _UpperCAmelCase             =							st.sidebar.slider(
          'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
      )
      if sampled == "beam":
            _UpperCAmelCase             =							st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
      else:
            _UpperCAmelCase             =							st.sidebar.slider(
                'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
            )
            _UpperCAmelCase             =							st.sidebar.slider(
                'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
            )
            _UpperCAmelCase             =							None

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

if st.button('Show me!'):
      if action in [0, 1, 3]:
            if index_type == "mixed":
                  _UpperCAmelCase       ,     _UpperCAmelCase             =							make_support(question, source=wiki_source, method='dense', n_results=10)
                  _UpperCAmelCase       ,     _UpperCAmelCase             =							make_support(question, source=wiki_source, method='sparse', n_results=10)
                  _UpperCAmelCase             =							[]
                  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)]
                  _UpperCAmelCase             =							support_list[:10]
                  _UpperCAmelCase             =							'<P> ' + ' <P> '.join([res[-1] for res in support_list])
            else:
                  _UpperCAmelCase       ,     _UpperCAmelCase             =							make_support(question, source=wiki_source, method=index_type, n_results=10)
      if action in [0, 3]:
            _UpperCAmelCase       ,     _UpperCAmelCase             =							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):
                  _UpperCAmelCase             =							'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
                  _UpperCAmelCase             =							res[1].strip()
                  if sec_titles == "":
                        _UpperCAmelCase             =							'[{}]({})'.format(res[0], wiki_url)
                  else:
                        _UpperCAmelCase             =							sec_titles.split(' & ')
                        _UpperCAmelCase             =							' & '.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]:
            _UpperCAmelCase             =							find_nearest_training(question)
            _UpperCAmelCase             =							nn_train_list[0]
            st.markdown(
                '--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
            )
            _UpperCAmelCase             =							[
                '{}. {}'.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)))


_UpperCAmelCase             =							'\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'







st.sidebar.markdown(disclaimer, unsafe_allow_html=True)