



import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
 import transformers





def      __snake_case	(			_UpperCAmelCase				):
    random.seed(_UpperCAmelCase				)
    np.random.seed(_UpperCAmelCase				)
    torch.manual_seed(_UpperCAmelCase				)
    torch.cuda.manual_seed_all(_UpperCAmelCase				)
    # ^^ safe to call this function even if cuda is not available

class     _A       :



       def __init__(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Iterable[torch.nn.Parameter]     ,		__SCREAMING_SNAKE_CASE							:     float = 0.99_99     ,		__SCREAMING_SNAKE_CASE							:     float = 0.0     ,		__SCREAMING_SNAKE_CASE							:     int = 0     ,		__SCREAMING_SNAKE_CASE							:     bool = False     ,		__SCREAMING_SNAKE_CASE							:     Union[float, int] = 1.0     ,		__SCREAMING_SNAKE_CASE							:     Union[float, int] = 2 / 3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any] = None     ,		__SCREAMING_SNAKE_CASE							:     Dict[str, Any] = None     ,		**__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		torch.nn.Module):
               __a						       =				(
                   '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
                   '''Please pass the parameters of the module instead.'''
               )
               deprecate(
                   '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''     ,		'''1.0.0'''     ,		__SCREAMING_SNAKE_CASE     ,		standard_warn=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				parameters.parameters()

               # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
               __a						       =				True

           if kwargs.get('''max_value'''     ,		__SCREAMING_SNAKE_CASE) is not None:
               __a						       =				'''The `max_value` argument is deprecated. Please use `decay` instead.'''
               deprecate('''max_value'''     ,		'''1.0.0'''     ,		__SCREAMING_SNAKE_CASE     ,		standard_warn=__SCREAMING_SNAKE_CASE)
               __a						       =				kwargs['''max_value''']

           if kwargs.get('''min_value'''     ,		__SCREAMING_SNAKE_CASE) is not None:
               __a						       =				'''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
               deprecate('''min_value'''     ,		'''1.0.0'''     ,		__SCREAMING_SNAKE_CASE     ,		standard_warn=__SCREAMING_SNAKE_CASE)
               __a						       =				kwargs['''min_value''']

           __a						       =				list(__SCREAMING_SNAKE_CASE)
           __a						       =				[p.clone().detach() for p in parameters]

           if kwargs.get('''device'''     ,		__SCREAMING_SNAKE_CASE) is not None:
               __a						       =				'''The `device` argument is deprecated. Please use `to` instead.'''
               deprecate('''device'''     ,		'''1.0.0'''     ,		__SCREAMING_SNAKE_CASE     ,		standard_warn=__SCREAMING_SNAKE_CASE)
               self.to(device=kwargs['''device'''])

           __a						       =				None

           __a						       =				decay
           __a						       =				min_decay
           __a						       =				update_after_step
           __a						       =				use_ema_warmup
           __a						       =				inv_gamma
           __a						       =				power
           __a						       =				0
           __a						       =				None  # set in `step()`

           __a						       =				model_cls
           __a						       =				model_config



       @classmethod
       def 		_lowerCamelCase			(		cls							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           __a       ,   __a						       =				model_cls.load_config(__SCREAMING_SNAKE_CASE     ,		return_unused_kwargs=__SCREAMING_SNAKE_CASE)
           __a						       =				model_cls.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a						       =				cls(model.parameters()     ,		model_cls=__SCREAMING_SNAKE_CASE     ,		model_config=model.config)

           ema_model.load_state_dict(__SCREAMING_SNAKE_CASE)
           return ema_model



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


           '''simple docstring'''

           if self.model_cls is None:
               raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.''')

           if self.model_config is None:
               raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.''')

           __a						       =				self.model_cls.from_config(self.model_config)
           __a						       =				self.state_dict()
           state_dict.pop('''shadow_params'''     ,		__SCREAMING_SNAKE_CASE)

           model.register_to_config(**__SCREAMING_SNAKE_CASE)
           self.copy_to(model.parameters())
           model.save_pretrained(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           __a						       =				max(0     ,		optimization_step - self.update_after_step - 1)

           if step <= 0:
               return 0.0

           if self.use_ema_warmup:
               __a						       =				1 - (1 + step / self.inv_gamma) ** -self.power
           else:
               __a						       =				(1 + step) / (10 + step)

           __a						       =				min(__SCREAMING_SNAKE_CASE     ,		self.decay)
           # make sure decay is not smaller than min_decay
           __a						       =				max(__SCREAMING_SNAKE_CASE     ,		self.min_decay)
           return cur_decay_value



       @torch.no_grad()
       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Iterable[torch.nn.Parameter]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		torch.nn.Module):
               __a						       =				(
                   '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
                   '''Please pass the parameters of the module instead.'''
               )
               deprecate(
                   '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''     ,		'''1.0.0'''     ,		__SCREAMING_SNAKE_CASE     ,		standard_warn=__SCREAMING_SNAKE_CASE     ,		)
               __a						       =				parameters.parameters()

           __a						       =				list(__SCREAMING_SNAKE_CASE)

           self.optimization_step += 1

           # Compute the decay factor for the exponential moving average.
           __a						       =				self.get_decay(self.optimization_step)
           __a						       =				decay
           __a						       =				1 - decay

           __a						       =				contextlib.nullcontext
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
               import deepspeed

           for s_param, param in zip(self.shadow_params     ,		__SCREAMING_SNAKE_CASE):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   __a						       =				deepspeed.zero.GatheredParameters(__SCREAMING_SNAKE_CASE     ,		modifier_rank=__SCREAMING_SNAKE_CASE)

               with context_manager():
                   if param.requires_grad:
                       s_param.sub_(one_minus_decay * (s_param - param))
                   else:
                       s_param.copy_(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Iterable[torch.nn.Parameter]):


           '''simple docstring'''

           __a						       =				list(__SCREAMING_SNAKE_CASE)
           for s_param, param in zip(self.shadow_params     ,		__SCREAMING_SNAKE_CASE):
               param.data.copy_(s_param.to(param.device).data)



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


           '''simple docstring'''

           __a						       =				[
               p.to(device=__SCREAMING_SNAKE_CASE     ,		dtype=__SCREAMING_SNAKE_CASE) if p.is_floating_point() else p.to(device=__SCREAMING_SNAKE_CASE)
               for p in self.shadow_params
           ]



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           return {
               "decay": self.decay,
               "min_decay": self.min_decay,
               "optimization_step": self.optimization_step,
               "update_after_step": self.update_after_step,
               "use_ema_warmup": self.use_ema_warmup,
               "inv_gamma": self.inv_gamma,
               "power": self.power,
               "shadow_params": self.shadow_params,
           }



       def 		_lowerCamelCase			(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Iterable[torch.nn.Parameter]):


           '''simple docstring'''

           __a						       =				[param.detach().cpu().clone() for param in parameters]



       def 		_lowerCamelCase			(		self							:     Tuple     ,		__SCREAMING_SNAKE_CASE							:     Iterable[torch.nn.Parameter]):


           '''simple docstring'''

           if self.temp_stored_params is None:
               raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`''')
           for c_param, param in zip(self.temp_stored_params     ,		__SCREAMING_SNAKE_CASE):
               param.data.copy_(c_param.data)

           # Better memory-wise.
           __a						       =				None





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


           '''simple docstring'''

           __a						       =				copy.deepcopy(__SCREAMING_SNAKE_CASE)

           __a						       =				state_dict.get('''decay'''     ,		self.decay)
           if self.decay < 0.0 or self.decay > 1.0:
               raise ValueError('''Decay must be between 0 and 1''')

           __a						       =				state_dict.get('''min_decay'''     ,		self.min_decay)
           if not isinstance(self.min_decay     ,		__SCREAMING_SNAKE_CASE):
               raise ValueError('''Invalid min_decay''')

           __a						       =				state_dict.get('''optimization_step'''     ,		self.optimization_step)
           if not isinstance(self.optimization_step     ,		__SCREAMING_SNAKE_CASE):
               raise ValueError('''Invalid optimization_step''')

           __a						       =				state_dict.get('''update_after_step'''     ,		self.update_after_step)
           if not isinstance(self.update_after_step     ,		__SCREAMING_SNAKE_CASE):
               raise ValueError('''Invalid update_after_step''')

           __a						       =				state_dict.get('''use_ema_warmup'''     ,		self.use_ema_warmup)
           if not isinstance(self.use_ema_warmup     ,		__SCREAMING_SNAKE_CASE):
               raise ValueError('''Invalid use_ema_warmup''')

           __a						       =				state_dict.get('''inv_gamma'''     ,		self.inv_gamma)
           if not isinstance(self.inv_gamma     ,		(float, int)):
               raise ValueError('''Invalid inv_gamma''')

           __a						       =				state_dict.get('''power'''     ,		self.power)
           if not isinstance(self.power     ,		(float, int)):
               raise ValueError('''Invalid power''')

           __a						       =				state_dict.get('''shadow_params'''     ,		__SCREAMING_SNAKE_CASE)
           if shadow_params is not None:
               __a						       =				shadow_params
               if not isinstance(self.shadow_params     ,		__SCREAMING_SNAKE_CASE):
                   raise ValueError('''shadow_params must be a list''')
               if not all(isinstance(__SCREAMING_SNAKE_CASE     ,		torch.Tensor) for p in self.shadow_params):
                   raise ValueError('''shadow_params must all be Tensors''')










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						:Dict      		=     '''bart'''
__snake_case						:Tuple      		=     True





@st.cache(allow_output_mutation=_UpperCAmelCase				)
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       ,   __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       ,   __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=_UpperCAmelCase				)
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(_UpperCAmelCase   , 1   , _UpperCAmelCase				)
        wikiaab_gpu_index_flat.add(_UpperCAmelCase				)  # TODO fix for larger GPU
    else:
        __a       ,   __a						       =				(None, None)
    __a						       =				Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]				)
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)





@st.cache(allow_output_mutation=_UpperCAmelCase				)
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(_UpperCAmelCase				)
    return (elia_train, eli5_train_q_index)


__snake_case		,__snake_case		,__snake_case						:List[str]      		=     load_indexes()
__snake_case		,__snake_case		,__snake_case		,__snake_case						:Dict      		=     load_models()
__snake_case		,__snake_case						:Tuple      		=     load_train_data()





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





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase="wiki40b"   , _UpperCAmelCase="dense"   , _UpperCAmelCase=10				):
    if source == "none":
        __a       ,   __a						       =				(''' <P> '''.join(['''''' for _ in range(11				)]				).strip(), [])
    else:
        if method == "dense":
            __a       ,   __a						       =				query_qa_dense_index(
                _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)
        else:
            __a       ,   __a						       =				query_es_index(
                _UpperCAmelCase   , _UpperCAmelCase   , index_name='''english_wiki40b_snippets_100w'''   , n_results=_UpperCAmelCase   , )
    __a						       =				[
        (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
    ]
    __a						       =				'''question: {} context: {}'''.format(_UpperCAmelCase   , _UpperCAmelCase				)
    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(
            _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , num_answers=1   , num_beams=_UpperCAmelCase   , min_len=_UpperCAmelCase   , max_len=_UpperCAmelCase   , do_sample=_UpperCAmelCase   , temp=_UpperCAmelCase   , top_p=_UpperCAmelCase   , top_k=_UpperCAmelCase   , max_input_length=1024   , device='''cuda:0'''   , )[0]
    return (answer, support_list)


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

# Start sidebar
__snake_case						:Dict      		=     '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__snake_case						:int      		=     '''
<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						:int      		=     '''
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						:Union[str, Any]      		=     [
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
__snake_case						:int      		=     st.sidebar.checkbox('''Demo options''')
if demo_options:
 __snake_case						:str      		=     st.sidebar.selectbox(
     '''''',
     action_list,
     index=3,
 )
 __snake_case						:Tuple      		=     action_list.index(action_st)
 __snake_case						:Optional[int]      		=     st.sidebar.selectbox(
     '''''',
     ['''Show full text of passages''', '''Show passage section titles'''],
     index=0,
 )
 __snake_case						:Dict      		=     show_type == '''Show full text of passages'''
else:
 __snake_case						:Dict      		=     3
 __snake_case						:str      		=     True

__snake_case						:Optional[Any]      		=     st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 __snake_case						:List[str]      		=     '''
    ### 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						:Dict      		=     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						:Optional[int]      		=     '''wiki40b'''
 __snake_case						:Dict      		=     '''dense'''

__snake_case						:Dict      		=     '''beam'''
__snake_case						:int      		=     2
__snake_case						:str      		=     64
__snake_case						:Tuple      		=     256
__snake_case						:int      		=     None
__snake_case						:List[Any]      		=     None
__snake_case						:int      		=     st.sidebar.checkbox('''Generation options''')
if generate_options:
 __snake_case						:Tuple      		=     '''
    ### 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						:Tuple      		=     st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
 __snake_case						:Dict      		=     st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 __snake_case						:Dict      		=     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						:List[str]      		=     st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  __snake_case						:Tuple      		=     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						: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						:Any      		=     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						:int      		=     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						:Optional[int]      		=     question_s

if st.button('''Show me!'''):
 if action in [0, 1, 3]:
  if index_type == "mixed":
   __snake_case		,__snake_case						:int      		=     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						:Optional[Any]      		=     []
   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						:Union[str, Any]      		=     support_list[:10]
   __snake_case						:Optional[int]      		=     '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
  else:
   __snake_case		,__snake_case						:Tuple      		=     make_support(question, source=wiki_source, method=index_type, n_results=10)
 if action in [0, 3]:
  __snake_case		,__snake_case						:Optional[int]      		=     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						:Dict      		=     '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
   __snake_case						:int      		=     res[1].strip()
   if sec_titles == "":
    __snake_case						:List[Any]      		=     '''[{}]({})'''.format(res[0], wiki_url)
   else:
    __snake_case						:Optional[int]      		=     sec_titles.split(''' & ''')
    __snake_case						:str      		=     ''' & '''.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						:str      		=     find_nearest_training(question)
  __snake_case						:str      		=     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						:Optional[Any]      		=     [
      '''{}. {}'''.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)





