



'''simple docstring'''





import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


__a          =		re.compile(R"\b(a|an|the)\b", re.UNICODE)

__a          =		None





def 						__snake_case(				)		-> Any:
      snake_case__  :    Any    						=      argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0."""		)
      parser.add_argument("""data_file"""     ,   metavar="""data.json"""     ,   help="""Input data JSON file."""		)
      parser.add_argument("""pred_file"""     ,   metavar="""pred.json"""     ,   help="""Model predictions."""		)
      parser.add_argument(
          """--out-file"""     ,   """-o"""     ,   metavar="""eval.json"""     ,   help="""Write accuracy metrics to file (default is stdout)."""		)
      parser.add_argument(
          """--na-prob-file"""     ,   """-n"""     ,   metavar="""na_prob.json"""     ,   help="""Model estimates of probability of no answer."""		)
      parser.add_argument(
          """--na-prob-thresh"""     ,   """-t"""     ,   type=_lowerCAmelCase     ,   default=1.0     ,   help="""Predict \"\" if no-answer probability exceeds this (default = 1.0)."""     ,   )
      parser.add_argument(
          """--out-image-dir"""     ,   """-p"""     ,   metavar="""out_images"""     ,   default=_lowerCAmelCase     ,   help="""Save precision-recall curves to directory."""		)
      parser.add_argument("""--verbose"""     ,   """-v"""     ,   action="""store_true"""		)
      if len(sys.argv		) == 1:
            parser.print_help()
            sys.exit(1		)
      return parser.parse_args()





def 						__snake_case(				_lowerCAmelCase		)		-> List[Any]:
      snake_case__  :    Any    						=      {}
      for article in dataset:
            for p in article["paragraphs"]:
                  for qa in p["qas"]:
                        snake_case__  :    List[str]    						=      bool(qa["""answers"""]["""text"""]		)
      return qid_to_has_ans





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:

      def remove_articles(_lowerCAmelCase		):
            return ARTICLES_REGEX.sub(""" """     ,   _lowerCAmelCase		)

      def white_space_fix(_lowerCAmelCase		):
            return " ".join(text.split()		)

      def remove_punc(_lowerCAmelCase		):
            snake_case__  :    Optional[int]    						=      set(string.punctuation		)
            return "".join(ch for ch in text if ch not in exclude		)

      def lower(_lowerCAmelCase		):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(_lowerCAmelCase		)		)		)		)





def 						__snake_case(				_lowerCAmelCase		)		-> Dict:
      if not s:
            return []
      return normalize_answer(_lowerCAmelCase		).split()





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      return int(normalize_answer(_lowerCAmelCase		) == normalize_answer(_lowerCAmelCase		)		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      snake_case__  :    Union[str, Any]    						=      get_tokens(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      get_tokens(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      collections.Counter(_lowerCAmelCase		) & collections.Counter(_lowerCAmelCase		)
      snake_case__  :    int    						=      sum(common.values()		)
      if len(_lowerCAmelCase		) == 0 or len(_lowerCAmelCase		) == 0:
            # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
            return int(gold_toks == pred_toks		)
      if num_same == 0:
            return 0
      snake_case__  :    List[Any]    						=      1.0 * num_same / len(_lowerCAmelCase		)
      snake_case__  :    Any    						=      1.0 * num_same / len(_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      (2 * precision * recall) / (precision + recall)
      return fa





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Optional[Any]    						=      {}
      snake_case__  :    Any    						=      {}
      for article in dataset:
            for p in article["paragraphs"]:
                  for qa in p["qas"]:
                        snake_case__  :    Optional[int]    						=      qa["""id"""]
                        snake_case__  :    Optional[Any]    						=      [t for t in qa["""answers"""]["""text"""] if normalize_answer(_lowerCAmelCase		)]
                        if not gold_answers:
                              # For unanswerable questions, only correct answer is empty string
                              snake_case__  :    Optional[int]    						=      [""""""]
                        if qid not in preds:
                              print(f"Missing prediction for {qid}"		)
                              continue
                        snake_case__  :    Tuple    						=      preds[qid]
                        # Take max over all gold answers
                        snake_case__  :    str    						=      max(compute_exact(_lowerCAmelCase     ,   _lowerCAmelCase		) for a in gold_answers		)
                        snake_case__  :    Any    						=      max(compute_fa(_lowerCAmelCase     ,   _lowerCAmelCase		) for a in gold_answers		)
      return exact_scores, fa_scores





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      snake_case__  :    Optional[int]    						=      {}
      for qid, s in scores.items():
            snake_case__  :    Optional[Any]    						=      na_probs[qid] > na_prob_thresh
            if pred_na:
                  snake_case__  :    Optional[int]    						=      float(not qid_to_has_ans[qid]		)
            else:
                  snake_case__  :    Union[str, Any]    						=      s
      return new_scores





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=None		)		-> List[str]:
      if not qid_list:
            snake_case__  :    str    						=      len(_lowerCAmelCase		)
            return collections.OrderedDict(
                [
                    ("""exact""", 100.0 * sum(exact_scores.values()		) / total),
                    ("""f1""", 100.0 * sum(fa_scores.values()		) / total),
                    ("""total""", total),
                ]		)
      else:
            snake_case__  :    int    						=      len(_lowerCAmelCase		)
            return collections.OrderedDict(
                [
                    ("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list		) / total),
                    ("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list		) / total),
                    ("""total""", total),
                ]		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Dict:
      for k in new_eval:
            snake_case__  :    str    						=      new_eval[k]





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> int:
      plt.step(_lowerCAmelCase     ,   _lowerCAmelCase     ,   color="""b"""     ,   alpha=0.2     ,   where="""post"""		)
      plt.fill_between(_lowerCAmelCase     ,   _lowerCAmelCase     ,   step="""post"""     ,   alpha=0.2     ,   color="""b"""		)
      plt.xlabel("""Recall"""		)
      plt.ylabel("""Precision"""		)
      plt.xlim([0.0, 1.05]		)
      plt.ylim([0.0, 1.05]		)
      plt.title(_lowerCAmelCase		)
      plt.savefig(_lowerCAmelCase		)
      plt.clf()





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase=None     ,   _lowerCAmelCase=None		)		-> Any:
      snake_case__  :    Optional[int]    						=      sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: na_probs[k]		)
      snake_case__  :    str    						=      0.0
      snake_case__  :    Dict    						=      1.0
      snake_case__  :    List[str]    						=      0.0
      snake_case__  :    Dict    						=      [1.0]
      snake_case__  :    int    						=      [0.0]
      snake_case__  :    str    						=      0.0
      for i, qid in enumerate(_lowerCAmelCase		):
            if qid_to_has_ans[qid]:
                  true_pos += scores[qid]
            snake_case__  :    Dict    						=      true_pos / float(i + 1		)
            snake_case__  :    Union[str, Any]    						=      true_pos / float(_lowerCAmelCase		)
            if i == len(_lowerCAmelCase		) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
                  # i.e., if we can put a threshold after this point
                  avg_prec += cur_p * (cur_r - recalls[-1])
                  precisions.append(_lowerCAmelCase		)
                  recalls.append(_lowerCAmelCase		)
      if out_image:
            plot_pr_curve(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      return {"ap": 100.0 * avg_prec}





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Union[str, Any]:
      if out_image_dir and not os.path.exists(_lowerCAmelCase		):
            os.makedirs(_lowerCAmelCase		)
      snake_case__  :    Optional[Any]    						=      sum(1 for v in qid_to_has_ans.values() if v		)
      if num_true_pos == 0:
            return
      snake_case__  :    Any    						=      make_precision_recall_eval(
          _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   out_image=os.path.join(_lowerCAmelCase     ,   """pr_exact.png"""		)     ,   title="""Precision-Recall curve for Exact Match score"""     ,   )
      snake_case__  :    Optional[Any]    						=      make_precision_recall_eval(
          _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   out_image=os.path.join(_lowerCAmelCase     ,   """pr_f1.png"""		)     ,   title="""Precision-Recall curve for F1 score"""     ,   )
      snake_case__  :    Any    						=      {k: float(_lowerCAmelCase		) for k, v in qid_to_has_ans.items()}
      snake_case__  :    Union[str, Any]    						=      make_precision_recall_eval(
          _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   out_image=os.path.join(_lowerCAmelCase     ,   """pr_oracle.png"""		)     ,   title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)"""     ,   )
      merge_eval(_lowerCAmelCase     ,   _lowerCAmelCase     ,   """pr_exact"""		)
      merge_eval(_lowerCAmelCase     ,   _lowerCAmelCase     ,   """pr_f1"""		)
      merge_eval(_lowerCAmelCase     ,   _lowerCAmelCase     ,   """pr_oracle"""		)





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      if not qid_list:
            return
      snake_case__  :    Optional[Any]    						=      [na_probs[k] for k in qid_list]
      snake_case__  :    Union[str, Any]    						=      np.ones_like(_lowerCAmelCase		) / float(len(_lowerCAmelCase		)		)
      plt.hist(_lowerCAmelCase     ,   weights=_lowerCAmelCase     ,   bins=20     ,   range=(0.0, 1.0)		)
      plt.xlabel("""Model probability of no-answer"""		)
      plt.ylabel("""Proportion of dataset"""		)
      plt.title(f"Histogram of no-answer probability: {name}"		)
      plt.savefig(os.path.join(_lowerCAmelCase     ,   f"na_prob_hist_{name}.png"		)		)
      plt.clf()





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    List[str]    						=      sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]		)
      snake_case__  :    Union[str, Any]    						=      num_no_ans
      snake_case__  :    Optional[int]    						=      cur_score
      snake_case__  :    Optional[Any]    						=      0.0
      snake_case__  :    Optional[int]    						=      sorted(_lowerCAmelCase     ,   key=lambda _lowerCAmelCase		: na_probs[k]		)
      for i, qid in enumerate(_lowerCAmelCase		):
            if qid not in scores:
                  continue
            if qid_to_has_ans[qid]:
                  snake_case__  :    List[str]    						=      scores[qid]
            else:
                  if preds[qid]:
                        snake_case__  :    Tuple    						=      -1
                  else:
                        snake_case__  :    List[Any]    						=      0
            cur_score += diff
            if cur_score > best_score:
                  snake_case__  :    Optional[Any]    						=      cur_score
                  snake_case__  :    Union[str, Any]    						=      na_probs[qid]
      return 100.0 * best_score / len(_lowerCAmelCase		), best_thresh





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)		-> Tuple:
      snake_case__						,				snake_case__  :    List[str]    						=      find_best_thresh(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      snake_case__						,				snake_case__  :    int    						=      find_best_thresh(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      snake_case__  :    List[Any]    						=      best_exact
      snake_case__  :    Dict    						=      exact_thresh
      snake_case__  :    List[Any]    						=      best_fa
      snake_case__  :    Dict    						=      fa_thresh





def 						__snake_case(				)		-> Optional[int]:
      with open(OPTS.data_file		) as f:
            snake_case__  :    Union[str, Any]    						=      json.load(_lowerCAmelCase		)
            snake_case__  :    Dict    						=      dataset_json["""data"""]
      with open(OPTS.pred_file		) as f:
            snake_case__  :    int    						=      json.load(_lowerCAmelCase		)
      if OPTS.na_prob_file:
            with open(OPTS.na_prob_file		) as f:
                  snake_case__  :    Tuple    						=      json.load(_lowerCAmelCase		)
      else:
            snake_case__  :    List[Any]    						=      {k: 0.0 for k in preds}
      snake_case__  :    List[Any]    						=      make_qid_to_has_ans(_lowerCAmelCase		)  # maps qid to True/False
      snake_case__  :    Optional[int]    						=      [k for k, v in qid_to_has_ans.items() if v]
      snake_case__  :    str    						=      [k for k, v in qid_to_has_ans.items() if not v]
      snake_case__						,				snake_case__  :    int    						=      get_raw_scores(_lowerCAmelCase     ,   _lowerCAmelCase		)
      snake_case__  :    Dict    						=      apply_no_ans_threshold(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   OPTS.na_prob_thresh		)
      snake_case__  :    Optional[Any]    						=      apply_no_ans_threshold(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   OPTS.na_prob_thresh		)
      snake_case__  :    List[Any]    						=      make_eval_dict(_lowerCAmelCase     ,   _lowerCAmelCase		)
      if has_ans_qids:
            snake_case__  :    Optional[Any]    						=      make_eval_dict(_lowerCAmelCase     ,   _lowerCAmelCase     ,   qid_list=_lowerCAmelCase		)
            merge_eval(_lowerCAmelCase     ,   _lowerCAmelCase     ,   """HasAns"""		)
      if no_ans_qids:
            snake_case__  :    str    						=      make_eval_dict(_lowerCAmelCase     ,   _lowerCAmelCase     ,   qid_list=_lowerCAmelCase		)
            merge_eval(_lowerCAmelCase     ,   _lowerCAmelCase     ,   """NoAns"""		)
      if OPTS.na_prob_file:
            find_all_best_thresh(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      if OPTS.na_prob_file and OPTS.out_image_dir:
            run_precision_recall_analysis(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase     ,   OPTS.out_image_dir		)
            histogram_na_prob(_lowerCAmelCase     ,   _lowerCAmelCase     ,   OPTS.out_image_dir     ,   """hasAns"""		)
            histogram_na_prob(_lowerCAmelCase     ,   _lowerCAmelCase     ,   OPTS.out_image_dir     ,   """noAns"""		)
      if OPTS.out_file:
            with open(OPTS.out_file     ,   """w"""		) as f:
                  json.dump(_lowerCAmelCase     ,   _lowerCAmelCase		)
      else:
            print(json.dumps(_lowerCAmelCase     ,   indent=2		)		)


if __name__ == "__main__":
       __a          =		parse_args()
       if OPTS.out_image_dir:
              import matplotlib

              matplotlib.use("Agg")
              import matplotlib.pyplot as plt
       main()




"""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


snake_case__						:   List[Any]			 = '''bart'''
snake_case__						:   Union[str, Any]			 = True


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      if LOAD_DENSE_INDEX:
            lowerCAmelCase    :		Dict				=      AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''	)
            lowerCAmelCase    :		List[str]				=      AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''	).to('''cuda:0'''	)
            lowerCAmelCase    :		Optional[int]				=      qar_model.eval()
      else:
            lowerCAmelCase,					lowerCAmelCase    :		int				=      (None, None)
      if MODEL_TYPE == "bart":
            lowerCAmelCase    :		Tuple				=      AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''	)
            lowerCAmelCase    :		Tuple				=      AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''	).to('''cuda:0'''	)
            lowerCAmelCase    :		Optional[Any]				=      torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''	)
            sas_model.load_state_dict(save_dict['''model''']	)
            lowerCAmelCase    :		Any				=      sas_model.eval()
      else:
            lowerCAmelCase,					lowerCAmelCase    :		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=_snake_case	)
def    _snake_case  (  ):
      if LOAD_DENSE_INDEX:
            lowerCAmelCase    :		List[str]				=      faiss.StandardGpuResources()
            lowerCAmelCase    :		Optional[Any]				=      datasets.load_dataset(path='''wiki_snippets'''       ,      name='''wiki40b_en_100_0'''	)['''train''']
            lowerCAmelCase    :		List[Any]				=      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    :		Union[str, Any]				=      faiss.IndexFlatIP(128	)
            lowerCAmelCase    :		int				=      faiss.index_cpu_to_gpu(_snake_case       ,      1       ,      _snake_case	)
            wikiaab_gpu_index_flat.add(_snake_case	)  # TODO fix for larger GPU
      else:
            lowerCAmelCase,					lowerCAmelCase    :		List[str]				=      (None, None)
      lowerCAmelCase    :		int				=      Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]	)
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=_snake_case	)
def    _snake_case  (  ):
      lowerCAmelCase    :		List[str]				=      datasets.load_dataset('''eli5'''       ,      name='''LFQA_reddit'''	)
      lowerCAmelCase    :		Any				=      elia['''train_eli5''']
      lowerCAmelCase    :		int				=      np.memmap(
          '''eli5_questions_reps.dat'''       ,      dtype='''float32'''       ,      mode='''r'''       ,      shape=(elia_train.num_rows, 128)	)
      lowerCAmelCase    :		Tuple				=      faiss.IndexFlatIP(128	)
      eli5_train_q_index.add(_snake_case	)
      return (elia_train, eli5_train_q_index)


snake_case__ ,   snake_case__ ,   snake_case__						:   Optional[Any]			 = load_indexes()
snake_case__ ,   snake_case__ ,   snake_case__ ,   snake_case__						:   str			 = load_models()
snake_case__ ,   snake_case__						:   Union[str, Any]			 = load_train_data()


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Dict=10	):
      lowerCAmelCase    :		Tuple				=      embed_questions_for_retrieval([question]       ,      _snake_case       ,      _snake_case	)
      lowerCAmelCase,					lowerCAmelCase    :		Any				=      eli5_train_q_index.search(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		str				=      [elia_train[int(_snake_case	)] for i in I[0]]
      return nn_examples


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


@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _snake_case	: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _snake_case	: None),
    }	)
def    _snake_case  (  _snake_case :	str       ,      _snake_case :	Dict       ,      _snake_case :	Dict       ,      _snake_case :	List[Any]=64       ,      _snake_case :	int=256       ,      _snake_case :	List[str]=False       ,      _snake_case :	Any=2       ,      _snake_case :	List[Any]=0.95       ,      _snake_case :	Tuple=0.8	):
      with torch.no_grad():
            lowerCAmelCase    :		Union[str, Any]				=      qa_sas_generate(
                _snake_case       ,      _snake_case       ,      _snake_case       ,      num_answers=1       ,      num_beams=_snake_case       ,      min_len=_snake_case       ,      max_len=_snake_case       ,      do_sample=_snake_case       ,      temp=_snake_case       ,      top_p=_snake_case       ,      top_k=_snake_case       ,      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__						:   Tuple			 = '''
<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[Any]			 = '''
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__						:   str			 = [
    '''Answer the question''',
    '''View the retrieved document only''',
    '''View the most similar ELI5 question and answer''',
    '''Show me everything, please!''',
]
snake_case__						:   List[Any]			 = st.sidebar.checkbox('''Demo options''')
if demo_options:
 snake_case__						:   Tuple			 = st.sidebar.selectbox(
     '''''',
     action_list,
     index=3,
 )
 snake_case__						:   List[Any]			 = action_list.index(action_st)
 snake_case__						:   List[str]			 = st.sidebar.selectbox(
     '''''',
     ['''Show full text of passages''', '''Show passage section titles'''],
     index=0,
 )
 snake_case__						:   List[Any]			 = show_type == '''Show full text of passages'''
else:
 snake_case__						:   Tuple			 = 3
 snake_case__						:   List[Any]			 = True

snake_case__						:   List[str]			 = st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
 snake_case__						:   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__						:   Union[str, Any]			 = st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
 snake_case__						:   Union[str, Any]			 = st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
 snake_case__						:   List[Any]			 = '''wiki40b'''
 snake_case__						:   Union[str, Any]			 = '''dense'''

snake_case__						:   int			 = '''beam'''
snake_case__						:   str			 = 2
snake_case__						:   Dict			 = 64
snake_case__						:   List[str]			 = 256
snake_case__						:   Dict			 = None
snake_case__						:   List[str]			 = None
snake_case__						:   List[str]			 = st.sidebar.checkbox('''Generation options''')
if generate_options:
 snake_case__						:   List[Any]			 = '''
    ### 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__						:   List[str]			 = st.sidebar.slider(
     '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 snake_case__						:   Optional[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__						:   Dict			 = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  snake_case__						:   int			 = 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__						:   int			 = 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__						:   List[str]			 = None

# start main text
snake_case__						:   str			 = [
    '''<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__						:   Union[str, 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[Any]			 = st.text_input('''Enter your question here:''', '''''')
else:
 snake_case__						:   int			 = question_s

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

**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)


