





'''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


A__							:  Optional[Any]    ='''bart'''
A__							:  Any    =True
@st.cache(allow_output_mutation=lowerCAmelCase    )
def   UpperCamelCase__ (						):





      """simple docstring"""


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





      """simple docstring"""


      if LOAD_DENSE_INDEX:
            _lowerCAmelCase					=				faiss.StandardGpuResources()
            _lowerCAmelCase					=				datasets.load_dataset(path="""wiki_snippets"""				,      name="""wiki40b_en_100_0"""    )["""train"""]
            _lowerCAmelCase					=				np.memmap(
                """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""				,      dtype="""float32"""				,      mode="""r"""				,      shape=(wikiaab_passages.num_rows, 1_28)				,      )
            _lowerCAmelCase					=				faiss.IndexFlatIP(1_28    )
            _lowerCAmelCase					=				faiss.index_cpu_to_gpu(lowerCAmelCase				,      1				,      lowerCAmelCase    )
            wikiaab_gpu_index_flat.add(lowerCAmelCase    )  # TODO fix for larger GPU
      else:
            _lowerCAmelCase	,				_lowerCAmelCase					=				(None, None)
      _lowerCAmelCase					=				Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]    )
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=lowerCAmelCase    )
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				datasets.load_dataset("""eli5"""				,      name="""LFQA_reddit"""    )
      _lowerCAmelCase					=				elia["""train_eli5"""]
      _lowerCAmelCase					=				np.memmap(
          """eli5_questions_reps.dat"""				,      dtype="""float32"""				,      mode="""r"""				,      shape=(elia_train.num_rows, 1_28)    )
      _lowerCAmelCase					=				faiss.IndexFlatIP(1_28    )
      eli5_train_q_index.add(lowerCAmelCase    )
      return (elia_train, eli5_train_q_index)


A__  ,		A__  ,		A__							:  List[Any]    =load_indexes()
A__  ,		A__  ,		A__  ,		A__							:  Any    =load_models()
A__  ,		A__							:  Optional[int]    =load_train_data()
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase=10    ):





      """simple docstring"""


      _lowerCAmelCase					=				embed_questions_for_retrieval([question]				,      lowerCAmelCase				,      lowerCAmelCase    )
      _lowerCAmelCase	,				_lowerCAmelCase					=				eli5_train_q_index.search(lowerCAmelCase				,      lowerCAmelCase    )
      _lowerCAmelCase					=				[elia_train[int(lowerCAmelCase    )] for i in I[0]]
      return nn_examples
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase="wiki40b"				,      lowerCAmelCase="dense"				,      lowerCAmelCase=10    ):





      """simple docstring"""


      if source == "none":
            _lowerCAmelCase	,				_lowerCAmelCase					=				(""" <P> """.join(["""""" for _ in range(11    )]    ).strip(), [])
      else:
            if method == "dense":
                  _lowerCAmelCase	,				_lowerCAmelCase					=				query_qa_dense_index(
                      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase    )
            else:
                  _lowerCAmelCase	,				_lowerCAmelCase					=				query_es_index(
                      lowerCAmelCase				,      lowerCAmelCase				,      index_name="""english_wiki40b_snippets_100w"""				,      n_results=lowerCAmelCase				,      )
      _lowerCAmelCase					=				[
          (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
      ]
      _lowerCAmelCase					=				"""question: {} context: {}""".format(lowerCAmelCase				,      lowerCAmelCase    )
      return question_doc, support_list
@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowerCAmelCase    : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCAmelCase    : None),
    }    )
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase=64				,      lowerCAmelCase=2_56				,      lowerCAmelCase=False				,      lowerCAmelCase=2				,      lowerCAmelCase=0.95				,      lowerCAmelCase=0.8    ):





      """simple docstring"""


      with torch.no_grad():
            _lowerCAmelCase					=				qa_sas_generate(
                lowerCAmelCase				,      lowerCAmelCase				,      lowerCAmelCase				,      num_answers=1				,      num_beams=lowerCAmelCase				,      min_len=lowerCAmelCase				,      max_len=lowerCAmelCase				,      do_sample=lowerCAmelCase				,      temp=lowerCAmelCase				,      top_p=lowerCAmelCase				,      top_k=lowerCAmelCase				,      max_input_length=10_24				,      device="""cuda:0"""				,      )[0]
      return (answer, support_list)


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

# Start sidebar
A__							:  Optional[Any]    ='''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
A__							:  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
A__							:  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)

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

A__							:  str    =st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
      A__							:  Any    ='''
    ### Information retriever options

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

A__							:  Optional[Any]    ='''beam'''
A__							:  Optional[int]    =2
A__							:  Union[str, Any]    =64
A__							:  Union[str, Any]    =2_56
A__							:  Tuple    =None
A__							:  Dict    =None
A__							:  Optional[int]    =st.sidebar.checkbox('''Generation options''')
if generate_options:
      A__							:  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)
      A__							:  Optional[Any]    =st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
      A__							:  List[Any]    =st.sidebar.slider(
          '''Minimum generation length''', min_value=8, max_value=2_56, value=64, step=8, format=None, key=None
      )
      A__							:  Optional[int]    =st.sidebar.slider(
          '''Maximum generation length''', min_value=64, max_value=5_12, value=2_56, step=16, format=None, key=None
      )
      if sampled == "beam":
            A__							:  Any    =st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
      else:
            A__							:  Tuple    =st.sidebar.slider(
                '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
            )
            A__							:  str    =st.sidebar.slider(
                '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
            )
            A__							:  str    =None

# start main text
A__							:  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?''',
]
A__							:  Tuple    =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>":
      A__							:  Dict    =st.text_input('''Enter your question here:''', '''''')
else:
      A__							:  Any    =question_s

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


A__							:  int    ='''
---

**Disclaimer**

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





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













'''simple docstring'''






from datetime import datetime as dt
import os

from github import Github


A__							:  str    =[
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''feature request''',
    '''new model''',
    '''wip''',
]
def   UpperCamelCase__ (						):





      """simple docstring"""


      _lowerCAmelCase					=				Github(os.environ["""GITHUB_TOKEN"""]    )
      _lowerCAmelCase					=				g.get_repo("""huggingface/transformers"""    )
      _lowerCAmelCase					=				repo.get_issues(state="""open"""    )

      for issue in open_issues:
            _lowerCAmelCase					=				sorted([comment for comment in issue.get_comments()]				,      key=lambda lowerCAmelCase    : i.created_at				,      reverse=lowerCAmelCase    )
            _lowerCAmelCase					=				comments[0] if len(lowerCAmelCase    ) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state="""closed"""    )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()    )
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      """This issue has been automatically marked as stale because it has not had """
                      """recent activity. If you think this still needs to be addressed """
                      """please comment on this thread.\n\nPlease note that issues that do not follow the """
                      """[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) """
                      """are likely to be ignored."""    )


if __name__ == "__main__":
      main()






