


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	 =      """bart"""
__a	 =      True
@st.cache(allow_output_mutation=lowerCAmelCase_ )
def 	_UpperCamelCase					(    )    ->List[str]:
   if LOAD_DENSE_INDEX:
      UpperCAmelCase      					=  AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" )
      UpperCAmelCase      					=  AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" )
      UpperCAmelCase      					=  qar_model.eval()
   else:
      UpperCAmelCase					,	UpperCAmelCase      					=  (None, None)
   if MODEL_TYPE == "bart":
      UpperCAmelCase      					=  AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" )
      UpperCAmelCase      					=  AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" )
      UpperCAmelCase      					=  torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" )
      sas_model.load_state_dict(save_dict["""model"""] )
      UpperCAmelCase      					=  sas_model.eval()
   else:
      UpperCAmelCase					,	UpperCAmelCase      					=  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					(    )    ->Tuple:
   if LOAD_DENSE_INDEX:
      UpperCAmelCase      					=  faiss.StandardGpuResources()
      UpperCAmelCase      					=  datasets.load_dataset(path="""wiki_snippets"""	,    name="""wiki40b_en_100_0""" )["""train"""]
      UpperCAmelCase      					=  np.memmap(
          """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""	,    dtype="""float32"""	,    mode="""r"""	,    shape=(wikiaab_passages.num_rows, 1_2_8)	,    )
      UpperCAmelCase      					=  faiss.IndexFlatIP(1_2_8 )
      UpperCAmelCase      					=  faiss.index_cpu_to_gpu(lowerCAmelCase_	,    1	,    lowerCAmelCase_ )
      wikiaab_gpu_index_flat.add(lowerCAmelCase_ )  # TODO fix for larger GPU
   else:
      UpperCAmelCase					,	UpperCAmelCase      					=  (None, None)
   UpperCAmelCase      					=  Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] )
   return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=lowerCAmelCase_ )
def 	_UpperCamelCase					(    )    ->int:
   UpperCAmelCase      					=  datasets.load_dataset("""eli5"""	,    name="""LFQA_reddit""" )
   UpperCAmelCase      					=  elia["""train_eli5"""]
   UpperCAmelCase      					=  np.memmap(
       """eli5_questions_reps.dat"""	,    dtype="""float32"""	,    mode="""r"""	,    shape=(elia_train.num_rows, 1_2_8) )
   UpperCAmelCase      					=  faiss.IndexFlatIP(1_2_8 )
   eli5_train_q_index.add(lowerCAmelCase_ )
   return (elia_train, eli5_train_q_index)


__a ,       __a ,       __a	 =      load_indexes()
__a ,       __a ,       __a ,       __a	 =      load_models()
__a ,       __a	 =      load_train_data()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_=1_0 )    ->Optional[Any]:
   UpperCAmelCase      					=  embed_questions_for_retrieval([question]	,    lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase					,	UpperCAmelCase      					=  eli5_train_q_index.search(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  [elia_train[int(lowerCAmelCase_ )] for i in I[0]]
   return nn_examples
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_="wiki40b"	,    lowerCAmelCase_="dense"	,    lowerCAmelCase_=1_0 )    ->int:
   if source == "none":
      UpperCAmelCase					,	UpperCAmelCase      					=  (""" <P> """.join(["""""" for _ in range(1_1 )] ).strip(), [])
   else:
      if method == "dense":
         UpperCAmelCase					,	UpperCAmelCase      					=  query_qa_dense_index(
             lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
      else:
         UpperCAmelCase					,	UpperCAmelCase      					=  query_es_index(
             lowerCAmelCase_	,    lowerCAmelCase_	,    index_name="""english_wiki40b_snippets_100w"""	,    n_results=lowerCAmelCase_	,    )
   UpperCAmelCase      					=  [
       (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
   ]
   UpperCAmelCase      					=  """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_=6_4	,    lowerCAmelCase_=2_5_6	,    lowerCAmelCase_=False	,    lowerCAmelCase_=2	,    lowerCAmelCase_=0.95	,    lowerCAmelCase_=0.8 )    ->Tuple:
   with torch.no_grad():
      UpperCAmelCase      					=  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=1_0_2_4	,    device="""cuda:0"""	,    )[0]
   return (answer, support_list)


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

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

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

__a	 =      """beam"""
__a	 =      2
__a	 =      64
__a	 =      256
__a	 =      None
__a	 =      None
__a	 =      st.sidebar.checkbox("""Generation options""")
if generate_options:
 __a	 =      """
    ### 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	 =      st.sidebar.selectbox("""Would you like to use beam search or sample an answer?""", ["""beam""", """sampled"""])
 __a	 =      st.sidebar.slider(
     """Minimum generation length""", min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 __a	 =      st.sidebar.slider(
     """Maximum generation length""", min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  __a	 =      st.sidebar.slider("""Beam size""", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  __a	 =      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	 =      st.sidebar.slider(
      """Temperature""", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
  )
  __a	 =      None

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

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

**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)







import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


__a	 =      re.compile("""[^A-Za-z_0-9]""")
# parameters used in DuplicationIndex
__a	 =      10
__a	 =      256
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Optional[MinHash]:
   if len(lowerCAmelCase_ ) < MIN_NUM_TOKENS:
      return None
   UpperCAmelCase      					=  MinHash(num_perm=lowerCAmelCase_ )
   for token in set(lowerCAmelCase_ ):
      min_hash.update(token.encode() )
   return min_hash
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Set[str]:
   return {t for t in NON_ALPHA.split(lowerCAmelCase_ ) if len(t.strip() ) > 0}






class 	__lowercase					:





      def __init__(		self    :       List[str]       ,							*,
          __lowerCamelCase    :       float = 0.85       ,							)     -> Any:






         """simple docstring"""







         UpperCAmelCase      					=  duplication_jaccard_threshold
         UpperCAmelCase      					=  NUM_PERM
         UpperCAmelCase      					=  MinHashLSH(threshold=self._duplication_jaccard_threshold       ,							num_perm=self._num_perm							)

         UpperCAmelCase      					=  defaultdict(__lowerCamelCase							)





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       MinHash							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  self._index.query(__lowerCamelCase							)
         if code_key in self._index.keys:
            print(F"""Duplicate key {code_key}"""							)
            return

         self._index.insert(__lowerCamelCase       ,							__lowerCamelCase							)
         if len(__lowerCamelCase							) > 0:
            for base_duplicate in close_duplicates:
               if base_duplicate in self._duplicate_clusters:
                  self._duplicate_clusters[base_duplicate].add(__lowerCamelCase							)
                  break
            else:
               self._duplicate_clusters[close_duplicates[0]].add(__lowerCamelCase							)





      def 							_lowercase					(		self    :       Union[str, Any]							)     -> List[List[Dict]]:






         """simple docstring"""







         UpperCAmelCase      					=  []
         for base, duplicates in self._duplicate_clusters.items():
            UpperCAmelCase      					=  [base] + list(__lowerCamelCase							)
            # reformat the cluster to be a list of dict
            UpperCAmelCase      					=  [{"""base_index""": el[0], """repo_name""": el[1], """path""": el[2]} for el in cluster]
            duplicate_clusters.append(__lowerCamelCase							)
         return duplicate_clusters





      def 							_lowercase					(		self    :       Tuple       ,							__lowerCamelCase    :       Optional[int]							)     -> None:






         """simple docstring"""







         UpperCAmelCase      					=  self.get_duplicate_clusters()
         with open(__lowerCamelCase       ,							"""w"""							) as f:
            json.dump(__lowerCamelCase       ,							__lowerCamelCase							)

def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->Tuple:
   UpperCAmelCase					,	UpperCAmelCase      					=  element
   UpperCAmelCase      					=  get_min_hash([t for t in NON_ALPHA.split(data["""content"""] ) if len(t.strip() ) > 0] )
   if min_hash is not None:
      return (index, data["repo_name"], data["path"]), min_hash
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->int:
   with mp.Pool() as pool:
      for data in pool.imap_unordered(
          _compute_min_hash	,    ThreadedIterator(lowerCAmelCase_	,    max_queue_size=1_0_0_0_0 )	,    chunksize=1_0_0	,    ):
         if data is not None:
            yield data
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Any:
   UpperCAmelCase      					=  DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase_ )

   for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase_ ) )	,    max_queue_size=1_0_0 ) ):
      di.add(lowerCAmelCase_	,    lowerCAmelCase_ )

   # Returns a List[Cluster] where Cluster is List[str] with the filenames.
   return di.get_duplicate_clusters()
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->float:
   UpperCAmelCase      					=  get_tokens(lowerCAmelCase_ )
   UpperCAmelCase      					=  get_tokens(lowerCAmelCase_ )
   return len(tokensa & tokensa ) / len(tokensa | tokensa )


__a	 =      None
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   UpperCAmelCase      					=  []
   for elementa in cluster:
      UpperCAmelCase      					=  _shared_dataset[elementa["""base_index"""]]["""content"""]
      for elementa in extremes:
         UpperCAmelCase      					=  _shared_dataset[elementa["""base_index"""]]["""content"""]
         if jaccard_similarity(lowerCAmelCase_	,    lowerCAmelCase_ ) >= jaccard_threshold:
            elementa["copies"] += 1
            break
      else:
         UpperCAmelCase      					=  1
         extremes.append(lowerCAmelCase_ )
   return extremes
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   global _shared_dataset
   UpperCAmelCase      					=  dataset
   UpperCAmelCase      					=  []
   UpperCAmelCase      					=  partial(_find_cluster_extremes_shared	,    jaccard_threshold=lowerCAmelCase_ )
   with mp.Pool() as pool:
      for extremes in tqdm(
          pool.imap_unordered(
              lowerCAmelCase_	,    lowerCAmelCase_	,    )	,    total=len(lowerCAmelCase_ )	,    ):
         extremes_list.append(lowerCAmelCase_ )
   return extremes_list







def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ = 0.85 )    ->Tuple[Type[Dataset], List[List[Dict]]]:
   UpperCAmelCase      					=  make_duplicate_clusters(lowerCAmelCase_	,    lowerCAmelCase_ )
   UpperCAmelCase      					=  {x["""base_index"""] for cluster in duplicate_clusters for x in cluster}
   UpperCAmelCase      					=  {}
   UpperCAmelCase      					=  find_extremes(lowerCAmelCase_	,    lowerCAmelCase_	,    lowerCAmelCase_ )
   for extremes in extremes_clusters:
      for element in extremes:
         UpperCAmelCase      					=  element
   UpperCAmelCase      					=  duplicate_indices - set(extreme_dict.keys() )
   UpperCAmelCase      					=  dataset.filter(lambda lowerCAmelCase_	,    lowerCAmelCase_ : idx not in remove_indices	,    with_indices=lowerCAmelCase_ )

   # update duplicate_clusters
   for cluster in duplicate_clusters:
      for element in cluster:
         UpperCAmelCase      					=  element["""base_index"""] in extreme_dict
         if element["is_extreme"]:
            UpperCAmelCase      					=  extreme_dict[element["""base_index"""]]["""copies"""]

   print(F"""Original dataset size: {len(lowerCAmelCase_ )}""" )
   print(F"""Number of duplicate clusters: {len(lowerCAmelCase_ )}""" )
   print(F"""Files in duplicate cluster: {len(lowerCAmelCase_ )}""" )
   print(F"""Unique files in duplicate cluster: {len(lowerCAmelCase_ )}""" )
   print(F"""Filtered dataset size: {len(lowerCAmelCase_ )}""" )

   return ds_filter, duplicate_clusters



