






"""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


_lowerCAmelCase	:	Optional[int]				=      """bart"""
_lowerCAmelCase	:	str				=      True


@st.cache(allow_output_mutation=snake_case				)
def 	SCREAMING_SNAKE_CASE__       (  )->		int:






     '''simple docstring'''



     if LOAD_DENSE_INDEX:
          UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased"				)
          UpperCAmelCase__						:    Any							=							AutoModel.from_pretrained("yjernite/retribert-base-uncased"				).to("cuda:0"				)
          UpperCAmelCase__						:    Optional[Any]							=							qar_model.eval()
     else:
          UpperCAmelCase__							,				UpperCAmelCase__						:    Tuple							=							(None, None)
     if MODEL_TYPE == "bart":
          UpperCAmelCase__						:    Tuple							=							AutoTokenizer.from_pretrained("yjernite/bart_eli5"				)
          UpperCAmelCase__						:    List[str]							=							AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5"				).to("cuda:0"				)
          UpperCAmelCase__						:    int							=							torch.load("seq2seq_models/eli5_bart_model_blm_2.pth"				)
          sas_model.load_state_dict(save_dict["model"]				)
          UpperCAmelCase__						:    Any							=							sas_model.eval()
     else:
          UpperCAmelCase__							,				UpperCAmelCase__						:    Union[str, 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 	SCREAMING_SNAKE_CASE__       (  )->		Optional[Any]:






     '''simple docstring'''



     if LOAD_DENSE_INDEX:
          UpperCAmelCase__						:    List[str]							=							faiss.StandardGpuResources()
          UpperCAmelCase__						:    Dict							=							datasets.load_dataset(path="wiki_snippets"			,					name="wiki40b_en_100_0"				)["train"]
          UpperCAmelCase__						:    List[str]							=							np.memmap(
              "wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"			,					dtype="float32"			,					mode="r"			,					shape=(wikiaab_passages.num_rows, 128)			,					)
          UpperCAmelCase__						:    int							=							faiss.IndexFlatIP(128				)
          UpperCAmelCase__						:    Dict							=							faiss.index_cpu_to_gpu(snake_case			,					1			,					snake_case				)
          wikiaab_gpu_index_flat.add(snake_case				)  # TODO fix for larger GPU
     else:
          UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							(None, None)
     UpperCAmelCase__						:    List[Any]							=							Elasticsearch([{"host": "localhost", "port": "9200"}]				)
     return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=snake_case				)
def 	SCREAMING_SNAKE_CASE__       (  )->		List[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    int							=							datasets.load_dataset("eli5"			,					name="LFQA_reddit"				)
     UpperCAmelCase__						:    Any							=							elia["train_eli5"]
     UpperCAmelCase__						:    int							=							np.memmap(
         "eli5_questions_reps.dat"			,					dtype="float32"			,					mode="r"			,					shape=(elia_train.num_rows, 128)				)
     UpperCAmelCase__						:    Union[str, Any]							=							faiss.IndexFlatIP(128				)
     eli5_train_q_index.add(snake_case				)
     return (elia_train, eli5_train_q_index)


_lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase	:	Optional[int]				=      load_indexes()
_lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase			,  _lowerCAmelCase	:	List[str]				=      load_models()
_lowerCAmelCase			,  _lowerCAmelCase	:	Union[str, Any]				=      load_train_data()


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : List[str]=10				)->		Optional[Any]:






     '''simple docstring'''



     UpperCAmelCase__						:    Dict							=							embed_questions_for_retrieval([question]			,					snake_case			,					snake_case				)
     UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							eli5_train_q_index.search(snake_case			,					snake_case				)
     UpperCAmelCase__						:    Tuple							=							[elia_train[int(snake_case				)] for i in I[0]]
     return nn_examples


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : str			,					snake_case       : Optional[Any]="wiki40b"			,					snake_case       : List[str]="dense"			,					snake_case       : Any=10				)->		Dict:






     '''simple docstring'''



     if source == "none":
          UpperCAmelCase__							,				UpperCAmelCase__						:    Optional[Any]							=							(" <P> ".join(["" for _ in range(11				)]				).strip(), [])
     else:
          if method == "dense":
               UpperCAmelCase__							,				UpperCAmelCase__						:    Any							=							query_qa_dense_index(
                   snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case				)
          else:
               UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							query_es_index(
                   snake_case			,					snake_case			,					index_name="english_wiki40b_snippets_100w"			,					n_results=snake_case			,					)
     UpperCAmelCase__						:    Optional[int]							=							[
         (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
     ]
     UpperCAmelCase__						:    Optional[int]							=							"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 	SCREAMING_SNAKE_CASE__       (  snake_case       : Union[str, Any]			,					snake_case       : List[str]			,					snake_case       : Optional[Any]			,					snake_case       : Union[str, Any]=64			,					snake_case       : List[str]=256			,					snake_case       : Optional[Any]=False			,					snake_case       : str=2			,					snake_case       : Optional[int]=0.95			,					snake_case       : Optional[int]=0.8				)->		int:






     '''simple docstring'''



     with torch.no_grad():
          UpperCAmelCase__						:    int							=							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
_lowerCAmelCase	:	str				=      """<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_lowerCAmelCase	:	List[str]				=      """
<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
_lowerCAmelCase	:	List[str]				=      """
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)

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

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

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

# start main text
_lowerCAmelCase	:	Union[str, Any]				=      [
    """<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?""",
]
_lowerCAmelCase	:	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>":
 _lowerCAmelCase	:	Optional[Any]				=      st.text_input("""Enter your question here:""", """""")
else:
 _lowerCAmelCase	:	Tuple				=      question_s

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


_lowerCAmelCase	:	Dict				=      """
---

**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 __future__ import annotations

from collections.abc import Iterator
from typing import Any




class 			lowerCAmelCase__			:
   def __init__(		self      :	Any							,			snake_case__      :	Any							):


        '''simple docstring'''
        UpperCAmelCase__						:    Any							=							data
        UpperCAmelCase__						:    Node | None							=							None




class 			lowerCAmelCase__			:
   def __init__(		self      :	Dict							):


        '''simple docstring'''
        UpperCAmelCase__						:    Tuple							=							None
        UpperCAmelCase__						:    Optional[int]							=							None
   def __iter__(		self      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__						:    Optional[int]							=							self.head
        while self.head:
             yield node.data
             UpperCAmelCase__						:    Optional[Any]							=							node.next
             if node == self.head:
                  break
   def __len__(		self      :	Tuple							):


        '''simple docstring'''
        return sum(1 for _ in self							)
   def __repr__(		self      :	str							):


        '''simple docstring'''
        return "->".join(str(snake_case__							) for item in iter(self							)							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(len(self							)							,			snake_case__							)
   def       __a							(		self      :	Optional[Any]							,			snake_case__      :	Any							):


        '''simple docstring'''
        self.insert_nth(0							,			snake_case__							)
   def       __a							(		self      :	Any							,			snake_case__      :	int							,			snake_case__      :	Any							):


        '''simple docstring'''
        if index < 0 or index > len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Any							=							Node(snake_case__							)
        if self.head is None:
             UpperCAmelCase__						:    Union[str, Any]							=							new_node  # first node points itself
             UpperCAmelCase__						:    List[str]							=							new_node
        elif index == 0:  # insert at head
             UpperCAmelCase__						:    Optional[Any]							=							self.head
             UpperCAmelCase__						:    List[Any]							=							new_node
        else:
             UpperCAmelCase__						:    Union[str, Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    str							=							new_node
             if index == len(self							) - 1:  # insert at tail
                  UpperCAmelCase__						:    Union[str, Any]							=							new_node
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        return self.delete_nth(0							)
   def       __a							(		self      :	Optional[Any]							):


        '''simple docstring'''
        return self.delete_nth(len(self							) - 1							)
   def       __a							(		self      :	Tuple							,			snake_case__      :	int = 0							):


        '''simple docstring'''
        if not 0 <= index < len(self							):
             raise IndexError("list index out of range."							)
        UpperCAmelCase__						:    Union[str, Any]							=							self.head
        if self.head == self.tail:  # just one node
             UpperCAmelCase__						:    str							=							None
        elif index == 0:  # delete head node
             UpperCAmelCase__						:    List[Any]							=							self.tail.next.next
             UpperCAmelCase__						:    Dict							=							self.head.next
        else:
             UpperCAmelCase__						:    List[Any]							=							self.head
             for _ in range(index - 1							):
                  UpperCAmelCase__						:    Tuple							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next
             UpperCAmelCase__						:    Dict							=							temp.next.next
             if index == len(self							) - 1:  # delete at tail
                  UpperCAmelCase__						:    int							=							temp
        return delete_node.data




   def       __a							(		self      :	Any							):


        '''simple docstring'''
        return len(self							) == 0




def 	SCREAMING_SNAKE_CASE__       (  )->		None:






     '''simple docstring'''



     UpperCAmelCase__						:    str							=							CircularLinkedList()
     assert len(snake_case				) == 0
     assert circular_linked_list.is_empty() is True
     assert str(snake_case				) == ""

     try:
          circular_linked_list.delete_front()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_tail()
          raise AssertionError  # This should not happen
     except IndexError:
          assert True  # This should happen

     try:
          circular_linked_list.delete_nth(-1				)
          raise AssertionError
     except IndexError:
          assert True

     try:
          circular_linked_list.delete_nth(0				)
          raise AssertionError
     except IndexError:
          assert True

     assert circular_linked_list.is_empty() is True
     for i in range(5				):
          assert len(snake_case				) == i
          circular_linked_list.insert_nth(snake_case			,					i + 1				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     circular_linked_list.insert_tail(6				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					7				)				)
     circular_linked_list.insert_head(0				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(0			,					7				)				)

     assert circular_linked_list.delete_front() == 0
     assert circular_linked_list.delete_tail() == 6
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)
     assert circular_linked_list.delete_nth(2				) == 3

     circular_linked_list.insert_nth(2			,					3				)
     assert str(snake_case				) == "->".join(str(snake_case				) for i in range(1			,					6				)				)

     assert circular_linked_list.is_empty() is False


if __name__ == "__main__":
 import doctest

 doctest.testmod()


