





"""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											=						"""bart"""
__A											=						True
@st.cache(allow_output_mutation=_SCREAMING_SNAKE_CASE     )
def 					__A  ()   ->Tuple:





  """simple docstring"""


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





  """simple docstring"""


  if LOAD_DENSE_INDEX:
    lowerCAmelCase__     :Optional[int]											=				faiss.StandardGpuResources()
    lowerCAmelCase__     :Union[str, Any]											=				datasets.load_dataset(path='wiki_snippets'	,  name='wiki40b_en_100_0'     )['train']
    lowerCAmelCase__     :Optional[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__     :List[Any]											=				faiss.IndexFlatIP(128     )
    lowerCAmelCase__     :Union[str, Any]											=				faiss.index_cpu_to_gpu(_SCREAMING_SNAKE_CASE	,  1	,  _SCREAMING_SNAKE_CASE     )
    wikiaab_gpu_index_flat.add(_SCREAMING_SNAKE_CASE     )  # TODO fix for larger GPU
  else:
    lowerCAmelCase__				,							lowerCAmelCase__     :str											=				(None, None)
  lowerCAmelCase__     :Optional[Any]											=				Elasticsearch([{'host': 'localhost', 'port': '9200'}]     )
  return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=_SCREAMING_SNAKE_CASE     )
def 					__A  ()   ->int:





  """simple docstring"""


  lowerCAmelCase__     :int											=				datasets.load_dataset('eli5'	,  name='LFQA_reddit'     )
  lowerCAmelCase__     :int											=				elia['train_eli5']
  lowerCAmelCase__     :int											=				np.memmap(
      'eli5_questions_reps.dat'	,  dtype='float32'	,  mode='r'	,  shape=(elia_train.num_rows, 128)     )
  lowerCAmelCase__     :Optional[Any]											=				faiss.IndexFlatIP(128     )
  eli5_train_q_index.add(_SCREAMING_SNAKE_CASE     )
  return (elia_train, eli5_train_q_index)


__A							,				__A							,				__A											=						load_indexes()
__A							,				__A							,				__A							,				__A											=						load_models()
__A							,				__A											=						load_train_data()
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=10     )   ->Optional[Any]:





  """simple docstring"""


  lowerCAmelCase__     :str											=				embed_questions_for_retrieval([question]	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__				,							lowerCAmelCase__     :int											=				eli5_train_q_index.search(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :int											=				[elia_train[int(_SCREAMING_SNAKE_CASE     )] for i in I[0]]
  return nn_examples
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE="wiki40b"	,  _SCREAMING_SNAKE_CASE="dense"	,  _SCREAMING_SNAKE_CASE=10     )   ->List[str]:





  """simple docstring"""


  if source == "none":
    lowerCAmelCase__				,							lowerCAmelCase__     :str											=				(' <P> '.join(['' for _ in range(11     )]     ).strip(), [])
  else:
    if method == "dense":
      lowerCAmelCase__				,							lowerCAmelCase__     :str											=				query_qa_dense_index(
          _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
    else:
      lowerCAmelCase__				,							lowerCAmelCase__     :int											=				query_es_index(
          _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  index_name='english_wiki40b_snippets_100w'	,  n_results=_SCREAMING_SNAKE_CASE	,  )
  lowerCAmelCase__     :Optional[int]											=				[
      (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
  ]
  lowerCAmelCase__     :List[Any]											=				'question: {} context: {}'.format(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )
  return question_doc, support_list
@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _SCREAMING_SNAKE_CASE     : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _SCREAMING_SNAKE_CASE     : None),
    }     )
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE=64	,  _SCREAMING_SNAKE_CASE=256	,  _SCREAMING_SNAKE_CASE=False	,  _SCREAMING_SNAKE_CASE=2	,  _SCREAMING_SNAKE_CASE=0.9_5	,  _SCREAMING_SNAKE_CASE=0.8     )   ->Any:





  """simple docstring"""


  with torch.no_grad():
    lowerCAmelCase__     :Optional[Any]											=				qa_sas_generate(
        _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  num_answers=1	,  num_beams=_SCREAMING_SNAKE_CASE	,  min_len=_SCREAMING_SNAKE_CASE	,  max_len=_SCREAMING_SNAKE_CASE	,  do_sample=_SCREAMING_SNAKE_CASE	,  temp=_SCREAMING_SNAKE_CASE	,  top_p=_SCREAMING_SNAKE_CASE	,  top_k=_SCREAMING_SNAKE_CASE	,  max_input_length=1024	,  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)







"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A											=						{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A											=						[
      """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MgpstrModel""",
      """MgpstrPreTrainedModel""",
      """MgpstrForSceneTextRecognition""",
  ]

if TYPE_CHECKING:
  from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
  from .processing_mgp_str import MgpstrProcessor
  from .tokenization_mgp_str import MgpstrTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mgp_str import (
        MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
        MgpstrForSceneTextRecognition,
        MgpstrModel,
        MgpstrPreTrainedModel,
    )
else:
  import sys

  __A											=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
