import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DonutImageProcessor


class 			lowerCamelCase_  (     unittest.TestCase				):






    '''simple docstring'''






    def __init__(			self				,				__lowercase				,				__lowercase=7				,				__lowercase=3				,				__lowercase=18				,				__lowercase=30				,				__lowercase=400				,				__lowercase=True				,				__lowercase=None				,				__lowercase=True				,				__lowercase=False				,				__lowercase=True				,				__lowercase=True				,				__lowercase=[0.5, 0.5, 0.5]				,				__lowercase=[0.5, 0.5, 0.5]				,				) ->			Union[str, Any]:
        __UpperCamelCase  :int    						=	parent
        __UpperCamelCase  :List[str]    						=	batch_size
        __UpperCamelCase  :Optional[int]    						=	num_channels
        __UpperCamelCase  :List[Any]    						=	image_size
        __UpperCamelCase  :Optional[Any]    						=	min_resolution
        __UpperCamelCase  :Optional[Any]    						=	max_resolution
        __UpperCamelCase  :Tuple    						=	do_resize
        __UpperCamelCase  :Dict    						=	size if size is not None else {'''height''': 18, '''width''': 20}
        __UpperCamelCase  :List[str]    						=	do_thumbnail
        __UpperCamelCase  :List[str]    						=	do_align_axis
        __UpperCamelCase  :Union[str, Any]    						=	do_pad
        __UpperCamelCase  :Optional[Any]    						=	do_normalize
        __UpperCamelCase  :Optional[Any]    						=	image_mean
        __UpperCamelCase  :Optional[int]    						=	image_std







    def     UpperCamelCase__     (			self) ->			str:
        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_thumbnail": self.do_thumbnail,
            "do_align_long_axis": self.do_align_axis,
            "do_pad": self.do_pad,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }


@require_torch
@require_vision
class 			lowerCamelCase_  (     UpperCAmelCase_		,			unittest.TestCase				):






    '''simple docstring'''






    a__			:		List[Any]      			=   DonutImageProcessor if is_vision_available() else None







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :List[str]    						=	DonutImageProcessingTester(self)







    @property
    def     UpperCamelCase__     (			self) ->			Tuple:
        return self.image_processor_tester.prepare_image_processor_dict()







    def     UpperCamelCase__     (			self) ->			str:
        __UpperCamelCase  :Dict    						=	self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(__lowercase				,				'''do_resize'''))
        self.assertTrue(hasattr(__lowercase				,				'''size'''))
        self.assertTrue(hasattr(__lowercase				,				'''do_thumbnail'''))
        self.assertTrue(hasattr(__lowercase				,				'''do_align_long_axis'''))
        self.assertTrue(hasattr(__lowercase				,				'''do_pad'''))
        self.assertTrue(hasattr(__lowercase				,				'''do_normalize'''))
        self.assertTrue(hasattr(__lowercase				,				'''image_mean'''))
        self.assertTrue(hasattr(__lowercase				,				'''image_std'''))







    def     UpperCamelCase__     (			self) ->			Any:
        __UpperCamelCase  :Dict    						=	self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size				,				{'''height''': 18, '''width''': 20})

        __UpperCamelCase  :int    						=	self.image_processing_class.from_dict(self.image_processor_dict				,				size=42)
        self.assertEqual(image_processor.size				,				{'''height''': 42, '''width''': 42})

        # Previous config had dimensions in (width, height) order
        __UpperCamelCase  :List[str]    						=	self.image_processing_class.from_dict(self.image_processor_dict				,				size=(42, 84))
        self.assertEqual(image_processor.size				,				{'''height''': 84, '''width''': 42})







    def     UpperCamelCase__     (			self) ->			Dict:
        pass







    @is_flaky()
    def     UpperCamelCase__     (			self) ->			str:
        # Initialize image_processing
        __UpperCamelCase  :Optional[Any]    						=	self.image_processing_class(**self.image_processor_dict)
        # create random PIL images
        __UpperCamelCase  :List[Any]    						=	prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase				,				Image.Image)

        # Test not batched input
        __UpperCamelCase  :List[Any]    						=	image_processing(image_inputs[0]				,				return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,				)

        # Test batched
        __UpperCamelCase  :Dict    						=	image_processing(__lowercase				,				return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,				)







    @is_flaky()
    def     UpperCamelCase__     (			self) ->			str:
        # Initialize image_processing
        __UpperCamelCase  :List[Any]    						=	self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        __UpperCamelCase  :Optional[int]    						=	prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__lowercase				,				numpify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase				,				np.ndarray)

        # Test not batched input
        __UpperCamelCase  :Optional[int]    						=	image_processing(image_inputs[0]				,				return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,				)

        # Test batched
        __UpperCamelCase  :List[Any]    						=	image_processing(__lowercase				,				return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,				)







    @is_flaky()
    def     UpperCamelCase__     (			self) ->			Dict:
        # Initialize image_processing
        __UpperCamelCase  :Tuple    						=	self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        __UpperCamelCase  :Union[str, Any]    						=	prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__lowercase				,				torchify=__lowercase)
        for image in image_inputs:
            self.assertIsInstance(__lowercase				,				torch.Tensor)

        # Test not batched input
        __UpperCamelCase  :List[Any]    						=	image_processing(image_inputs[0]				,				return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,				)

        # Test batched
        __UpperCamelCase  :Optional[Any]    						=	image_processing(__lowercase				,				return_tensors='''pt''').pixel_values
        self.assertEqual(
            encoded_images.shape				,				(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )				,				)






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


__lowercase											=      '''bart'''
__lowercase											=      True



@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE			)
def lowerCamelCase						(     ):
    '''simple docstring'''







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







    if LOAD_DENSE_INDEX:
        __UpperCamelCase  :int    						=	faiss.StandardGpuResources()
        __UpperCamelCase  :List[Any]    						=	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  :Tuple    						=	faiss.IndexFlatIP(128			)
        __UpperCamelCase  :str    						=	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:
        __UpperCamelCase						, __UpperCamelCase  :Any    						=	(None, None)
    __UpperCamelCase  :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 lowerCamelCase						(     ):
    '''simple docstring'''







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


__lowercase				, __lowercase				, __lowercase											=      load_indexes()
__lowercase				, __lowercase				, __lowercase				, __lowercase											=      load_models()
__lowercase				, __lowercase											=      load_train_data()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=10			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	embed_questions_for_retrieval([question]						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase						, __UpperCamelCase  :Union[str, Any]    						=	eli5_train_q_index.search(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[Any]    						=	[elia_train[int(SCREAMING_SNAKE_CASE			)] for i in I[0]]
    return nn_examples



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="wiki40b"						,    SCREAMING_SNAKE_CASE="dense"						,    SCREAMING_SNAKE_CASE=10			):
    '''simple docstring'''







    if source == "none":
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	(''' <P> '''.join(['''''' for _ in range(11			)]			).strip(), [])
    else:
        if method == "dense":
            __UpperCamelCase						, __UpperCamelCase  :List[str]    						=	query_qa_dense_index(
                SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
        else:
            __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	query_es_index(
                SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    index_name='''english_wiki40b_snippets_100w'''						,    n_results=SCREAMING_SNAKE_CASE						,    )
    __UpperCamelCase  :int    						=	[
        (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
    ]
    __UpperCamelCase  :int    						=	'''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 lowerCamelCase						(     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=0.95						,    SCREAMING_SNAKE_CASE=0.8			):
    '''simple docstring'''







    with torch.no_grad():
        __UpperCamelCase  :Union[str, 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=1_024						,    device='''cuda:0'''						,    )[0]
    return (answer, support_list)


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

# Start sidebar
__lowercase											=      '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__lowercase											=      '''
<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
__lowercase											=      '''
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)

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

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

__lowercase											=      '''beam'''
__lowercase											=      2
__lowercase											=      64
__lowercase											=      256
__lowercase											=      None
__lowercase											=      None
__lowercase											=      st.sidebar.checkbox('''Generation options''')
if generate_options:
    __lowercase											=      '''
    ### 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)
    __lowercase											=      st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
    __lowercase											=      st.sidebar.slider(
        '''Minimum generation length''', min_value=8, max_value=256, value=64, step=8, format=None, key=None
    )
    __lowercase											=      st.sidebar.slider(
        '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, format=None, key=None
    )
    if sampled == "beam":
        __lowercase											=      st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
    else:
        __lowercase											=      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
        )
        __lowercase											=      st.sidebar.slider(
            '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, format=None, key=None
        )
        __lowercase											=      None

# start main text
__lowercase											=      [
    '''<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?''',
]
__lowercase											=      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>":
    __lowercase											=      st.text_input('''Enter your question here:''', '''''')
else:
    __lowercase											=      question_s

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


__lowercase											=      '''
---

**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)





