





import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.generation import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )



@require_flax
class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def  lowercase_					(       self   ,				A_   ,				A_     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			jnp.ones((batch_size, length)     ) / length
        return scores



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			20

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(batch_size=2   ,				length=A_     )

        # tweak scores to not be uniform anymore
        SCREAMING_SNAKE_CASE__											=			scores.at[1, 5].set((1 / length) + 0.1     )  # peak, 1st batch
        SCREAMING_SNAKE_CASE__											=			scores.at[1, 10].set((1 / length) - 0.4     )  # valley, 1st batch

        # compute softmax
        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(A_   ,				axis=-1     )

        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=0.5     )
        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=1.3     )

        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(temp_dist_warper_sharper(A_   ,				scores.copy()   ,				cur_len=A_     )   ,				axis=-1     )
        SCREAMING_SNAKE_CASE__											=			jax.nn.softmax(temp_dist_warper_smoother(A_   ,				scores.copy()   ,				cur_len=A_     )   ,				axis=-1     )

        # uniform distribution stays uniform
        self.assertTrue(jnp.allclose(probs[0, :]   ,				warped_prob_sharp[0, :]   ,				atol=1E-3     )     )
        self.assertTrue(jnp.allclose(probs[0, :]   ,				warped_prob_smooth[0, :]   ,				atol=1E-3     )     )

        # sharp peaks get higher, valleys get lower
        self.assertLess(probs[1, :].max()   ,				warped_prob_sharp[1, :].max()     )
        self.assertGreater(probs[1, :].min()   ,				warped_prob_sharp[1, :].min()     )

        # smooth peaks get lower, valleys get higher
        self.assertGreater(probs[1, :].max()   ,				warped_prob_smooth[1, :].max()     )
        self.assertLess(probs[1, :].min()   ,				warped_prob_smooth[1, :].min()     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			2

        # create ramp distribution
        SCREAMING_SNAKE_CASE__											=			np.broadcast_to(np.arange(A_     )[None, :]   ,				(batch_size, vocab_size)     ).copy()
        SCREAMING_SNAKE_CASE__											=			ramp_logits[1:, : vocab_size // 2] + vocab_size

        SCREAMING_SNAKE_CASE__											=			FlaxTopKLogitsWarper(3     )

        SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )

        # check that correct tokens are filtered
        self.assertListEqual(jnp.isinf(scores[0]     ).tolist()   ,				7 * [True] + 3 * [False]     )
        self.assertListEqual(jnp.isinf(scores[1]     ).tolist()   ,				2 * [True] + 3 * [False] + 5 * [True]     )

        # check special case
        SCREAMING_SNAKE_CASE__											=			5
        SCREAMING_SNAKE_CASE__											=			FlaxTopKLogitsWarper(top_k=1   ,				filter_value=0.0   ,				min_tokens_to_keep=3     )

        SCREAMING_SNAKE_CASE__											=			np.broadcast_to(np.arange(A_     )[None, :]   ,				(batch_size, length)     ).copy()
        SCREAMING_SNAKE_CASE__											=			top_k_warp_safety_check(A_   ,				A_   ,				cur_len=A_     )

        # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
        self.assertListEqual((scores == 0.0).sum(axis=-1     ).tolist()   ,				[2, 2]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			None
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			2

        # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
        SCREAMING_SNAKE_CASE__											=			np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]     )     )

        SCREAMING_SNAKE_CASE__											=			FlaxTopPLogitsWarper(0.8     )
        SCREAMING_SNAKE_CASE__											=			np.exp(top_p_warp(A_   ,				A_   ,				cur_len=A_     )     )

        # dist should be filtered to keep min num values so that sum is >= top_p
        # exp (-inf) => 0
        SCREAMING_SNAKE_CASE__											=			np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]     )
        self.assertTrue(np.allclose(A_   ,				A_   ,				atol=1E-3     )     )

        # check edge cases with negative and extreme logits
        SCREAMING_SNAKE_CASE__											=			np.broadcast_to(np.arange(A_     )[None, :]   ,				(batch_size, vocab_size)     ).copy() - (
            vocab_size // 2
        )

        # make ramp_logits more extreme
        SCREAMING_SNAKE_CASE__											=			ramp_logits[1] * 100.0

        # make sure at least 2 tokens are kept
        SCREAMING_SNAKE_CASE__											=			FlaxTopPLogitsWarper(0.9   ,				min_tokens_to_keep=2   ,				filter_value=0.0     )
        SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )

        # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
        self.assertListEqual((filtered_dist != 0.0).sum(axis=-1     ).tolist()   ,				[3, 2]     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0

        SCREAMING_SNAKE_CASE__											=			FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=A_     )

        # check that min length is applied at length 5
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, 20)   ,				vocab_size=20     )
        SCREAMING_SNAKE_CASE__											=			5
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			min_dist_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist()   ,				4 * [-float('''inf'''     )]     )

        # check that min length is not applied anymore at length 15
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			min_dist_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertFalse(jnp.isinf(A_     ).any()     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0

        SCREAMING_SNAKE_CASE__											=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A_     )

        # check that all scores are -inf except the bos_token_id score
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, 1)   ,				vocab_size=20     )
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			logits_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]     ).all()     )
        self.assertListEqual(scores[:, bos_token_id].tolist()   ,				4 * [0]     )  # score for bos_token_id shold be zero

        # check that bos_token_id is not forced if current length is greater than 1
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			logits_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertFalse(jnp.isinf(A_     ).any()     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			20
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			0
        SCREAMING_SNAKE_CASE__											=			5

        SCREAMING_SNAKE_CASE__											=			FlaxForcedEOSTokenLogitsProcessor(max_length=A_   ,				eos_token_id=A_     )

        # check that all scores are -inf except the eos_token_id when max_length is reached
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, 4)   ,				vocab_size=20     )
        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			logits_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]     ).all()     )
        self.assertListEqual(scores[:, eos_token_id].tolist()   ,				4 * [0]     )  # score for eos_token_id should be zero

        # check that eos_token_id is not forced if max_length is not reached
        SCREAMING_SNAKE_CASE__											=			3
        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			logits_processor(A_   ,				A_   ,				cur_len=A_     )
        self.assertFalse(jnp.isinf(A_     ).any()     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			15

        # dummy input_ids and scores
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, sequence_length)   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.copy()

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			scores.copy()

        # instantiate all dist processors
        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=0.5     )
        SCREAMING_SNAKE_CASE__											=			FlaxTopKLogitsWarper(3     )
        SCREAMING_SNAKE_CASE__											=			FlaxTopPLogitsWarper(0.8     )

        # instantiate all logits processors
        SCREAMING_SNAKE_CASE__											=			FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=A_     )
        SCREAMING_SNAKE_CASE__											=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A_     )
        SCREAMING_SNAKE_CASE__											=			FlaxForcedEOSTokenLogitsProcessor(max_length=A_   ,				eos_token_id=A_     )

        SCREAMING_SNAKE_CASE__											=			10

        # no processor list
        SCREAMING_SNAKE_CASE__											=			temp_dist_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			min_dist_proc(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			bos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
        SCREAMING_SNAKE_CASE__											=			eos_dist_proc(A_   ,				A_   ,				cur_len=A_     )

        # with processor list
        SCREAMING_SNAKE_CASE__											=			FlaxLogitsProcessorList(
            [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
        SCREAMING_SNAKE_CASE__											=			processor(A_   ,				A_   ,				cur_len=A_     )

        # scores should be equal
        self.assertTrue(jnp.allclose(A_   ,				A_   ,				atol=1E-3     )     )

        # input_ids should never be changed
        self.assertListEqual(input_ids.tolist()   ,				input_ids_comp.tolist()     )




    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			4
        SCREAMING_SNAKE_CASE__											=			10
        SCREAMING_SNAKE_CASE__											=			15
        SCREAMING_SNAKE_CASE__											=			2
        SCREAMING_SNAKE_CASE__											=			1
        SCREAMING_SNAKE_CASE__											=			15

        # dummy input_ids and scores
        SCREAMING_SNAKE_CASE__											=			ids_tensor((batch_size, sequence_length)   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			input_ids.copy()

        SCREAMING_SNAKE_CASE__											=			self._get_uniform_logits(A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			scores.copy()

        # instantiate all dist processors
        SCREAMING_SNAKE_CASE__											=			FlaxTemperatureLogitsWarper(temperature=0.5     )
        SCREAMING_SNAKE_CASE__											=			FlaxTopKLogitsWarper(3     )
        SCREAMING_SNAKE_CASE__											=			FlaxTopPLogitsWarper(0.8     )

        # instantiate all logits processors
        SCREAMING_SNAKE_CASE__											=			FlaxMinLengthLogitsProcessor(min_length=10   ,				eos_token_id=A_     )
        SCREAMING_SNAKE_CASE__											=			FlaxForcedBOSTokenLogitsProcessor(bos_token_id=A_     )
        SCREAMING_SNAKE_CASE__											=			FlaxForcedEOSTokenLogitsProcessor(max_length=A_   ,				eos_token_id=A_     )

        SCREAMING_SNAKE_CASE__											=			10

        # no processor list
        def run_no_processor_list(A_   ,				A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			temp_dist_warp(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			top_k_warp(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			top_p_warp(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			min_dist_proc(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			bos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
            SCREAMING_SNAKE_CASE__											=			eos_dist_proc(A_   ,				A_   ,				cur_len=A_     )
            return scores

        # with processor list
        def run_processor_list(A_   ,				A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			FlaxLogitsProcessorList(
                [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]     )
            SCREAMING_SNAKE_CASE__											=			processor(A_   ,				A_   ,				cur_len=A_     )
            return scores

        SCREAMING_SNAKE_CASE__											=			jax.jit(A_     )
        SCREAMING_SNAKE_CASE__											=			jax.jit(A_     )

        SCREAMING_SNAKE_CASE__											=			jitted_run_no_processor_list(A_   ,				A_   ,				A_     )
        SCREAMING_SNAKE_CASE__											=			jitted_run_processor_list(A_   ,				A_   ,				A_     )

        # scores should be equal
        self.assertTrue(jnp.allclose(A_   ,				A_   ,				atol=1E-3     )     )

        # input_ids should never be changed
        self.assertListEqual(input_ids.tolist()   ,				input_ids_comp.tolist()     )


"""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=__snake_case      )
def  lowerCamelCase__						(     )				->						Dict:


					"""simple docstring"""





					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=__snake_case      )
def  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					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_28),	)
										_UpperCamelCase									=    faiss.IndexFlatIP(1_28      )
										_UpperCamelCase									=    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									=    (None, None)
					_UpperCamelCase									=    Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]      )
					return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=__snake_case      )
def  lowerCamelCase__						(     )				->						int:


					"""simple docstring"""





					_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_28)      )
					_UpperCamelCase									=    faiss.IndexFlatIP(1_28      )
					eli5_train_q_index.add(__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  lowerCamelCase__						(     __snake_case,	__snake_case=10      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    embed_questions_for_retrieval([question],	__snake_case,	__snake_case      )
					_UpperCamelCase					,    _UpperCamelCase									=    eli5_train_q_index.search(__snake_case,	__snake_case      )
					_UpperCamelCase									=    [elia_train[int(__snake_case      )] for i in I[0]]
					return nn_examples



def  lowerCamelCase__						(     __snake_case,	__snake_case="wiki40b",	__snake_case="dense",	__snake_case=10      )				->						List[str]:


					"""simple docstring"""





					if source == "none":
										_UpperCamelCase					,    _UpperCamelCase									=    (''' <P> '''.join(['''''' for _ in range(11      )]      ).strip(), [])
					else:
										if method == "dense":
															_UpperCamelCase					,    _UpperCamelCase									=    query_qa_dense_index(
															    __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
										else:
															_UpperCamelCase					,    _UpperCamelCase									=    query_es_index(
															    __snake_case,	__snake_case,	index_name='''english_wiki40b_snippets_100w''',	n_results=__snake_case,	)
					_UpperCamelCase									=    [
					    (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
					]
					_UpperCamelCase									=    '''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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=64,	__snake_case=2_56,	__snake_case=False,	__snake_case=2,	__snake_case=0.95,	__snake_case=0.8      )				->						Dict:


					"""simple docstring"""





					with torch.no_grad():
										_UpperCamelCase									=    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=10_24,	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)






