





'''simple docstring'''






import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__UpperCamelCase					    =	logging.getLogger(__name__)




class  _A (					__lowercase		):

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Any  ,      __magic_name__					:   Union[str, Any]=None  ,      __magic_name__					:   Optional[Any]=None			)					->    str:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     self.layer[current_layer](__magic_name__  ,      __magic_name__  ,      head_mask[current_layer]			)

					__snake_case					:      Any							      =     layer_outputs[0]

					return hidden_states





@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'''						,					__lowercase						,					)
class  _A (					__lowercase		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   str			)					->    Dict:


					"""simple docstring"""

					super().__init__(__magic_name__			)

					__snake_case					:      Optional[Any]							      =     BertEncoderWithPabee(__magic_name__			)

					self.init_weights()
					__snake_case					:      Union[str, Any]							      =     0
					__snake_case					:      Union[str, Any]							      =     0
					__snake_case					:      Optional[int]							      =     0

					__snake_case					:      List[str]							      =     0

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Tuple			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     threshold

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   List[Any]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     patience

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     0
					__snake_case					:      Dict							      =     0

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    int:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.inference_layers_num / self.inference_instances_num
					__snake_case					:      Any							      =     (
					    f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
					    f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
					)
					print(__magic_name__			)

		@add_start_docstrings_to_model_forward(__magic_name__			)
		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   List[Any]=None  ,      __magic_name__					:   Optional[int]=None  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   int=None  ,      __magic_name__					:   Optional[int]=None  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   Optional[int]=False  ,      )					->    str:


					"""simple docstring"""

					if input_ids is not None and inputs_embeds is not None:
								raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""			)
					elif input_ids is not None:
								__snake_case					:      Union[str, Any]							      =     input_ids.size()
					elif inputs_embeds is not None:
								__snake_case					:      List[str]							      =     inputs_embeds.size()[:-1]
					else:
								raise ValueError("""You have to specify either input_ids or inputs_embeds"""			)

					__snake_case					:      Tuple							      =     input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
								__snake_case					:      int							      =     torch.ones(__magic_name__  ,      device=__magic_name__			)
					if token_type_ids is None:
								__snake_case					:      int							      =     torch.zeros(__magic_name__  ,      dtype=torch.long  ,      device=__magic_name__			)

					# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
					# ourselves in which case we just need to make it broadcastable to all heads.
					__snake_case					:      torch.Tensor							      =     self.get_extended_attention_mask(__magic_name__  ,      __magic_name__  ,      __magic_name__			)

					# If a 2D ou 3D attention mask is provided for the cross-attention
					# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
					if self.config.is_decoder and encoder_hidden_states is not None:
								__snake_case      ,     __snake_case      ,     __snake_case					:      str							      =     encoder_hidden_states.size()
								__snake_case					:      List[Any]							      =     (encoder_batch_size, encoder_sequence_length)
								if encoder_attention_mask is None:
											__snake_case					:      List[Any]							      =     torch.ones(__magic_name__  ,      device=__magic_name__			)
								__snake_case					:      Optional[Any]							      =     self.invert_attention_mask(__magic_name__			)
					else:
								__snake_case					:      Any							      =     None

					# Prepare head mask if needed
					# 1.0 in head_mask indicate we keep the head
					# attention_probs has shape bsz x n_heads x N x N
					# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
					# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
					__snake_case					:      List[Any]							      =     self.get_head_mask(__magic_name__  ,      self.config.num_hidden_layers			)

					__snake_case					:      Optional[Any]							      =     self.embeddings(
					    input_ids=__magic_name__  ,      position_ids=__magic_name__  ,      token_type_ids=__magic_name__  ,      inputs_embeds=__magic_name__			)
					__snake_case					:      Optional[int]							      =     embedding_output

					if self.training:
								__snake_case					:      Any							      =     []
								for i in range(self.config.num_hidden_layers			):
											__snake_case					:      Any							      =     self.encoder.adaptive_forward(
											    __magic_name__  ,      current_layer=__magic_name__  ,      attention_mask=__magic_name__  ,      head_mask=__magic_name__			)

											__snake_case					:      int							      =     self.pooler(__magic_name__			)
											__snake_case					:      Tuple							      =     output_layers[i](output_dropout(__magic_name__			)			)
											res.append(__magic_name__			)
					elif self.patience == 0:  # Use all layers for inference
								__snake_case					:      int							      =     self.encoder(
								    __magic_name__  ,      attention_mask=__magic_name__  ,      head_mask=__magic_name__  ,      encoder_hidden_states=__magic_name__  ,      encoder_attention_mask=__magic_name__  ,      )
								__snake_case					:      Tuple							      =     self.pooler(encoder_outputs[0]			)
								__snake_case					:      Optional[int]							      =     [output_layers[self.config.num_hidden_layers - 1](__magic_name__			)]
					else:
								__snake_case					:      List[str]							      =     0
								__snake_case					:      List[Any]							      =     None
								__snake_case					:      Optional[int]							      =     0
								for i in range(self.config.num_hidden_layers			):
											calculated_layer_num += 1
											__snake_case					:      List[Any]							      =     self.encoder.adaptive_forward(
											    __magic_name__  ,      current_layer=__magic_name__  ,      attention_mask=__magic_name__  ,      head_mask=__magic_name__			)

											__snake_case					:      Union[str, Any]							      =     self.pooler(__magic_name__			)
											__snake_case					:      str							      =     output_layers[i](__magic_name__			)
											if regression:
														__snake_case					:      str							      =     logits.detach()
														if patient_result is not None:
																	__snake_case					:      Any							      =     patient_result.detach()
														if (patient_result is not None) and torch.abs(patient_result - labels			) < self.regression_threshold:
																	patient_counter += 1
														else:
																	__snake_case					:      Any							      =     0
											else:
														__snake_case					:      int							      =     logits.detach().argmax(dim=1			)
														if patient_result is not None:
																	__snake_case					:      Any							      =     patient_result.detach().argmax(dim=1			)
														if (patient_result is not None) and torch.all(labels.eq(__magic_name__			)			):
																	patient_counter += 1
														else:
																	__snake_case					:      Tuple							      =     0

											__snake_case					:      Optional[int]							      =     logits
											if patient_counter == self.patience:
														break
								__snake_case					:      Tuple							      =     [patient_result]
								self.inference_layers_num += calculated_layer_num
								self.inference_instances_num += 1

					return res





@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. '''						,					__lowercase						,					)
class  _A (					__lowercase		):

		def __init__(   self					:   Union[str, Any]  ,      __magic_name__					:   str			)					->    Any:


					"""simple docstring"""

					super().__init__(__magic_name__			)
					__snake_case					:      List[str]							      =     config.num_labels

					__snake_case					:      Union[str, Any]							      =     BertModelWithPabee(__magic_name__			)
					__snake_case					:      str							      =     nn.Dropout(config.hidden_dropout_prob			)
					__snake_case					:      Union[str, Any]							      =     nn.ModuleList(
					    [nn.Linear(config.hidden_size  ,      self.config.num_labels			) for _ in range(config.num_hidden_layers			)]			)

					self.init_weights()

		@add_start_docstrings_to_model_forward(__magic_name__			)
		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   str=None  ,      __magic_name__					:   Union[str, Any]=None  ,      __magic_name__					:   str=None  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   str=None  ,      __magic_name__					:   Tuple=None  ,      )					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.bert(
					    input_ids=__magic_name__  ,      attention_mask=__magic_name__  ,      token_type_ids=__magic_name__  ,      position_ids=__magic_name__  ,      head_mask=__magic_name__  ,      inputs_embeds=__magic_name__  ,      output_dropout=self.dropout  ,      output_layers=self.classifiers  ,      regression=self.num_labels == 1  ,      )

					__snake_case					:      Optional[int]							      =     (logits[-1],)

					if labels is not None:
								__snake_case					:      List[str]							      =     None
								__snake_case					:      Optional[Any]							      =     0
								for ix, logits_item in enumerate(__magic_name__			):
											if self.num_labels == 1:
														#  We are doing regression
														__snake_case					:      Optional[int]							      =     MSELoss()
														__snake_case					:      List[Any]							      =     loss_fct(logits_item.view(-1			)  ,      labels.view(-1			)			)
											else:
														__snake_case					:      Union[str, Any]							      =     CrossEntropyLoss()
														__snake_case					:      List[Any]							      =     loss_fct(logits_item.view(-1  ,      self.num_labels			)  ,      labels.view(-1			)			)
											if total_loss is None:
														__snake_case					:      List[Any]							      =     loss
											else:
														total_loss += loss * (ix + 1)
											total_weights += ix + 1
								__snake_case					:      List[Any]							      =     (total_loss / total_weights,) + outputs

					return outputs









'''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


__UpperCamelCase					    =	"bart"
__UpperCamelCase					    =	True







@st.cache(allow_output_mutation=_lowerCamelCase )
def 					_a						(       ) ->				Union[str, Any]:



			"""simple docstring"""


			if LOAD_DENSE_INDEX:
						__snake_case					:      int							      =     AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased""" )
						__snake_case					:      Tuple							      =     AutoModel.from_pretrained("""yjernite/retribert-base-uncased""" ).to("""cuda:0""" )
						__snake_case					:      List[Any]							      =     qar_model.eval()
			else:
						__snake_case      ,     __snake_case					:      Optional[Any]							      =     (None, None)
			if MODEL_TYPE == "bart":
						__snake_case					:      List[str]							      =     AutoTokenizer.from_pretrained("""yjernite/bart_eli5""" )
						__snake_case					:      Any							      =     AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5""" ).to("""cuda:0""" )
						__snake_case					:      int							      =     torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth""" )
						sas_model.load_state_dict(save_dict["""model"""] )
						__snake_case					:      int							      =     sas_model.eval()
			else:
						__snake_case      ,     __snake_case					:      Dict							      =     make_qa_sas_model(
						    model_name="""t5-small"""    ,	from_file="""seq2seq_models/eli5_t5_model_1024_4.pth"""    ,	device="""cuda:0""" )
			return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)







@st.cache(allow_output_mutation=_lowerCamelCase )
def 					_a						(       ) ->				Tuple:



			"""simple docstring"""


			if LOAD_DENSE_INDEX:
						__snake_case					:      Tuple							      =     faiss.StandardGpuResources()
						__snake_case					:      Optional[Any]							      =     datasets.load_dataset(path="""wiki_snippets"""    ,	name="""wiki40b_en_100_0""" )["""train"""]
						__snake_case					:      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)    ,	)
						__snake_case					:      Optional[int]							      =     faiss.IndexFlatIP(128 )
						__snake_case					:      Any							      =     faiss.index_cpu_to_gpu(_lowerCamelCase    ,	1    ,	_lowerCamelCase )
						wikiaab_gpu_index_flat.add(_lowerCamelCase )  # TODO fix for larger GPU
			else:
						__snake_case      ,     __snake_case					:      Tuple							      =     (None, None)
			__snake_case					:      List[str]							      =     Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}] )
			return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)







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



			"""simple docstring"""


			__snake_case					:      Tuple							      =     datasets.load_dataset("""eli5"""    ,	name="""LFQA_reddit""" )
			__snake_case					:      Dict							      =     elia["""train_eli5"""]
			__snake_case					:      int							      =     np.memmap(
			    """eli5_questions_reps.dat"""    ,	dtype="""float32"""    ,	mode="""r"""    ,	shape=(elia_train.num_rows, 128) )
			__snake_case					:      Dict							      =     faiss.IndexFlatIP(128 )
			eli5_train_q_index.add(_lowerCamelCase )
			return (elia_train, eli5_train_q_index)


__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase					    =	load_indexes()
__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase    ,		__UpperCamelCase					    =	load_models()
__UpperCamelCase    ,		__UpperCamelCase					    =	load_train_data()







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase=10 ) ->				int:



			"""simple docstring"""


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







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase="wiki40b"    ,	_lowerCamelCase="dense"    ,	_lowerCamelCase=10 ) ->				Optional[Any]:



			"""simple docstring"""


			if source == "none":
						__snake_case      ,     __snake_case					:      Dict							      =     (""" <P> """.join(["""""" for _ in range(11 )] ).strip(), [])
			else:
						if method == "dense":
									__snake_case      ,     __snake_case					:      Dict							      =     query_qa_dense_index(
									    _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )
						else:
									__snake_case      ,     __snake_case					:      str							      =     query_es_index(
									    _lowerCamelCase    ,	_lowerCamelCase    ,	index_name="""english_wiki40b_snippets_100w"""    ,	n_results=_lowerCamelCase    ,	)
			__snake_case					:      Optional[int]							      =     [
			    (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
			]
			__snake_case					:      Optional[Any]							      =     """question: {} context: {}""".format(_lowerCamelCase    ,	_lowerCamelCase )
			return question_doc, support_list







@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _lowerCamelCase : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _lowerCamelCase : None),
    } )
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=64    ,	_lowerCamelCase=256    ,	_lowerCamelCase=False    ,	_lowerCamelCase=2    ,	_lowerCamelCase=0.95    ,	_lowerCamelCase=0.8 ) ->				List[str]:



			"""simple docstring"""


			with torch.no_grad():
						__snake_case					:      Union[str, Any]							      =     qa_sas_generate(
						    _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	num_answers=1    ,	num_beams=_lowerCamelCase    ,	min_len=_lowerCamelCase    ,	max_len=_lowerCamelCase    ,	do_sample=_lowerCamelCase    ,	temp=_lowerCamelCase    ,	top_p=_lowerCamelCase    ,	top_k=_lowerCamelCase    ,	max_input_length=1024    ,	device="""cuda:0"""    ,	)[0]
			return (answer, support_list)


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

# Start sidebar
__UpperCamelCase					    =	"<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
__UpperCamelCase					    =	"\n<html>\n  <head>\n    <style>\n      .img-container {\n        padding-left: 90px;\n        padding-right: 90px;\n        padding-top: 50px;\n        padding-bottom: 50px;\n        background-color: #f0f3f9;\n      }\n    </style>\n  </head>\n  <body>\n    <span class=\"img-container\"> <!-- Inline parent element -->\n      %s\n    </span>\n  </body>\n</html>\n" % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
__UpperCamelCase					    =	"\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n"
st.sidebar.markdown(description, unsafe_allow_html=True)

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

__UpperCamelCase					    =	st.sidebar.checkbox("Retrieval options")
if retrieval_options:
	__UpperCamelCase					    =	"\n    ### Information retriever options\n\n    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n    "
	st.sidebar.markdown(retriever_info)
	__UpperCamelCase					    =	st.sidebar.selectbox("Which Wikipedia format should the model use?", ["wiki40b", "none"])
	__UpperCamelCase					    =	st.sidebar.selectbox("Which Wikipedia indexer should the model use?", ["dense", "sparse", "mixed"])
else:
	__UpperCamelCase					    =	"wiki40b"
	__UpperCamelCase					    =	"dense"

__UpperCamelCase					    =	"beam"
__UpperCamelCase					    =	2
__UpperCamelCase					    =	64
__UpperCamelCase					    =	256
__UpperCamelCase					    =	None
__UpperCamelCase					    =	None
__UpperCamelCase					    =	st.sidebar.checkbox("Generation options")
if generate_options:
	__UpperCamelCase					    =	"\n    ### Answer generation options\n\n    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n    **beam** search, or **sample** from the decoder's output probabilities.\n    "
	st.sidebar.markdown(generate_info)
	__UpperCamelCase					    =	st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
	__UpperCamelCase					    =	st.sidebar.slider(
	    "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
	)
	__UpperCamelCase					    =	st.sidebar.slider(
	    "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
	)
	if sampled == "beam":
		__UpperCamelCase					    =	st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
	else:
		__UpperCamelCase					    =	st.sidebar.slider(
		    "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
		)
		__UpperCamelCase					    =	st.sidebar.slider(
		    "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
		)
		__UpperCamelCase					    =	None

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

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


__UpperCamelCase					    =	"\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n"

st.sidebar.markdown(disclaimer, unsafe_allow_html=True)


