





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=a)
def    __UpperCamelCase      (    ) ->Optional[int]:
					if LOAD_DENSE_INDEX:
										lowerCamelCase__       =       AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased")
										lowerCamelCase__       =       AutoModel.from_pretrained("yjernite/retribert-base-uncased").to("cuda:0")
										lowerCamelCase__       =       qar_model.eval()
					else:
										lowerCamelCase__    ,		lowerCamelCase__       =       (None, None)
					if MODEL_TYPE == "bart":
										lowerCamelCase__       =       AutoTokenizer.from_pretrained("yjernite/bart_eli5")
										lowerCamelCase__       =       AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5").to("cuda:0")
										lowerCamelCase__       =       torch.load("seq2seq_models/eli5_bart_model_blm_2.pth")
										sas_model.load_state_dict(save_dict["model"])
										lowerCamelCase__       =       sas_model.eval()
					else:
										lowerCamelCase__    ,		lowerCamelCase__       =       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=a)
def    __UpperCamelCase      (    ) ->str:
					if LOAD_DENSE_INDEX:
										lowerCamelCase__       =       faiss.StandardGpuResources()
										lowerCamelCase__       =       datasets.load_dataset(path="wiki_snippets",     name="wiki40b_en_100_0")["train"]
										lowerCamelCase__       =       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__       =       faiss.IndexFlatIP(128)
										lowerCamelCase__       =       faiss.index_cpu_to_gpu(a,     1,     a)
										wikiaab_gpu_index_flat.add(a)  # TODO fix for larger GPU
					else:
										lowerCamelCase__    ,		lowerCamelCase__       =       (None, None)
					lowerCamelCase__       =       Elasticsearch([{"host": "localhost", "port": "9200"}])
					return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)





@st.cache(allow_output_mutation=a)
def    __UpperCamelCase      (    ) ->Union[str, Any]:
					lowerCamelCase__       =       datasets.load_dataset("eli5",     name="LFQA_reddit")
					lowerCamelCase__       =       elia["train_eli5"]
					lowerCamelCase__       =       np.memmap(
					    "eli5_questions_reps.dat",     dtype="float32",     mode="r",     shape=(elia_train.num_rows, 128))
					lowerCamelCase__       =       faiss.IndexFlatIP(128)
					eli5_train_q_index.add(a)
					return (elia_train, eli5_train_q_index)


A_						,	A_						,	A_												=     load_indexes()
A_						,	A_						,	A_						,	A_												=     load_models()
A_						,	A_												=     load_train_data()





def    __UpperCamelCase      (    a,     a=10) ->Tuple:
					lowerCamelCase__       =       embed_questions_for_retrieval([question],     a,     a)
					lowerCamelCase__    ,		lowerCamelCase__       =       eli5_train_q_index.search(a,     a)
					lowerCamelCase__       =       [elia_train[int(a)] for i in I[0]]
					return nn_examples





def    __UpperCamelCase      (    a,     a="wiki40b",     a="dense",     a=10) ->List[Any]:
					if source == "none":
										lowerCamelCase__    ,		lowerCamelCase__       =       (" <P> ".join(["" for _ in range(11)]).strip(), [])
					else:
										if method == "dense":
															lowerCamelCase__    ,		lowerCamelCase__       =       query_qa_dense_index(
															    a,     a,     a,     a,     a,     a)
										else:
															lowerCamelCase__    ,		lowerCamelCase__       =       query_es_index(
															    a,     a,     index_name="english_wiki40b_snippets_100w",     n_results=a,     )
					lowerCamelCase__       =       [
					    (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
					]
					lowerCamelCase__       =       "question: {} context: {}".format(a,     a)
					return question_doc, support_list





@st.cache(
    hash_funcs={
        torch.Tensor: (lambda a: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a: None),
    })
def    __UpperCamelCase      (    a,     a,     a,     a=64,     a=256,     a=False,     a=2,     a=0.9_5,     a=0.8) ->List[Any]:
					with torch.no_grad():
										lowerCamelCase__       =       qa_sas_generate(
										    a,     a,     a,     num_answers=1,     num_beams=a,     min_len=a,     max_len=a,     do_sample=a,     temp=a,     top_p=a,     top_k=a,     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_												=     "\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
A_												=     "\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)

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_												=     "\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)
						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_												=     6_4
A_												=     2_5_6
A_												=     None
A_												=     None
A_												=     st.sidebar.checkbox("Generation options")
if generate_options:
						A_												=     "\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)
						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=2_5_6, value=6_4, step=8, format=None, key=None
						)
						A_												=     st.sidebar.slider(
						    "Maximum generation length", min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, 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=0.9_5, step=0.0_1, format=None, key=None
												)
												A_												=     st.sidebar.slider(
												    "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.0_1, 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=1_0)
																		A_						,	A_												=     make_support(question, source=wiki_source, method="sparse", n_results=1_0)
																		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[:1_0]
																		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=1_0)
						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_												=     "\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)








import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
						import torch


if is_vision_available():
						from PIL import Image

						from transformers import MgpstrProcessor, ViTImageProcessor

@require_torch
@require_vision
class SCREAMING_SNAKE_CASE_ (					unittest.TestCase   ):





					"""simple docstring"""


					A__				 =    ViTImageProcessor if is_vision_available() else None





					@property
					def 					__magic_name__ ( self	):
										return self.image_processor_tester.prepare_image_processor_dict()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       (3, 32, 128)
										lowerCamelCase__       =       tempfile.mkdtemp()

										# fmt: off
										lowerCamelCase__       =       ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
										# fmt: on
										lowerCamelCase__       =       dict(zip(_lowerCAmelCase	,  range(len(_lowerCAmelCase	)	)	)	)

										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["vocab_file"]	)
										with open(self.vocab_file	,  "w"	,  encoding="utf-8"	) as fp:
															fp.write(json.dumps(_lowerCAmelCase	) + "\n"	)

										lowerCamelCase__       =       {
										    "do_normalize": False,
										    "do_resize": True,
										    "image_processor_type": "ViTImageProcessor",
										    "resample": 3,
										    "size": {"height": 32, "width": 128},
										}
										lowerCamelCase__       =       os.path.join(self.tmpdirname	,  _lowerCAmelCase	)
										with open(self.image_processor_file	,  "w"	,  encoding="utf-8"	) as fp:
															json.dump(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return MgpstrTokenizer.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	,  **_lowerCAmelCase	):
										return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										shutil.rmtree(self.tmpdirname	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta	)

										lowerCamelCase__       =       Image.fromarray(np.moveaxis(_lowerCAmelCase	,  0	,  -1	)	)

										return image_input





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)
										lowerCamelCase__       =       MgpstrProcessor.from_pretrained(self.tmpdirname	,  use_fast=_lowerCAmelCase	)

										self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer.get_vocab()	)
										self.assertIsInstance(processor.char_tokenizer	,  _lowerCAmelCase	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_tokenizer()
										lowerCamelCase__       =       self.get_image_processor()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)
										processor.save_pretrained(self.tmpdirname	)

										lowerCamelCase__       =       self.get_tokenizer(bos_token="(BOS)"	,  eos_token="(EOS)"	)
										lowerCamelCase__       =       self.get_image_processor(do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										lowerCamelCase__       =       MgpstrProcessor.from_pretrained(
										    self.tmpdirname	,  bos_token="(BOS)"	,  eos_token="(EOS)"	,  do_normalize=_lowerCAmelCase	,  padding_value=1.0	)

										self.assertEqual(processor.char_tokenizer.get_vocab()	,  tokenizer_add_kwargs.get_vocab()	)
										self.assertIsInstance(processor.char_tokenizer	,  _lowerCAmelCase	)

										self.assertEqual(processor.image_processor.to_json_string()	,  image_processor_add_kwargs.to_json_string()	)
										self.assertIsInstance(processor.image_processor	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       image_processor(_lowerCAmelCase	,  return_tensors="np"	)
										lowerCamelCase__       =       processor(images=_lowerCAmelCase	,  return_tensors="np"	)

										for key in input_image_proc.keys():
															self.assertAlmostEqual(input_image_proc[key].sum()	,  input_processor[key].sum()	,  delta=1E-2	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "test"

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	)

										lowerCamelCase__       =       tokenizer(_lowerCAmelCase	)
										for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]	,  encoded_processor[key]	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       "test"
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  ["pixel_values", "labels"]	)

										# test if it raises when no input is passed
										with pytest.raises(_lowerCAmelCase	):
															processor()





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

										lowerCamelCase__       =       processor.char_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       tokenizer.batch_decode(_lowerCAmelCase	)
										lowerCamelCase__       =       [seq.replace(" "	,  ""	) for seq in decoded_tok]

										self.assertListEqual(_lowerCAmelCase	,  _lowerCAmelCase	)





					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       None
										lowerCamelCase__       =       self.prepare_image_inputs()

										lowerCamelCase__       =       processor(text=_lowerCAmelCase	,  images=_lowerCAmelCase	)

										self.assertListEqual(list(inputs.keys()	)	,  processor.model_input_names	)






					def 					__magic_name__ ( self	):
										lowerCamelCase__       =       self.get_image_processor()
										lowerCamelCase__       =       self.get_tokenizer()

										lowerCamelCase__       =       MgpstrProcessor(tokenizer=_lowerCAmelCase	,  image_processor=_lowerCAmelCase	)

										lowerCamelCase__       =       torch.randn(1	,  27	,  38	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  5_0257	)
										lowerCamelCase__       =       torch.randn(1	,  27	,  3_0522	)

										lowerCamelCase__       =       processor.batch_decode([char_input, bpe_input, wp_input]	)

										self.assertListEqual(list(results.keys()	)	,  ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"]	)

