
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  :					List[str]													=       'bart'
lowercase  :					int													=       True




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






							'''simple docstring'''





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




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






							'''simple docstring'''





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




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






							'''simple docstring'''





							__UpperCamelCase	:   int			   =					datasets.load_dataset("eli5"						,  name="LFQA_reddit")
							__UpperCamelCase	:   Any			   =					elia["train_eli5"]
							__UpperCamelCase	:   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(_lowerCamelCase)
							return (elia_train, eli5_train_q_index)


lowercase							,			lowercase							,			lowercase  :					Union[str, Any]													=       load_indexes()
lowercase							,			lowercase							,			lowercase							,			lowercase  :					str													=       load_models()
lowercase							,			lowercase  :					Dict													=       load_train_data()




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			Any=10)					->    Union[str, Any]:






							'''simple docstring'''





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




def 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			List[Any]						,  _lowerCamelCase :			Dict="wiki40b"						,  _lowerCamelCase :			Union[str, Any]="dense"						,  _lowerCamelCase :			int=10)					->    Union[str, Any]:






							'''simple docstring'''





							if source == "none":
														__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					(" <P> ".join(["" for _ in range(11)]).strip(), [])
							else:
														if method == "dense":
																					__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					query_qa_dense_index(
																					    _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase)
														else:
																					__UpperCamelCase   ,						__UpperCamelCase	:   List[Any]			   =					query_es_index(
																					    _lowerCamelCase						,  _lowerCamelCase						,  index_name="english_wiki40b_snippets_100w"						,  n_results=_lowerCamelCase						,  )
							__UpperCamelCase	:   List[str]			   =					[
							    (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
							]
							__UpperCamelCase	:   Tuple			   =					"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 	_SCREAMING_SNAKE_CASE					(		_lowerCamelCase :			Tuple						,  _lowerCamelCase :			Union[str, Any]						,  _lowerCamelCase :			str						,  _lowerCamelCase :			Dict=64						,  _lowerCamelCase :			Optional[Any]=256						,  _lowerCamelCase :			Union[str, Any]=False						,  _lowerCamelCase :			str=2						,  _lowerCamelCase :			Dict=0.9_5						,  _lowerCamelCase :			int=0.8)					->    Any:






							'''simple docstring'''





							with torch.no_grad():
														__UpperCamelCase	:   Optional[int]			   =					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=1_024						,  device="cuda:0"						,  )[0]
							return (answer, support_list)


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

# Start sidebar
lowercase  :					Any													=       '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
lowercase  :					List[Any]													=       '\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
lowercase  :					Tuple													=       '\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)

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

lowercase  :					List[str]													=       st.sidebar.checkbox('Retrieval options')
if retrieval_options:
		lowercase  :					Any													=       '\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)
		lowercase  :					List[str]													=       st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
		lowercase  :					Dict													=       st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
		lowercase  :					int													=       'wiki40b'
		lowercase  :					int													=       'dense'

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

# start main text
lowercase  :					List[Any]													=       [
    '<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  :					List[Any]													=       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  :					List[Any]													=       st.text_input('Enter your question here:', '')
else:
		lowercase  :					List[str]													=       question_s

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase  :					int													=       logging.get_logger(__name__)


lowercase  :					Union[str, Any]													=       '▁'

lowercase  :					Tuple													=       {'vocab_file': 'spiece.model'}

lowercase  :					Dict													=       {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

lowercase  :					Any													=       {
    'google/reformer-crime-and-punishment': 524288,
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







	def __init__(					self		:int     ,   a		:List[Any]     ,   a		:Tuple="</s>"     ,   a		:str="<unk>"     ,   a		:Dict=[]     ,   a		:Optional[Dict[str, Any]] = None     ,   **a		:Union[str, Any]     ,   )		-> None:
								__UpperCamelCase	:   str			   =					{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    eos_token=a     ,   unk_token=a     ,   additional_special_tokens=a     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **a     ,   )

								__UpperCamelCase	:   Optional[Any]			   =					vocab_file
								__UpperCamelCase	:   Dict			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								return self.sp_model.get_piece_size()







	def    _lowerCamelCase				(					self		:Optional[int]						)		-> Dict[str, int]:
								__UpperCamelCase	:   str			   =					{self.convert_ids_to_tokens(a						): i for i in range(self.vocab_size						)}
								vocab.update(self.added_tokens_encoder						)
								return vocab







	def __getstate__(					self		:str						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					self.__dict__.copy()
								__UpperCamelCase	:   Optional[Any]			   =					None
								return state







	def __setstate__(					self		:int     ,   a		:List[str]						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					d

								# for backward compatibility
								if not hasattr(self     ,   "sp_model_kwargs"						):
															__UpperCamelCase	:   int			   =					{}

								__UpperCamelCase	:   Any			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(self.vocab_file						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[Any]						)		-> str:
								return self.sp_model.piece_to_id(a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Union[str, Any]						)		-> Optional[int]:
								if index < self.sp_model.get_piece_size():
															__UpperCamelCase	:   Optional[int]			   =					self.sp_model.IdToPiece(a						)
								return token







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[]
								__UpperCamelCase	:   str			   =					""
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(a						) + token
																						__UpperCamelCase	:   List[Any]			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:Optional[str] = None						)		-> Tuple[str]:
								if not os.path.isdir(a						):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
															return
								__UpperCamelCase	:   List[Any]			   =					os.path.join(
								    a     ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

								if os.path.abspath(self.vocab_file						) != os.path.abspath(a						) and os.path.isfile(self.vocab_file						):
															copyfile(self.vocab_file     ,   a						)
								elif not os.path.isfile(self.vocab_file						):
															with open(a     ,   "wb"						) as fi:
																						__UpperCamelCase	:   int			   =					self.sp_model.serialized_model_proto()
																						fi.write(a						)

								return (out_vocab_file,)