



'''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


lowerCamelCase							:str       = '''bart'''
lowerCamelCase							:Any       = True



@st.cache(allow_output_mutation=lowerCamelCase__   )
def      a      (			):
			'''simple docstring'''





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





			if LOAD_DENSE_INDEX:
						A_     :						Optional[int]     =					faiss.StandardGpuResources()
						A_     :						Any     =					datasets.load_dataset(path="""wiki_snippets"""						,				name="""wiki40b_en_100_0"""   )["""train"""]
						A_     :						Tuple     =					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)						,				)
						A_     :						Any     =					faiss.IndexFlatIP(1_28   )
						A_     :						str     =					faiss.index_cpu_to_gpu(lowerCamelCase__						,				1						,				lowerCamelCase__   )
						wikiaab_gpu_index_flat.add(lowerCamelCase__   )  # TODO fix for larger GPU
			else:
						A_     :						Dict     =					(None, None)
			A_     :						Optional[Any]     =					Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]   )
			return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=lowerCamelCase__   )
def      a      (			):
			'''simple docstring'''





			A_     :						int     =					datasets.load_dataset("""eli5"""						,				name="""LFQA_reddit"""   )
			A_     :						Union[str, Any]     =					elia["""train_eli5"""]
			A_     :						Any     =					np.memmap(
			    """eli5_questions_reps.dat"""						,				dtype="""float32"""						,				mode="""r"""						,				shape=(elia_train.num_rows, 1_28)   )
			A_     :						Any     =					faiss.IndexFlatIP(1_28   )
			eli5_train_q_index.add(lowerCamelCase__   )
			return (elia_train, eli5_train_q_index)


lowerCamelCase							:Optional[int]       = load_indexes()
lowerCamelCase							:Dict       = load_models()
lowerCamelCase							:Optional[int]       = load_train_data()



def      a      (			lowerCamelCase__						,				lowerCamelCase__=10   ):
			'''simple docstring'''





			A_     :						Dict     =					embed_questions_for_retrieval([question]						,				lowerCamelCase__						,				lowerCamelCase__   )
			A_     :						Any     =					eli5_train_q_index.search(lowerCamelCase__						,				lowerCamelCase__   )
			A_     :						Dict     =					[elia_train[int(lowerCamelCase__   )] for i in I[0]]
			return nn_examples



def      a      (			lowerCamelCase__						,				lowerCamelCase__="wiki40b"						,				lowerCamelCase__="dense"						,				lowerCamelCase__=10   ):
			'''simple docstring'''





			if source == "none":
						A_     :						Any     =					(""" <P> """.join(["""""" for _ in range(11   )]   ).strip(), [])
			else:
						if method == "dense":
									A_     :						List[Any]     =					query_qa_dense_index(
									    lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__						,				lowerCamelCase__   )
						else:
									A_     :						List[str]     =					query_es_index(
									    lowerCamelCase__						,				lowerCamelCase__						,				index_name="""english_wiki40b_snippets_100w"""						,				n_results=lowerCamelCase__						,				)
			A_     :						int     =					[
			    (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
			]
			A_     :						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__=2_56						,				lowerCamelCase__=False						,				lowerCamelCase__=2						,				lowerCamelCase__=0.95						,				lowerCamelCase__=0.8   ):
			'''simple docstring'''





			with torch.no_grad():
						A_     :						List[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=10_24						,				device="""cuda:0"""						,				)[0]
			return (answer, support_list)


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

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

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

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

lowerCamelCase							:Any       = '''beam'''
lowerCamelCase							:Any       = 2
lowerCamelCase							:List[Any]       = 6_4
lowerCamelCase							:Union[str, Any]       = 2_5_6
lowerCamelCase							:Optional[Any]       = None
lowerCamelCase							:Tuple       = None
lowerCamelCase							:str       = st.sidebar.checkbox('''Generation options''')
if generate_options:
	lowerCamelCase							:List[str]       = '''
    ### 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)
	lowerCamelCase							:int       = st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
	lowerCamelCase							:Any       = st.sidebar.slider(
	    '''Minimum generation length''', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
	)
	lowerCamelCase							:List[str]       = 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":
		lowerCamelCase							:Dict       = st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
	else:
		lowerCamelCase							:Optional[Any]       = st.sidebar.slider(
		    '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
		)
		lowerCamelCase							:int       = st.sidebar.slider(
		    '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
		)
		lowerCamelCase							:Optional[int]       = None

# start main text
lowerCamelCase							: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?''',
]
lowerCamelCase							:Tuple       = 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>":
	lowerCamelCase							:str       = st.text_input('''Enter your question here:''', '''''')
else:
	lowerCamelCase							:Dict       = question_s

if st.button('''Show me!'''):
	if action in [0, 1, 3]:
		if index_type == "mixed":
			lowerCamelCase							:Tuple       = make_support(question, source=wiki_source, method='''dense''', n_results=1_0)
			lowerCamelCase							:Optional[Any]       = make_support(question, source=wiki_source, method='''sparse''', n_results=1_0)
			lowerCamelCase							:Optional[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)]
			lowerCamelCase							:Union[str, Any]       = support_list[:1_0]
			lowerCamelCase							:Any       = '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
		else:
			lowerCamelCase							:Dict       = make_support(question, source=wiki_source, method=index_type, n_results=1_0)
	if action in [0, 3]:
		lowerCamelCase							:str       = 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):
			lowerCamelCase							:Tuple       = '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
			lowerCamelCase							:Tuple       = res[1].strip()
			if sec_titles == "":
				lowerCamelCase							:Any       = '''[{}]({})'''.format(res[0], wiki_url)
			else:
				lowerCamelCase							:Any       = sec_titles.split(''' & ''')
				lowerCamelCase							: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]:
		lowerCamelCase							:str       = find_nearest_training(question)
		lowerCamelCase							:List[Any]       = nn_train_list[0]
		st.markdown(
		    '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
		)
		lowerCamelCase							:Union[str, 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)))


lowerCamelCase							:Dict       = '''
---

**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)




'''simple docstring'''




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)

lowerCamelCase							:Union[str, Any]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:Tuple       = {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase=False	,				lowercase=True	,				lowercase=False	,				lowercase="<s>"	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<sep>"	,				lowercase="<pad>"	,				lowercase="<cls>"	,				lowercase="<mask>"	,				lowercase=["<eop>", "<eod>"]	,				lowercase = None	,				**lowercase	,				):
				A_     :						Optional[int]     =					AddedToken(lowercase	,				lstrip=lowercase	,				rstrip=lowercase							) if isinstance(lowercase	,				lowercase							) else mask_token

				A_     :						str     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=lowercase	,				remove_space=lowercase	,				keep_accents=lowercase	,				bos_token=lowercase	,				eos_token=lowercase	,				unk_token=lowercase	,				sep_token=lowercase	,				pad_token=lowercase	,				cls_token=lowercase	,				mask_token=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				**lowercase	,				)

				A_     :						List[str]     =					3

				A_     :						List[str]     =					do_lower_case
				A_     :						Tuple     =					remove_space
				A_     :						Tuple     =					keep_accents
				A_     :						Tuple     =					vocab_file

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)

				try:
							import jieba
				except ModuleNotFoundError as error:
							raise error.__class__(
							    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
							    """See https://pypi.org/project/jieba/ for installation."""							)
				A_     :						Tuple     =					jieba
				A_     :						int     =					str.maketrans(""" \n"""	,				"""\u2582\u2583"""							)


	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def    _a     (self							):
				return len(self.sp_model							)


	def    _a     (self							):
				A_     :						str     =					{self.convert_ids_to_tokens(lowercase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def __getstate__(self							):
				A_     :						Optional[Any]     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						List[Any]     =					{}

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase							):
				if self.remove_space:
							A_     :						Union[str, Any]     =					""" """.join(inputs.strip().split()							)
				else:
							A_     :						Optional[int]     =					inputs
				A_     :						List[str]     =					outputs.replace("""``"""	,				"""\""""							).replace("""''"""	,				"""\""""							)

				if not self.keep_accents:
							A_     :						str     =					unicodedata.normalize("""NFKD"""	,				lowercase							)
							A_     :						Union[str, Any]     =					"""""".join([c for c in outputs if not unicodedata.combining(lowercase							)]							)
				if self.do_lower_case:
							A_     :						int     =					outputs.lower()

				return outputs


	def    _a     (self	,				lowercase							):
				A_     :						Optional[int]     =					self.preprocess_text(lowercase							)
				A_     :						Dict     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)
				A_     :						List[Any]     =					[]
				for piece in pieces:
							if len(lowercase							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
										A_     :						Union[str, Any]     =					self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase	,				""""""							)							)
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
													if len(cur_pieces[0]							) == 1:
																A_     :						Dict     =					cur_pieces[1:]
													else:
																A_     :						str     =					cur_pieces[0][1:]
										cur_pieces.append(piece[-1]							)
										new_pieces.extend(lowercase							)
							else:
										new_pieces.append(lowercase							)

				return new_pieces


	def    _a     (self	,				lowercase							):
				return self.sp_model.PieceToId(lowercase							)


	def    _a     (self	,				lowercase							):
				return self.sp_model.IdToPiece(lowercase							)


	def    _a     (self	,				lowercase							):
				A_     :						int     =					"""""".join(lowercase							).replace(lowercase	,				""" """							).strip()
				return out_string


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						List[str]     =					[self.sep_token_id]
				A_     :						Any     =					[self.cls_token_id]
				if token_ids_a is None:
							return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):

				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase	,				token_ids_a=lowercase	,				already_has_special_tokens=lowercase							)

				if token_ids_a is not None:
							return ([0] * len(lowercase							)) + [1] + ([0] * len(lowercase							)) + [1, 1]
				return ([0] * len(lowercase							)) + [1, 1]


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						str     =					[self.sep_token_id]
				A_     :						str     =					[2]

				if token_ids_a is None:
							return len(token_ids_a + sep							) * [0] + cls_segment_id
				return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if not os.path.isdir(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Tuple     =					os.path.join(
				    lowercase	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

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

				return (out_vocab_file,)


	def    _a     (self	,				*lowercase	,				**lowercase							):
				A_     :						Any     =					super()._decode(*lowercase	,				**lowercase							)
				A_     :						int     =					text.replace(""" """	,				""""""							).replace("""\u2582"""	,				""" """							).replace("""\u2583"""	,				"""\n"""							)
				return text