



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


__snake_case						:Dict      		=     '''bart'''
__snake_case						:Tuple      		=     True





@st.cache(allow_output_mutation=_UpperCAmelCase				)
def      __snake_case	(			):
    if LOAD_DENSE_INDEX:
        __a						       =				AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''				)
        __a						       =				AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''				).to('''cuda:0'''				)
        __a						       =				qar_model.eval()
    else:
        __a       ,   __a						       =				(None, None)
    if MODEL_TYPE == "bart":
        __a						       =				AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''				)
        __a						       =				AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''				).to('''cuda:0'''				)
        __a						       =				torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''				)
        sas_model.load_state_dict(save_dict['''model''']				)
        __a						       =				sas_model.eval()
    else:
        __a       ,   __a						       =				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=_UpperCAmelCase				)
def      __snake_case	(			):
    if LOAD_DENSE_INDEX:
        __a						       =				faiss.StandardGpuResources()
        __a						       =				datasets.load_dataset(path='''wiki_snippets'''   , name='''wiki40b_en_100_0'''				)['''train''']
        __a						       =				np.memmap(
            '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'''   , dtype='''float32'''   , mode='''r'''   , shape=(wikiaab_passages.num_rows, 128)   , )
        __a						       =				faiss.IndexFlatIP(128				)
        __a						       =				faiss.index_cpu_to_gpu(_UpperCAmelCase   , 1   , _UpperCAmelCase				)
        wikiaab_gpu_index_flat.add(_UpperCAmelCase				)  # TODO fix for larger GPU
    else:
        __a       ,   __a						       =				(None, None)
    __a						       =				Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]				)
    return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)





@st.cache(allow_output_mutation=_UpperCAmelCase				)
def      __snake_case	(			):
    __a						       =				datasets.load_dataset('''eli5'''   , name='''LFQA_reddit'''				)
    __a						       =				elia['''train_eli5''']
    __a						       =				np.memmap(
        '''eli5_questions_reps.dat'''   , dtype='''float32'''   , mode='''r'''   , shape=(elia_train.num_rows, 128)				)
    __a						       =				faiss.IndexFlatIP(128				)
    eli5_train_q_index.add(_UpperCAmelCase				)
    return (elia_train, eli5_train_q_index)


__snake_case		,__snake_case		,__snake_case						:List[str]      		=     load_indexes()
__snake_case		,__snake_case		,__snake_case		,__snake_case						:Dict      		=     load_models()
__snake_case		,__snake_case						:Tuple      		=     load_train_data()





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase=10				):
    __a						       =				embed_questions_for_retrieval([question]   , _UpperCAmelCase   , _UpperCAmelCase				)
    __a       ,   __a						       =				eli5_train_q_index.search(_UpperCAmelCase   , _UpperCAmelCase				)
    __a						       =				[elia_train[int(_UpperCAmelCase				)] for i in I[0]]
    return nn_examples





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





@st.cache(
    hash_funcs={
        torch.Tensor: (lambda _UpperCAmelCase				: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda _UpperCAmelCase				: None),
    }				)
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=64   , _UpperCAmelCase=256   , _UpperCAmelCase=False   , _UpperCAmelCase=2   , _UpperCAmelCase=0.95   , _UpperCAmelCase=0.8				):
    with torch.no_grad():
        __a						       =				qa_sas_generate(
            _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , num_answers=1   , num_beams=_UpperCAmelCase   , min_len=_UpperCAmelCase   , max_len=_UpperCAmelCase   , do_sample=_UpperCAmelCase   , temp=_UpperCAmelCase   , top_p=_UpperCAmelCase   , top_k=_UpperCAmelCase   , max_input_length=1024   , device='''cuda:0'''   , )[0]
    return (answer, support_list)


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

# Start sidebar
__snake_case						:Dict      		=     '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
__snake_case						:int      		=     '''
<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
__snake_case						:int      		=     '''
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)

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

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

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

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

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


__snake_case						:Tuple      		=     '''
---

**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)









import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
	from .tokenization_ta import TaTokenizer
else:
	lowerCAmelCase           =		None


lowerCAmelCase           =		logging.get_logger(__name__)

lowerCAmelCase           =		{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase           =		{
    '''vocab_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/tokenizer.json''',
        '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/tokenizer.json''',
        '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/tokenizer.json''',
        '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/tokenizer.json''',
        '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/tokenizer.json''',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase           =		{
    '''t5-small''': 5_1_2,
    '''t5-base''': 5_1_2,
    '''t5-large''': 5_1_2,
    '''t5-3b''': 5_1_2,
    '''t5-11b''': 5_1_2,
}





class       A    (   A_ ):
							UpperCamelCase_   :							Dict       =VOCAB_FILES_NAMES
							UpperCamelCase_   :							Dict       =PRETRAINED_VOCAB_FILES_MAP
							UpperCamelCase_   :							List[Any]       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCamelCase_   :							str       =['''input_ids''', '''attention_mask''']
							UpperCamelCase_   :							List[str]       =TaTokenizer

							UpperCamelCase_   :							List[int]       =[]


							def __init__(self      ,				lowerCAmelCase=None      ,				lowerCAmelCase=None      ,				lowerCAmelCase="</s>"      ,				lowerCAmelCase="<unk>"      ,				lowerCAmelCase="<pad>"      ,				lowerCAmelCase=1_0_0      ,				lowerCAmelCase=None      ,				**lowerCAmelCase      ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
											__lowercase=			[f'<extra_id_{i}>' for i in range(lowerCAmelCase					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
											# Check that we have the right number of extra special tokens
											__lowercase=			len(set(filter(lambda lowerCAmelCase					: bool('extra_id_' in str(lowerCAmelCase					)					)      ,				lowerCAmelCase					)					)					)
											if extra_tokens != extra_ids:
													raise ValueError(
													    f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
													    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
													    ' tokens'					)

									super().__init__(
									    lowerCAmelCase      ,				tokenizer_file=lowerCAmelCase      ,				eos_token=lowerCAmelCase      ,				unk_token=lowerCAmelCase      ,				pad_token=lowerCAmelCase      ,				extra_ids=lowerCAmelCase      ,				additional_special_tokens=lowerCAmelCase      ,				**lowerCAmelCase      ,				)

									__lowercase=			vocab_file
									__lowercase=			False if not self.vocab_file else True
									__lowercase=			extra_ids


							@staticmethod
							def 			_A       (lowerCAmelCase      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
											__lowercase=			TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
											if init_max_model_length is not None and init_max_model_length != max_model_length:
													return init_max_model_length
											elif init_max_model_length is None:
													warnings.warn(
													    'This tokenizer was incorrectly instantiated with a model max length of'
													    f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
													    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
													    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
													    f' {pretrained_model_name_or_path} automatically truncating your input to'
													    f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
													    f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
													    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
													    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'      ,				lowerCAmelCase      ,				)

									return max_model_length


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									if not self.can_save_slow_tokenizer:
											raise ValueError(
											    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
											    'tokenizer.'					)

									if not os.path.isdir(lowerCAmelCase					):
											logger.error(f'Vocabulary path ({save_directory}) should be a directory'					)
											return
									__lowercase=			os.path.join(
									    lowerCAmelCase      ,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowerCAmelCase					):
											copyfile(self.vocab_file      ,				lowerCAmelCase					)
											logger.info(f'Copy vocab file to {out_vocab_file}'					)

									return (out_vocab_file,)


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
											return self.prefix_tokens + token_ids_a
									else:
											__lowercase=			token_ids_a + [self.eos_token_id]
											return self.prefix_tokens + token_ids_a + token_ids_a


							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase = None					):
									__lowercase=			[self.eos_token_id]

									if token_ids_a is None:
											return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


							def 			_A       (self					):
									return list(
									    set(filter(lambda lowerCAmelCase					: bool(re.search(r'<extra_id_\d+>'      ,				lowerCAmelCase					)					) is not None      ,				self.additional_special_tokens					)					)					)







							def 			_A       (self					):
									return [self.convert_tokens_to_ids(lowerCAmelCase					) for token in self.get_sentinel_tokens()]






