'''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


_A								=   'bart'
_A								=   True

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

@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE_							)
def _UpperCamelCase   (       ):
     lowercase_					:       int     				=					datasets.load_dataset('eli5'  ,       name='LFQA_reddit'							)
     lowercase_					:       Any     				=					elia['train_eli5']
     lowercase_					:       Dict     				=					np.memmap(
         'eli5_questions_reps.dat'  ,       dtype='float32'  ,       mode='r'  ,       shape=(elia_train.num_rows, 128)							)
     lowercase_					:       int     				=					faiss.IndexFlatIP(128							)
     eli5_train_q_index.add(SCREAMING_SNAKE_CASE_							)
     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   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=10							):
     lowercase_					:       Tuple     				=					embed_questions_for_retrieval([question]  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     lowercase_					:       str     				=					eli5_train_q_index.search(SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_							)
     lowercase_					:       Any     				=					[elia_train[int(SCREAMING_SNAKE_CASE_							)] for i in I[0]]
     return nn_examples

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

@st.cache(
    hash_funcs={
        torch.Tensor: (lambda SCREAMING_SNAKE_CASE_							: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE_							: None),
    }							)
def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_=64  ,       SCREAMING_SNAKE_CASE_=256  ,       SCREAMING_SNAKE_CASE_=False  ,       SCREAMING_SNAKE_CASE_=2  ,       SCREAMING_SNAKE_CASE_=0.95  ,       SCREAMING_SNAKE_CASE_=0.8							):
     with torch.no_grad():
          lowercase_					:       Optional[int]     				=					qa_sas_generate(
              SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       SCREAMING_SNAKE_CASE_  ,       num_answers=1  ,       num_beams=SCREAMING_SNAKE_CASE_  ,       min_len=SCREAMING_SNAKE_CASE_  ,       max_len=SCREAMING_SNAKE_CASE_  ,       do_sample=SCREAMING_SNAKE_CASE_  ,       temp=SCREAMING_SNAKE_CASE_  ,       top_p=SCREAMING_SNAKE_CASE_  ,       top_k=SCREAMING_SNAKE_CASE_  ,       max_input_length=1_024  ,       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=0.95, step=0.01, format=None, key=None
      )
      _A								=   st.sidebar.slider(
          'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, 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)





'''simple docstring'''




from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
 import tensorflow as tf

 from transformers import TFBlipTextModel
 from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class       UpperCAmelCase__  :






   """simple docstring"""
   def __init__(self       ,				_a       ,				_a=12       ,				_a=7       ,				_a=True       ,				_a=True       ,				_a=True       ,				_a=99       ,				_a=32       ,				_a=32       ,				_a=2       ,				_a=4       ,				_a=37       ,				_a=0.1       ,				_a=0.1       ,				_a=512       ,				_a=0.02       ,				_a=0       ,				_a=None       ,				)    ->     Optional[Any]:
        lowercase_					:       List[Any]     				=					parent
        lowercase_					:       List[Any]     				=					batch_size
        lowercase_					:       Optional[Any]     				=					seq_length
        lowercase_					:       Optional[Any]     				=					is_training
        lowercase_					:       Optional[int]     				=					use_input_mask
        lowercase_					:       Any     				=					use_labels
        lowercase_					:       Union[str, Any]     				=					vocab_size
        lowercase_					:       Union[str, Any]     				=					hidden_size
        lowercase_					:       str     				=					projection_dim
        lowercase_					:       str     				=					num_hidden_layers
        lowercase_					:       List[str]     				=					num_attention_heads
        lowercase_					:       List[str]     				=					intermediate_size
        lowercase_					:       Optional[Any]     				=					dropout
        lowercase_					:       Tuple     				=					attention_dropout
        lowercase_					:       Optional[Any]     				=					max_position_embeddings
        lowercase_					:       Any     				=					initializer_range
        lowercase_					:       List[str]     				=					scope
        lowercase_					:       Optional[Any]     				=					bos_token_id
   def 		_lowerCamelCase (self				)    ->     Tuple:
        lowercase_					:       Optional[Any]     				=					ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size				)

        lowercase_					:       Optional[int]     				=					None
        if self.use_input_mask:
             lowercase_					:       Dict     				=					random_attention_mask([self.batch_size, self.seq_length]				)

        if input_mask is not None:
             lowercase_					:       Dict     				=					input_mask.numpy()
             lowercase_				,lowercase_					:       int     				=					input_mask.shape
             lowercase_					:       int     				=					np.random.randint(1       ,				seq_length - 1       ,				size=(batch_size,)				)
             for batch_idx, start_index in enumerate(_a				):
                  lowercase_					:       Tuple     				=					1
                  lowercase_					:       List[Any]     				=					0

        lowercase_					:       List[Any]     				=					self.get_config()

        return config, input_ids, tf.convert_to_tensor(_a				)
   def 		_lowerCamelCase (self				)    ->     Any:
        return BlipTextConfig(
            vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				projection_dim=self.projection_dim       ,				num_hidden_layers=self.num_hidden_layers       ,				num_attention_heads=self.num_attention_heads       ,				intermediate_size=self.intermediate_size       ,				dropout=self.dropout       ,				attention_dropout=self.attention_dropout       ,				max_position_embeddings=self.max_position_embeddings       ,				initializer_range=self.initializer_range       ,				bos_token_id=self.bos_token_id       ,				)
   def 		_lowerCamelCase (self       ,				_a       ,				_a       ,				_a				)    ->     List[Any]:
        lowercase_					:       List[Any]     				=					TFBlipTextModel(config=_a				)
        lowercase_					:       List[str]     				=					model(_a       ,				attention_mask=_a       ,				training=_a				)
        lowercase_					:       Union[str, Any]     				=					model(_a       ,				training=_a				)
        self.parent.assertEqual(result.last_hidden_state.shape       ,				(self.batch_size, self.seq_length, self.hidden_size)				)
        self.parent.assertEqual(result.pooler_output.shape       ,				(self.batch_size, self.hidden_size)				)





   def 		_lowerCamelCase (self				)    ->     Dict:
        lowercase_					:       Optional[Any]     				=					self.prepare_config_and_inputs()
        lowercase_				,lowercase_				,lowercase_					:       Dict     				=					config_and_inputs
        lowercase_					:       Optional[int]     				=					{'input_ids': input_ids, 'attention_mask': input_mask}
        return config, inputs_dict



@require_tf
class       UpperCAmelCase__  (       _snake_case					,     unittest.TestCase			):






   """simple docstring"""
   A							:       Any     =    (TFBlipTextModel,) if is_tf_available() else ()
   A							:       Union[str, Any]     =    False
   A							:       int     =    False
   A							:       str     =    False
   def 		_lowerCamelCase (self				)    ->     int:
        lowercase_					:       Tuple     				=					BlipTextModelTester(self				)
        lowercase_					:       str     				=					ConfigTester(self       ,				config_class=_a       ,				hidden_size=37				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        self.config_tester.run_common_tests()
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        lowercase_					:       str     				=					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_a				)
   def 		_lowerCamelCase (self				)    ->     Dict:
        pass
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @unittest.skip(reason='Blip does not use inputs_embeds'				)
   def 		_lowerCamelCase (self				)    ->     List[Any]:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Any:
        pass
   @unittest.skip(reason='BlipTextModel has no base class and is not available in MODEL_MAPPING'				)
   def 		_lowerCamelCase (self				)    ->     Optional[int]:
        pass
   @slow
   def 		_lowerCamelCase (self				)    ->     Optional[Any]:
        for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_					:       List[str]     				=					TFBlipTextModel.from_pretrained(_a				)
             self.assertIsNotNone(_a				)





   def 		_lowerCamelCase (self       ,				_a=True				)    ->     Optional[int]:
        super().test_pt_tf_model_equivalence(allow_missing_keys=_a				)




