
"""simple docstring"""




from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

def lowercase_  (   ):
							import os as original_os
							from os import path as original_path
							from os import rename as original_rename
							from os.path import dirname as original_dirname
							from os.path import join as original_join

							assert _test_patching.os is original_os
							assert _test_patching.path is original_path
							assert _test_patching.join is original_join

							assert _test_patching.renamed_os is original_os
							assert _test_patching.renamed_path is original_path
							assert _test_patching.renamed_join is original_join

							SCREAMING_SNAKE_CASE__						:       Any           =   """__test_patch_submodule_mock__"""
							with patch_submodule(_test_patching   ,"""os.path.join"""   ,_UpperCAmelCase						):
														# Every way to access os.path.join must be patched, and the rest must stay untouched

														# check os.path.join
														assert isinstance(_test_patching.os   ,_PatchedModuleObj						)
														assert isinstance(_test_patching.os.path   ,_PatchedModuleObj						)
														assert _test_patching.os.path.join is mock

														# check path.join
														assert isinstance(_test_patching.path   ,_PatchedModuleObj						)
														assert _test_patching.path.join is mock

														# check join
														assert _test_patching.join is mock

														# check that the other attributes are untouched
														assert _test_patching.os.rename is original_rename
														assert _test_patching.path.dirname is original_dirname
														assert _test_patching.os.path.dirname is original_dirname

														# Even renamed modules or objects must be patched

														# check renamed_os.path.join
														assert isinstance(_test_patching.renamed_os   ,_PatchedModuleObj						)
														assert isinstance(_test_patching.renamed_os.path   ,_PatchedModuleObj						)
														assert _test_patching.renamed_os.path.join is mock

														# check renamed_path.join
														assert isinstance(_test_patching.renamed_path   ,_PatchedModuleObj						)
														assert _test_patching.renamed_path.join is mock

														# check renamed_join
														assert _test_patching.renamed_join is mock

														# check that the other attributes are untouched
														assert _test_patching.renamed_os.rename is original_rename
														assert _test_patching.renamed_path.dirname is original_dirname
														assert _test_patching.renamed_os.path.dirname is original_dirname

							# check that everthing is back to normal when the patch is over

							assert _test_patching.os is original_os
							assert _test_patching.path is original_path
							assert _test_patching.join is original_join

							assert _test_patching.renamed_os is original_os
							assert _test_patching.renamed_path is original_path
							assert _test_patching.renamed_join is original_join

def lowercase_  (   ):
							assert _test_patching.open is open

							SCREAMING_SNAKE_CASE__						:       int           =   """__test_patch_submodule_builtin_mock__"""
							# _test_patching has "open" in its globals
							assert _test_patching.open is open
							with patch_submodule(_test_patching   ,"""open"""   ,_UpperCAmelCase						):
														assert _test_patching.open is mock

							# check that everthing is back to normal when the patch is over

							assert _test_patching.open is open

def lowercase_  (   ):
							# pandas.read_csv is not present in _test_patching
							SCREAMING_SNAKE_CASE__						:       Dict           =   """__test_patch_submodule_missing_mock__"""
							with patch_submodule(_test_patching   ,"""pandas.read_csv"""   ,_UpperCAmelCase						):
														pass

def lowercase_  (   ):
							# builtin should always be mocked even if they're not in the globals
							# in case they're loaded at one point
							SCREAMING_SNAKE_CASE__						:       Tuple           =   """__test_patch_submodule_missing_builtin_mock__"""
							# _test_patching doesn't have "len" in its globals
							assert getattr(_test_patching   ,"""len"""   ,_UpperCAmelCase						) is None
							with patch_submodule(_test_patching   ,"""len"""   ,_UpperCAmelCase						):
														assert _test_patching.len is mock
							assert _test_patching.len is len

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       Any           =   """__test_patch_submodule_start_and_stop_mock__"""
							SCREAMING_SNAKE_CASE__						:       Dict           =   patch_submodule(_test_patching   ,"""open"""   ,_UpperCAmelCase						)
							assert _test_patching.open is open
							patch.start()
							assert _test_patching.open is mock
							patch.stop()
							assert _test_patching.open is open

def lowercase_  (   ):
							from os import rename as original_rename
							from os.path import dirname as original_dirname
							from os.path import join as original_join

							SCREAMING_SNAKE_CASE__						:       List[Any]           =   """__test_patch_submodule_successive_join__"""
							SCREAMING_SNAKE_CASE__						:       Any           =   """__test_patch_submodule_successive_dirname__"""
							SCREAMING_SNAKE_CASE__						:       Tuple           =   """__test_patch_submodule_successive_rename__"""
							assert _test_patching.os.path.join is original_join
							assert _test_patching.os.path.dirname is original_dirname
							assert _test_patching.os.rename is original_rename

							with patch_submodule(_test_patching   ,"""os.path.join"""   ,_UpperCAmelCase						):
														with patch_submodule(_test_patching   ,"""os.rename"""   ,_UpperCAmelCase						):
																					with patch_submodule(_test_patching   ,"""os.path.dirname"""   ,_UpperCAmelCase						):
																												assert _test_patching.os.path.join is mock_join
																												assert _test_patching.os.path.dirname is mock_dirname
																												assert _test_patching.os.rename is mock_rename

    # try another order
							with patch_submodule(_test_patching   ,"""os.rename"""   ,_UpperCAmelCase						):
														with patch_submodule(_test_patching   ,"""os.path.join"""   ,_UpperCAmelCase						):
																					with patch_submodule(_test_patching   ,"""os.path.dirname"""   ,_UpperCAmelCase						):
																												assert _test_patching.os.path.join is mock_join
																												assert _test_patching.os.path.dirname is mock_dirname
																												assert _test_patching.os.rename is mock_rename

							assert _test_patching.os.path.join is original_join
							assert _test_patching.os.path.dirname is original_dirname
							assert _test_patching.os.rename is original_rename

def lowercase_  (   ):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   """__test_patch_submodule_doesnt_exist_mock__"""
							with patch_submodule(_test_patching   ,"""__module_that_doesn_exist__.__attribute_that_doesn_exist__"""   ,_UpperCAmelCase						):
														pass
							with patch_submodule(_test_patching   ,"""os.__attribute_that_doesn_exist__"""   ,_UpperCAmelCase						):
														pass









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)





