





"""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=a_)
def      __A   (      )		->		Dict:
				if LOAD_DENSE_INDEX:
								__a			:    Optional[Any]      							=	AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased''')
								__a			:    Dict      							=	AutoModel.from_pretrained('''yjernite/retribert-base-uncased''').to('''cuda:0''')
								__a			:    Union[str, Any]      							=	qar_model.eval()
				else:
								__a							,    __a			:    Optional[int]      							=	(None, None)
				if MODEL_TYPE == "bart":
								__a			:    List[Any]      							=	AutoTokenizer.from_pretrained('''yjernite/bart_eli5''')
								__a			:    Tuple      							=	AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5''').to('''cuda:0''')
								__a			:    Optional[int]      							=	torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth''')
								sas_model.load_state_dict(save_dict['''model'''])
								__a			:    List[Any]      							=	sas_model.eval()
				else:
								__a							,    __a			:    List[str]      							=	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=a_)
def      __A   (      )		->		Optional[Any]:
				if LOAD_DENSE_INDEX:
								__a			:    str      							=	faiss.StandardGpuResources()
								__a			:    Tuple      							=	datasets.load_dataset(path='''wiki_snippets''' ,							name='''wiki40b_en_100_0''')['''train''']
								__a			:    Dict      							=	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			:    Dict      							=	faiss.IndexFlatIP(1_28)
								__a			:    Optional[int]      							=	faiss.index_cpu_to_gpu(a_ ,							1 ,							a_)
								wikiaab_gpu_index_flat.add(a_)  # TODO fix for larger GPU
				else:
								__a							,    __a			:    List[str]      							=	(None, None)
				__a			:    int      							=	Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}])
				return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)

@st.cache(allow_output_mutation=a_)
def      __A   (      )		->		Union[str, Any]:
				__a			:    Union[str, Any]      							=	datasets.load_dataset('''eli5''' ,							name='''LFQA_reddit''')
				__a			:    List[str]      							=	elia['''train_eli5''']
				__a			:    Tuple      							=	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(a_)
				return (elia_train, eli5_train_q_index)


A      , A      , A			      =	load_indexes()
A      , A      , A      , A			      =	load_models()
A      , A			      =	load_train_data()

def      __A   (      a_    :str ,							a_    :str=10)		->		Dict:
				__a			:    List[Any]      							=	embed_questions_for_retrieval([question] ,							a_ ,							a_)
				__a							,    __a			:    Tuple      							=	eli5_train_q_index.search(a_ ,							a_)
				__a			:    Union[str, Any]      							=	[elia_train[int(a_)] for i in I[0]]
				return nn_examples

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

@st.cache(
    hash_funcs={
        torch.Tensor: (lambda a_: None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda a_: None),
    })
def      __A   (      a_    :Tuple ,							a_    :Any ,							a_    :Tuple ,							a_    :List[Any]=64 ,							a_    :int=2_56 ,							a_    :Any=False ,							a_    :Dict=2 ,							a_    :Dict=0.9_5 ,							a_    :List[Any]=0.8)		->		List[Any]:
				with torch.no_grad():
								__a			:    str      							=	qa_sas_generate(
								    a_ ,							a_ ,							a_ ,							num_answers=1 ,							num_beams=a_ ,							min_len=a_ ,							max_len=a_ ,							do_sample=a_ ,							temp=a_ ,							top_p=a_ ,							top_k=a_ ,							max_input_length=10_24 ,							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			      =	'''
<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
A			      =	'''
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)

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			      =	'''
    ### 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)
		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			      =	64
A			      =	256
A			      =	None
A			      =	None
A			      =	st.sidebar.checkbox('''Generation options''')
if generate_options:
		A			      =	'''
    ### 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)
		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=256, value=64, step=8, format=None, key=None
		)
		A			      =	st.sidebar.slider(
		    '''Maximum generation length''', min_value=64, max_value=512, value=256, step=16, 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=10)
						A      , A			      =	make_support(question, source=wiki_source, method='''sparse''', n_results=10)
						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[:10]
						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=10)
		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			      =	'''
---

**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"""


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase				 =					'''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''






def    _snake_case					(		):
	A     = _ask_options(
	    'In which compute environment are you running?'						,   ['This machine', 'AWS (Amazon SageMaker)']						,   _convert_compute_environment						,   )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		A     = get_sagemaker_input()
	else:
		A     = get_cluster_input()
	return config






def    _snake_case					(		snake_case__ :  Any=None  ):
	if subparsers is not None:
		A     = subparsers.add_parser('config'						,   description=snake_case__  )
	else:
		A     = argparse.ArgumentParser('Accelerate config command'						,   description=snake_case__  )

	parser.add_argument(
	    '--config_file'						,   default=snake_case__						,   help=(
	        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
	        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
	        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
	        'with \'huggingface\'.'
	    )						,   )

	if subparsers is not None:
		parser.set_defaults(func=snake_case__  )
	return parser






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = get_user_input()
	if args.config_file is not None:
		A     = args.config_file
	else:
		if not os.path.isdir(snake_case__  ):
			os.makedirs(snake_case__  )
		A     = default_yaml_config_file

	if config_file.endswith('.json'  ):
		config.to_json_file(snake_case__  )
	else:
		config.to_yaml_file(snake_case__  )
	print(F'accelerate configuration saved at {config_file}'  )






def    _snake_case					(		):
	A     = config_command_parser()
	A     = parser.parse_args()
	config_command(snake_case__  )


if __name__ == "__main__":
		main()