


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      :    Optional[Any]		=					'''bart'''
A      :    Optional[int]		=					True

@st.cache(allow_output_mutation=__a						)
def       lowercase_		(		):

      """simple docstring"""




      if LOAD_DENSE_INDEX:
            lowerCamelCase__		:				List[str]    =  AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased"						)
            lowerCamelCase__		:				List[str]    =  AutoModel.from_pretrained("yjernite/retribert-base-uncased"						).to("cuda:0"						)
            lowerCamelCase__		:				List[str]    =  qar_model.eval()
      else:
            lowerCamelCase__    ,   lowerCamelCase__		:				str    =  (None, None)
      if MODEL_TYPE == "bart":
            lowerCamelCase__		:				str    =  AutoTokenizer.from_pretrained("yjernite/bart_eli5"						)
            lowerCamelCase__		:				str    =  AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5"						).to("cuda:0"						)
            lowerCamelCase__		:				Union[str, Any]    =  torch.load("seq2seq_models/eli5_bart_model_blm_2.pth"						)
            sas_model.load_state_dict(save_dict["model"]						)
            lowerCamelCase__		:				Dict    =  sas_model.eval()
      else:
            lowerCamelCase__    ,   lowerCamelCase__		:				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=__a						)
def       lowercase_		(		):

      """simple docstring"""




      if LOAD_DENSE_INDEX:
            lowerCamelCase__		:				Union[str, Any]    =  faiss.StandardGpuResources()
            lowerCamelCase__		:				Optional[Any]    =  datasets.load_dataset(path="wiki_snippets"    ,					name="wiki40b_en_100_0"						)["train"]
            lowerCamelCase__		:				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)    ,					)
            lowerCamelCase__		:				int    =  faiss.IndexFlatIP(128						)
            lowerCamelCase__		:				Any    =  faiss.index_cpu_to_gpu(__a    ,					1    ,					__a						)
            wikiaab_gpu_index_flat.add(__a						)  # TODO fix for larger GPU
      else:
            lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  (None, None)
      lowerCamelCase__		:				Any    =  Elasticsearch([{"host": "localhost", "port": "9200"}]						)
      return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)

@st.cache(allow_output_mutation=__a						)
def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				int    =  datasets.load_dataset("eli5"    ,					name="LFQA_reddit"						)
      lowerCamelCase__		:				int    =  elia["train_eli5"]
      lowerCamelCase__		:				Optional[int]    =  np.memmap(
          "eli5_questions_reps.dat"    ,					dtype="float32"    ,					mode="r"    ,					shape=(elia_train.num_rows, 128)						)
      lowerCamelCase__		:				List[Any]    =  faiss.IndexFlatIP(128						)
      eli5_train_q_index.add(__a						)
      return (elia_train, eli5_train_q_index)


A      :    str		=					load_indexes()
A      :    str		=					load_models()
A      :    Optional[Any]		=					load_train_data()

def       lowercase_		(		_A			:    int    ,					_A			:    List[str]=10						):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  embed_questions_for_retrieval([question]    ,					__a    ,					__a						)
      lowerCamelCase__    ,   lowerCamelCase__		:				Tuple    =  eli5_train_q_index.search(__a    ,					__a						)
      lowerCamelCase__		:				List[str]    =  [elia_train[int(__a						)] for i in I[0]]
      return nn_examples

def       lowercase_		(		_A			:    Optional[Any]    ,					_A			:    Any="wiki40b"    ,					_A			:    int="dense"    ,					_A			:    Any=10						):

      """simple docstring"""




      if source == "none":
            lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  (" <P> ".join(["" for _ in range(11						)]						).strip(), [])
      else:
            if method == "dense":
                  lowerCamelCase__    ,   lowerCamelCase__		:				str    =  query_qa_dense_index(
                      __a    ,					__a    ,					__a    ,					__a    ,					__a    ,					__a						)
            else:
                  lowerCamelCase__    ,   lowerCamelCase__		:				List[Any]    =  query_es_index(
                      __a    ,					__a    ,					index_name="english_wiki40b_snippets_100w"    ,					n_results=__a    ,					)
      lowerCamelCase__		:				Optional[Any]    =  [
          (res["article_title"], res["section_title"].strip(), res["score"], res["passage_text"]) for res in hit_lst
      ]
      lowerCamelCase__		:				Optional[int]    =  "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       lowercase_		(		_A			:    List[str]    ,					_A			:    List[Any]    ,					_A			:    Optional[Any]    ,					_A			:    Union[str, Any]=64    ,					_A			:    str=256    ,					_A			:    Union[str, Any]=False    ,					_A			:    Union[str, Any]=2    ,					_A			:    int=0.95    ,					_A			:    Any=0.8						):

      """simple docstring"""




      with torch.no_grad():
            lowerCamelCase__		:				Tuple    =  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=1024    ,					device="cuda:0"    ,					)[0]
      return (answer, support_list)


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

# Start sidebar
A      :    int		=					'''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
A      :    Optional[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
A      :    List[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)

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

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

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

# start main text
A      :    Optional[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?''',
]
A      :    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>":
    A      :    Any		=					st.text_input("Enter your question here:", "")
else:
    A      :    List[Any]		=					question_s

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


A      :    Any		=					'''
---

**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)









from collections import deque


class   A			:
			'''simple docstring'''




			def __init__(			self   :    List[str]							,	__lowerCAmelCase   :    str							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    int   )	->	None:







					"""simple docstring"""






					A__        =   process_name  # process name
					A__        =   arrival_time  # arrival time of the process
					# completion time of finished process or last interrupted time
					A__        =   arrival_time
					A__        =   burst_time  # remaining burst time
					A__        =   0  # total time of the process wait in ready queue
					A__        =   0  # time from arrival time to completion time


class   A			:
			'''simple docstring'''




			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    int							,	__lowerCAmelCase   :    list[int]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int							,	)	->	None:







					"""simple docstring"""






					A__        =   number_of_queues
					# time slice of queues that round robin algorithm applied
					A__        =   time_slices
					# unfinished process is in this ready_queue
					A__        =   queue
					# current time
					A__        =   current_time
					# finished process is in this sequence queue
					A__        =   deque()






			def    a_       (			self   :    Dict   )	->	list[str]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(self.finish_queue   )   ):
							sequence.append(self.finish_queue[i].process_name   )
					return sequence






			def    a_       (			self   :    Tuple							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							waiting_times.append(queue[i].waiting_time   )
					return waiting_times






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							turnaround_times.append(queue[i].turnaround_time   )
					return turnaround_times






			def    a_       (			self   :    Dict							,	__lowerCAmelCase   :    list[Process]   )	->	list[int]:







					"""simple docstring"""






					A__        =   []
					for i in range(len(__lowerCAmelCase   )   ):
							completion_times.append(queue[i].stop_time   )
					return completion_times






			def    a_       (			self   :    int							,	__lowerCAmelCase   :    deque[Process]   )	->	list[int]:







					"""simple docstring"""






					return [q.burst_time for q in queue]






			def    a_       (			self   :    Any							,	__lowerCAmelCase   :    Process   )	->	int:







					"""simple docstring"""






					process.waiting_time += self.current_time - process.stop_time
					return process.waiting_time






			def    a_       (			self   :    Union[str, Any]							,	__lowerCAmelCase   :    deque[Process]   )	->	deque[Process]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of finished process
					while len(__lowerCAmelCase   ) != 0:
							A__        =   ready_queue.popleft()  # current process

							# if process's arrival time is later than current time, update current time
							if self.current_time < cp.arrival_time:
									self.current_time += cp.arrival_time

							# update waiting time of current process
							self.update_waiting_time(__lowerCAmelCase   )
							# update current time
							self.current_time += cp.burst_time
							# finish the process and set the process's burst-time 0
							A__        =   0
							# set the process's turnaround time because it is finished
							A__        =   self.current_time - cp.arrival_time
							# set the completion time
							A__        =   self.current_time
							# add the process to queue that has finished queue
							finished.append(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# FCFS will finish all remaining processes
					return finished






			def    a_       (			self   :    Optional[Any]							,	__lowerCAmelCase   :    deque[Process]							,	__lowerCAmelCase   :    int   )	->	tuple[deque[Process], deque[Process]]:







					"""simple docstring"""






					A__        =   deque()  # sequence deque of terminated process
					# just for 1 cycle and unfinished processes will go back to queue
					for _ in range(len(__lowerCAmelCase   )   ):
							A__        =   ready_queue.popleft()  # current process

							# if process's arrival time is later than current time, update current time
							if self.current_time < cp.arrival_time:
									self.current_time += cp.arrival_time

							# update waiting time of unfinished processes
							self.update_waiting_time(__lowerCAmelCase   )
							# if the burst time of process is bigger than time-slice
							if cp.burst_time > time_slice:
									# use CPU for only time-slice
									self.current_time += time_slice
									# update remaining burst time
									cp.burst_time -= time_slice
									# update end point time
									A__        =   self.current_time
									# locate the process behind the queue because it is not finished
									ready_queue.append(__lowerCAmelCase   )
							else:
									# use CPU for remaining burst time
									self.current_time += cp.burst_time
									# set burst time 0 because the process is finished
									A__        =   0
									# set the finish time
									A__        =   self.current_time
									# update the process' turnaround time because it is finished
									A__        =   self.current_time - cp.arrival_time
									# add the process to queue that has finished queue
									finished.append(__lowerCAmelCase   )

					self.finish_queue.extend(__lowerCAmelCase   )  # add finished process to finish queue
					# return finished processes queue and remaining processes queue
					return finished, ready_queue







			def    a_       (			self   :    List[Any]   )	->	deque[Process]:







					"""simple docstring"""






					for i in range(self.number_of_queues - 1   ):
							A__      ,			A__        =   self.round_robin(
							    self.ready_queue							,	self.time_slices[i]   )
					#  the last queue has first_come_first_served algorithm
					self.first_come_first_served(self.ready_queue   )

					return self.finish_queue


if __name__ == "__main__":
					import doctest

					A    :					Union[str, Any] =      Process('''P1''', 0, 5_3)
					A    :					Optional[Any] =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					int =      Process('''P4''', 0, 2_4)
					A    :					Any =      3
					A    :					List[Any] =      [1_7, 2_5]
					A    :					Optional[Any] =      deque([Pa, Pa, Pa, Pa])

					if len(time_slices) != number_of_queues - 1:
										raise SystemExit(0)

					doctest.testmod(extraglobs={'''queue''': deque([Pa, Pa, Pa, Pa])})

					A    :					Optional[Any] =      Process('''P1''', 0, 5_3)
					A    :					int =      Process('''P2''', 0, 1_7)
					A    :					Optional[int] =      Process('''P3''', 0, 6_8)
					A    :					Tuple =      Process('''P4''', 0, 2_4)
					A    :					Union[str, Any] =      3
					A    :					Optional[Any] =      [1_7, 2_5]
					A    :					Tuple =      deque([Pa, Pa, Pa, Pa])
					A    :					Optional[int] =      MLFQ(number_of_queues, time_slices, queue, 0)
					A    :					Dict =      mlfq.multi_level_feedback_queue()

					# print total waiting times of processes(P1, P2, P3, P4)
					print(
					    F'''waiting time:\
        \t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print completion times of processes(P1, P2, P3, P4)
					print(
					    F'''completion time:\
        \t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print total turnaround times of processes(P1, P2, P3, P4)
					print(
					    F'''turnaround time:\
        \t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
					)
					# print sequence of finished processes
					print(
					    F'''sequence of finished processes:\
        {mlfq.calculate_sequence_of_finish_queue()}'''
					)



