




"""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=_lowercase )
def 	UpperCamelCase__  (		):
		if LOAD_DENSE_INDEX:
				snake_case		:    Tuple             =					AutoTokenizer.from_pretrained("yjernite/retribert-base-uncased" )
				snake_case		:    Optional[Any]             =					AutoModel.from_pretrained("yjernite/retribert-base-uncased" ).to("cuda:0" )
				snake_case		:    Optional[int]             =					qar_model.eval()
		else:
				snake_case		:    str             =					(None, None)
		if MODEL_TYPE == "bart":
				snake_case		:    Dict             =					AutoTokenizer.from_pretrained("yjernite/bart_eli5" )
				snake_case		:    Optional[Any]             =					AutoModelForSeqaSeqLM.from_pretrained("yjernite/bart_eli5" ).to("cuda:0" )
				snake_case		:    List[Any]             =					torch.load("seq2seq_models/eli5_bart_model_blm_2.pth" )
				sas_model.load_state_dict(save_dict["model"] )
				snake_case		:    Dict             =					sas_model.eval()
		else:
				snake_case		:    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=_lowercase )
def 	UpperCamelCase__  (		):
		if LOAD_DENSE_INDEX:
				snake_case		:    str             =					faiss.StandardGpuResources()
				snake_case		:    Optional[Any]             =					datasets.load_dataset(path="wiki_snippets"							,  name="wiki40b_en_100_0" )['train']
				snake_case		:    Union[str, 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)							,  )
				snake_case		:    Tuple             =					faiss.IndexFlatIP(128 )
				snake_case		:    Any             =					faiss.index_cpu_to_gpu(_lowercase							,  1							,  _lowercase )
				wikiaab_gpu_index_flat.add(_lowercase )  # TODO fix for larger GPU
		else:
				snake_case		:    Union[str, Any]             =					(None, None)
		snake_case		:    Optional[int]             =					Elasticsearch([{"host": "localhost", "port": "9200"}] )
		return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=_lowercase )
def 	UpperCamelCase__  (		):
		snake_case		:    int             =					datasets.load_dataset("eli5"							,  name="LFQA_reddit" )
		snake_case		:    Dict             =					elia['train_eli5']
		snake_case		:    Optional[int]             =					np.memmap(
		    "eli5_questions_reps.dat"							,  dtype="float32"							,  mode="r"							,  shape=(elia_train.num_rows, 128) )
		snake_case		:    Union[str, Any]             =					faiss.IndexFlatIP(128 )
		eli5_train_q_index.add(_lowercase )
		return (elia_train, eli5_train_q_index)


__A					=      load_indexes()
__A					=      load_models()
__A					=      load_train_data()



def 	UpperCamelCase__  (		lowercase__	:  str							,  lowercase__	:  Optional[int]=10 ):
		snake_case		:    int             =					embed_questions_for_retrieval([question]							,  _lowercase							,  _lowercase )
		snake_case		:    Dict             =					eli5_train_q_index.search(_lowercase							,  _lowercase )
		snake_case		:    int             =					[elia_train[int(_lowercase )] for i in I[0]]
		return nn_examples



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



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowercase__ : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowercase__ : None),
    } )
def 	UpperCamelCase__  (		lowercase__	:  Optional[int]							,  lowercase__	:  Dict							,  lowercase__	:  List[str]							,  lowercase__	:  List[Any]=64							,  lowercase__	:  List[Any]=256							,  lowercase__	:  str=False							,  lowercase__	:  Union[str, Any]=2							,  lowercase__	:  Dict=0.95							,  lowercase__	:  List[Any]=0.8 ):
		with torch.no_grad():
				snake_case		:    Any             =					qa_sas_generate(
				    _lowercase							,  _lowercase							,  _lowercase							,  num_answers=1							,  num_beams=_lowercase							,  min_len=_lowercase							,  max_len=_lowercase							,  do_sample=_lowercase							,  temp=_lowercase							,  top_p=_lowercase							,  top_k=_lowercase							,  max_input_length=1024							,  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					=      make_support(question, source=wiki_source, method="dense", n_results=10)
																					__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					=      make_support(question, source=wiki_source, method=index_type, n_results=10)
							if action in [0, 3]:
														__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)








from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


__UpperCamelCase:	Optional[int] = logging.get_logger(__name__)


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				["audio_values", "audio_mask"]

	def __init__(						self:						Any,					lowerCamelCase_:						int=2048,					lowerCamelCase_:						str=1,					lowerCamelCase_:						str=[16, 16],					lowerCamelCase_:						Union[str, Any]=128,					lowerCamelCase_:						Tuple=44100,					lowerCamelCase_:						Union[str, Any]=86,					lowerCamelCase_:						Optional[Any]=2048,					lowerCamelCase_:						Dict=0.0,					**lowerCamelCase_:						str,					):
			super().__init__(
			    feature_size=lowerCamelCase_,					sampling_rate=lowerCamelCase_,					padding_value=lowerCamelCase_,					**lowerCamelCase_,					)

			lowercase__					:      List[str]      =		spectrogram_length
			lowercase__					:      Union[str, Any]      =		num_channels
			lowercase__					:      int      =		patch_size
			lowercase__					:      Union[str, Any]      =		feature_size // self.patch_size[1]
			lowercase__					:      Optional[int]      =		n_fft
			lowercase__					:      int      =		sampling_rate // hop_length_to_sampling_rate
			lowercase__					:      str      =		sampling_rate
			lowercase__					:      Union[str, Any]      =		padding_value
			lowercase__					:      Any      =		mel_filter_bank(
			    num_frequency_bins=1 + n_fft // 2,					num_mel_filters=lowerCamelCase_,					min_frequency=0.0,					max_frequency=2_2_0_5_0.0,					sampling_rate=lowerCamelCase_,					norm='slaney',					mel_scale='slaney',					).T

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						np.array			):
			lowercase__					:      Optional[Any]      =		spectrogram(
			    lowerCamelCase_,					window_function(self.n_fft,					'hann'			),					frame_length=self.n_fft,					hop_length=self.hop_length,					power=2.0,					mel_filters=self.mel_filters.T,					log_mel='dB',					db_range=8_0.0,					)
			lowercase__					:      Optional[Any]      =		log_spec[:, :-1]
			lowercase__					:      int      =		log_spec - 2_0.0
			lowercase__					:      Dict      =		np.clip(log_spec / 4_0.0,					-2.0,					0.0			) + 1.0
			return log_spec





	def __call__(						self:						Dict,					lowerCamelCase_:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],					lowerCamelCase_:						Optional[Union[str, TensorType]] = None,					lowerCamelCase_:						Optional[bool] = True,					lowerCamelCase_:						Optional[int] = None,					lowerCamelCase_:						bool = False,					lowerCamelCase_:						bool = False,					**lowerCamelCase_:						str,					):

			if sampling_rate is not None:
					if sampling_rate != self.sampling_rate:
							raise ValueError(
							    'This feature extractor is set to support sampling rate'
							    F""" of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled"""
							    F""" with {self.sampling_rate} and not {sampling_rate}."""			)
			else:
					logger.warning(
					    'It is strongly recommended to pass the `sampling_rate` argument to this function. '
					    'Failing to do so can result in silent errors that might be hard to debug.'			)

			lowercase__					:      Any      =		isinstance(lowerCamelCase_,					np.ndarray			) and len(raw_speech.shape			) > 1
			if is_batched_numpy and len(raw_speech.shape			) > 2:
					raise ValueError(F"""Only mono-channel audio is supported for input to {self}"""			)
			lowercase__					:      Optional[Any]      =		is_batched_numpy or (
			    isinstance(lowerCamelCase_,					(list, tuple)			) and (isinstance(raw_speech[0],					(np.ndarray, tuple, list)			))
			)
			if is_batched:
					lowercase__					:      int      =		[np.asarray([speech],					dtype=np.floataa			).T for speech in raw_speech]
			elif not is_batched and not isinstance(lowerCamelCase_,					np.ndarray			):
					lowercase__					:      Optional[int]      =		np.asarray(lowerCamelCase_,					dtype=np.floataa			)
			elif isinstance(lowerCamelCase_,					np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
					lowercase__					:      str      =		raw_speech.astype(np.floataa			)
			# always return batch
			if not is_batched:
					lowercase__					:      Tuple      =		[np.asarray([raw_speech]			).T]

			# Convert audio signals to log mel spectrograms, truncate by time axis
			lowercase__					:      List[str]      =		[
			    self._np_extract_fbank_features(waveform.squeeze()			).T[: self.spectrogram_length] for waveform in raw_speech
			]
			if isinstance(audio_features[0],					lowerCamelCase_			):
					lowercase__					:      str      =		[np.asarray(lowerCamelCase_,					dtype=np.floataa			) for feature in audio_features]

			# Create audio attention mask
			lowercase__					:      Dict      =		max(
			    [ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len for feature in audio_features]			)  # The maximum number of audio patches in a batch
			if return_attention_mask:
					lowercase__					:      int      =		[
					    (ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [1]
					    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]			) * self.freq_len) * [0]
					    for feature in audio_features
					]
					lowercase__					:      Any      =		np.array(lowerCamelCase_			).astype(np.floataa			)

			# convert into correct format for padding
			lowercase__					:      Any      =		max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
			lowercase__					:      Tuple      =		np.ones([len(lowerCamelCase_			), 1, max_time_len, self.feature_size]			).astype(np.floataa			)
			lowercase__					:      Dict      =		padded_audio_features * self.padding_value
			for i in range(len(lowerCamelCase_			)			):
					lowercase__					:      Union[str, Any]      =		audio_features[i]
					lowercase__					:      str      =		feature

			# return as BatchFeature
			if return_attention_mask:
					lowercase__					:      Dict      =		{'audio_values': padded_audio_features, 'audio_mask': audio_mask}
			else:
					lowercase__					:      int      =		{'audio_values': padded_audio_features}

			lowercase__					:      List[Any]      =		BatchFeature(data=lowerCamelCase_,					tensor_type=lowerCamelCase_			)
			return encoded_inputs


