


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


SCREAMING_SNAKE_CASE       :      str												= "bart"
SCREAMING_SNAKE_CASE       :      Optional[int]												= True



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



@st.cache(allow_output_mutation=lowerCamelCase_ )
def 				UpperCamelCase_(      )    ->						Any:
    _lowercase       :							List[str]      		=		datasets.load_dataset('eli5'							, name='LFQA_reddit' )
    _lowercase       :							Optional[Any]      		=		elia['train_eli5']
    _lowercase       :							Tuple      		=		np.memmap(
        'eli5_questions_reps.dat'							, dtype='float32'							, mode='r'							, shape=(elia_train.num_rows, 128) )
    _lowercase       :							Union[str, Any]      		=		faiss.IndexFlatIP(128 )
    eli5_train_q_index.add(lowerCamelCase_ )
    return (elia_train, eli5_train_q_index)


SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Any												= load_indexes()
SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      Optional[Any]												= load_models()
SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE       :      List[Any]												= load_train_data()



def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_=10 )    ->						List[str]:
    _lowercase       :							Any      		=		embed_questions_for_retrieval([question]							, lowerCamelCase_							, lowerCamelCase_ )
    _lowercase		,				_lowercase       :							List[str]      		=		eli5_train_q_index.search(lowerCamelCase_							, lowerCamelCase_ )
    _lowercase       :							List[str]      		=		[elia_train[int(lowerCamelCase_ )] for i in I[0]]
    return nn_examples



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



@st.cache(
    hash_funcs={
        torch.Tensor: (lambda lowerCamelCase_ : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda lowerCamelCase_ : None),
    } )
def 				UpperCamelCase_(      lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_=64							, lowerCamelCase_=256							, lowerCamelCase_=False							, lowerCamelCase_=2							, lowerCamelCase_=0.95							, lowerCamelCase_=0.8 )    ->						Dict:
    with torch.no_grad():
        _lowercase       :							str      		=		qa_sas_generate(
            lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_							, num_answers=1							, num_beams=lowerCamelCase_							, min_len=lowerCamelCase_							, max_len=lowerCamelCase_							, do_sample=lowerCamelCase_							, temp=lowerCamelCase_							, top_p=lowerCamelCase_							, top_k=lowerCamelCase_							, max_input_length=1024							, device='cuda:0'							, )[0]
    return (answer, support_list)


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

# Start sidebar
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= "<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"
SCREAMING_SNAKE_CASE       :      List[Any]												= "\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
SCREAMING_SNAKE_CASE       :      Any												= "\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)

SCREAMING_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!",
]
SCREAMING_SNAKE_CASE       :      Optional[int]												= st.sidebar.checkbox("Demo options")
if demo_options:
 SCREAMING_SNAKE_CASE       :      List[str]												= st.sidebar.selectbox(
     "",
     action_list,
     index=3,
 )
 SCREAMING_SNAKE_CASE       :      Optional[int]												= action_list.index(action_st)
 SCREAMING_SNAKE_CASE       :      Tuple												= st.sidebar.selectbox(
     "",
     ["Show full text of passages", "Show passage section titles"],
     index=0,
 )
 SCREAMING_SNAKE_CASE       :      int												= show_type == "Show full text of passages"
else:
 SCREAMING_SNAKE_CASE       :      Any												= 3
 SCREAMING_SNAKE_CASE       :      Dict												= True

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

SCREAMING_SNAKE_CASE       :      str												= "beam"
SCREAMING_SNAKE_CASE       :      Optional[Any]												= 2
SCREAMING_SNAKE_CASE       :      List[str]												= 64
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= 256
SCREAMING_SNAKE_CASE       :      Union[str, Any]												= None
SCREAMING_SNAKE_CASE       :      List[Any]												= None
SCREAMING_SNAKE_CASE       :      str												= st.sidebar.checkbox("Generation options")
if generate_options:
 SCREAMING_SNAKE_CASE       :      Any												= "\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)
 SCREAMING_SNAKE_CASE       :      List[Any]												= st.sidebar.selectbox("Would you like to use beam search or sample an answer?", ["beam", "sampled"])
 SCREAMING_SNAKE_CASE       :      Tuple												= st.sidebar.slider(
     "Minimum generation length", min_value=8, max_value=256, value=64, step=8, format=None, key=None
 )
 SCREAMING_SNAKE_CASE       :      int												= st.sidebar.slider(
     "Maximum generation length", min_value=64, max_value=512, value=256, step=16, format=None, key=None
 )
 if sampled == "beam":
  SCREAMING_SNAKE_CASE       :      int												= st.sidebar.slider("Beam size", min_value=1, max_value=8, value=2, step=None, format=None, key=None)
 else:
  SCREAMING_SNAKE_CASE       :      Union[str, Any]												= st.sidebar.slider(
      "Nucleus sampling p", min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
  )
  SCREAMING_SNAKE_CASE       :      Any												= st.sidebar.slider(
      "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
  )
  SCREAMING_SNAKE_CASE       :      str												= None

# start main text
SCREAMING_SNAKE_CASE       :      List[str]												= [
    "<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?",
]
SCREAMING_SNAKE_CASE       :      str												= 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>":
 SCREAMING_SNAKE_CASE       :      List[str]												= st.text_input("Enter your question here:", "")
else:
 SCREAMING_SNAKE_CASE       :      Optional[int]												= question_s

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


SCREAMING_SNAKE_CASE       :      Tuple												= "\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 collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


__SCREAMING_SNAKE_CASE     :Dict       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :int       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : int           =							"""perceiver"""




 def __init__(					self       :    Any	,			snake_case_       :    List[Any]=2_5_6	,			snake_case_       :    str=1_2_8_0	,			snake_case_       :    Optional[int]=7_6_8	,			snake_case_       :    int=1	,			snake_case_       :    List[Any]=2_6	,			snake_case_       :    Dict=8	,			snake_case_       :    List[Any]=8	,			snake_case_       :    Tuple=None	,			snake_case_       :    Tuple=None	,			snake_case_       :    Any="kv"	,			snake_case_       :    Any=1	,			snake_case_       :    List[str]=1	,			snake_case_       :    Optional[int]="gelu"	,			snake_case_       :    List[Any]=0.1	,			snake_case_       :    Dict=0.0_2	,			snake_case_       :    int=1e-12	,			snake_case_       :    List[str]=True	,			snake_case_       :    str=2_6_2	,			snake_case_       :    Optional[Any]=2_0_4_8	,			snake_case_       :    Union[str, Any]=5_6	,			snake_case_       :    Dict=[3_6_8, 4_9_6]	,			snake_case_       :    Tuple=1_6	,			snake_case_       :    Union[str, Any]=1_9_2_0	,			snake_case_       :    List[Any]=1_6	,			snake_case_       :    Tuple=[1, 1_6, 2_2_4, 2_2_4]	,			**snake_case_       :    List[Any]	,			):
      super().__init__(**snake_case_    )

      _UpperCAmelCase     							=   num_latents
      _UpperCAmelCase     							=   d_latents
      _UpperCAmelCase     							=   d_model
      _UpperCAmelCase     							=   num_blocks
      _UpperCAmelCase     							=   num_self_attends_per_block
      _UpperCAmelCase     							=   num_self_attention_heads
      _UpperCAmelCase     							=   num_cross_attention_heads
      _UpperCAmelCase     							=   qk_channels
      _UpperCAmelCase     							=   v_channels
      _UpperCAmelCase     							=   cross_attention_shape_for_attention
      _UpperCAmelCase     							=   self_attention_widening_factor
      _UpperCAmelCase     							=   cross_attention_widening_factor
      _UpperCAmelCase     							=   hidden_act
      _UpperCAmelCase     							=   attention_probs_dropout_prob
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps
      _UpperCAmelCase     							=   use_query_residual
      # masked language modeling attributes
      _UpperCAmelCase     							=   vocab_size
      _UpperCAmelCase     							=   max_position_embeddings
      # image classification attributes
      _UpperCAmelCase     							=   image_size
      # flow attributes
      _UpperCAmelCase     							=   train_size
      # multimodal autoencoding attributes
      _UpperCAmelCase     							=   num_frames
      _UpperCAmelCase     							=   audio_samples_per_frame
      _UpperCAmelCase     							=   samples_per_patch
      _UpperCAmelCase     							=   output_shape







class   A_	(  lowerCAmelCase_			):




 @property
 def      lowercase	(					self       :    int    ):
      if self.task == "multiple-choice":
           _UpperCAmelCase     							=   {0: "batch", 1: "choice", 2: "sequence"}
      else:
           _UpperCAmelCase     							=   {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("inputs", dynamic_axis),
              ("attention_mask", dynamic_axis),
          ]    )




 @property
 def      lowercase	(					self       :    Optional[Any]    ):
      return 1e-4




 def      lowercase	(					self       :    List[str]	,			snake_case_       :    Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    int = -1	,			snake_case_       :    bool = False	,			snake_case_       :    Optional[TensorType] = None	,			snake_case_       :    int = 3	,			snake_case_       :    int = 4_0	,			snake_case_       :    int = 4_0	,			):
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(snake_case_	,			snake_case_    ):
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch	,			num_token_to_add=0    )
           # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   preprocessor.num_special_tokens_to_add(snake_case_    )
           _UpperCAmelCase     							=   compute_effective_axis_dimension(
               snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_sequence	,			num_token_to_add=snake_case_    )
           # Generate dummy inputs according to compute batch and sequence
           _UpperCAmelCase     							=   [" ".join(["a"]    ) * seq_length] * batch_size
           _UpperCAmelCase     							=   dict(preprocessor(snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("input_ids"    )
           return inputs
      elif isinstance(snake_case_	,			snake_case_    ) and preprocessor.model_input_names[0] == "pixel_values":
           # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
           _UpperCAmelCase     							=   compute_effective_axis_dimension(snake_case_	,			fixed_dimension=OnnxConfig.default_fixed_batch    )
           _UpperCAmelCase     							=   self._generate_dummy_images(snake_case_	,			snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   dict(preprocessor(images=snake_case_	,			return_tensors=snake_case_    )    )
           _UpperCAmelCase     							=   inputs.pop("pixel_values"    )
           return inputs
      else:
           raise ValueError(
               "Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor."    )




