


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)






import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


SCREAMING_SNAKE_CASE       :      Any												= logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE       :      Union[str, Any]												= {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE       :      str												= {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Optional[Any]												= {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


SCREAMING_SNAKE_CASE       :      List[Any]												= {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE       :      Dict												= {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}






class _lowerCamelCase(						_a   ):
       lowercase_ :	Any     =			VOCAB_FILES_NAMES
       lowercase_ :	Optional[int]     =			CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	str     =			CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION






class _lowerCamelCase(						_a   ):
       lowercase_ :	Optional[int]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	str     =			QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Union[str, Any]     =			QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION


SCREAMING_SNAKE_CASE       :      Optional[int]												= collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

SCREAMING_SNAKE_CASE       :      Any												= collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE       :      str												= r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "






@add_start_docstrings(_a   )
class _lowerCamelCase:
       def __call__(					self,      lowerCamelCase,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = False,      lowerCamelCase = False,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = None,      **lowerCamelCase,      )       ->   BatchEncoding:




           """simple docstring"""

           if titles is None and texts is None:
               return super().__call__(
                   lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           elif titles is None or texts is None:
               _lowercase       :							Dict      		=		titles if texts is None else texts
               return super().__call__(
                   lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase,      return_attention_mask=lowerCamelCase,      **lowerCamelCase,      )
           _lowercase       :							Union[str, Any]      		=		titles if not isinstance(lowerCamelCase,      lowerCamelCase) else [titles]
           _lowercase       :							Tuple      		=		texts if not isinstance(lowerCamelCase,      lowerCamelCase) else [texts]
           _lowercase       :							Optional[Any]      		=		len(lowerCamelCase)
           _lowercase       :							Any      		=		questions if not isinstance(lowerCamelCase,      lowerCamelCase) else [questions] * n_passages
           if len(lowerCamelCase) != len(lowerCamelCase):
               raise ValueError(
                   F'''There should be as many titles than texts but got {len(lowerCamelCase)} titles and {len(lowerCamelCase)} texts.''')
           _lowercase       :							Any      		=		super().__call__(lowerCamelCase,      lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							Tuple      		=		super().__call__(lowerCamelCase,      add_special_tokens=lowerCamelCase,      padding=lowerCamelCase,      truncation=lowerCamelCase)['input_ids']
           _lowercase       :							int      		=		{
               'input_ids': [
                   (encoded_question_and_title + encoded_text)[:max_length]
                   if max_length is not None and truncation
                   else encoded_question_and_title + encoded_text
                   for encoded_question_and_title, encoded_text in zip(lowerCamelCase,      lowerCamelCase)
               ]
           }
           if return_attention_mask is not False:
               _lowercase       :							Optional[Any]      		=		[]
               for input_ids in encoded_inputs["input_ids"]:
                   attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
               _lowercase       :							Union[str, Any]      		=		attention_mask
           return self.pad(lowerCamelCase,      padding=lowerCamelCase,      max_length=lowerCamelCase,      return_tensors=lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase = 16,      lowerCamelCase = 64,      lowerCamelCase = 4,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		reader_input['input_ids']
           _lowercase		,				_lowercase		,				_lowercase       :							Tuple      		=		reader_output[:3]
           _lowercase       :							Tuple      		=		len(lowerCamelCase)
           _lowercase       :							str      		=		sorted(range(lowerCamelCase),      reverse=lowerCamelCase,      key=relevance_logits.__getitem__)
           _lowercase       :							List[DPRReaderOutput]      		=		[]
           for doc_id in sorted_docs:
               _lowercase       :							str      		=		list(input_ids[doc_id])
               # assuming question & title information is at the beginning of the sequence
               _lowercase       :							Any      		=		sequence_ids.index(self.sep_token_id,      2) + 1  # second sep id
               if sequence_ids[-1] == self.pad_token_id:
                   _lowercase       :							List[Any]      		=		sequence_ids.index(self.pad_token_id)
               else:
                   _lowercase       :							List[str]      		=		len(lowerCamelCase)

               _lowercase       :							Tuple      		=		self._get_best_spans(
                   start_logits=start_logits[doc_id][passage_offset:sequence_len],      end_logits=end_logits[doc_id][passage_offset:sequence_len],      max_answer_length=lowerCamelCase,      top_spans=lowerCamelCase,      )
               for start_index, end_index in best_spans:
                   start_index += passage_offset
                   end_index += passage_offset
                   nbest_spans_predictions.append(
                       DPRSpanPrediction(
                           span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index],      relevance_score=relevance_logits[doc_id],      doc_id=lowerCamelCase,      start_index=lowerCamelCase,      end_index=lowerCamelCase,      text=self.decode(sequence_ids[start_index : end_index + 1]),      ))
               if len(lowerCamelCase) >= num_spans:
                   break
           return nbest_spans_predictions[:num_spans]




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      lowerCamelCase,      )       ->   List[DPRSpanPrediction]:




           """simple docstring"""

           _lowercase       :							str      		=		[]
           for start_index, start_score in enumerate(lowerCamelCase):
               for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
                   scores.append(((start_index, start_index + answer_length), start_score + end_score))
           _lowercase       :							Dict      		=		sorted(lowerCamelCase,      key=lambda lowerCamelCase: x[1],      reverse=lowerCamelCase)
           _lowercase       :							List[str]      		=		[]
           for (start_index, end_index), score in scores:
               if start_index > end_index:
                   raise ValueError(F'''Wrong span indices: [{start_index}:{end_index}]''')
               _lowercase       :							Dict      		=		end_index - start_index + 1
               if length > max_answer_length:
                   raise ValueError(F'''Span is too long: {length} > {max_answer_length}''')
               if any(
                   start_index <= prev_start_index <= prev_end_index <= end_index
                   or prev_start_index <= start_index <= end_index <= prev_end_index
                   for (prev_start_index, prev_end_index) in chosen_span_intervals):
                   continue
               chosen_span_intervals.append((start_index, end_index))

               if len(lowerCamelCase) == top_spans:
                   break
           return chosen_span_intervals






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a, _a   ):
       lowercase_ :	Union[str, Any]     =			VOCAB_FILES_NAMES
       lowercase_ :	Any     =			READER_PRETRAINED_VOCAB_FILES_MAP
       lowercase_ :	Dict     =			READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase_ :	Optional[int]     =			READER_PRETRAINED_INIT_CONFIGURATION
       lowercase_ :	str     =			["""input_ids""", """attention_mask"""]


