




"""simple docstring"""






import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


lowercase__     						=						NewType("""DataClass""", Any)
lowercase__     						=						NewType("""DataClassType""", Any)







def      _snake_case			(				lowercase__		):
				if isinstance(_snake_case							,    _snake_case		):
								return v
				if v.lower() in ("yes", "true", "t", "y", "1"):
								return True
				elif v.lower() in ("no", "false", "f", "n", "0"):
								return False
				else:
								raise ArgumentTypeError(
								    f'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''		)







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Dict    =    {str(_snake_case		): choice for choice in choices}
				return lambda lowercase__		: str_to_choice.get(_snake_case							,    _snake_case		)







def      _snake_case			(				*,
    lowercase__ = None							,    lowercase__ = None							,    lowercase__ = dataclasses.MISSING							,    lowercase__ = dataclasses.MISSING							,    lowercase__ = None							,    **lowercase__							,    ):
				if metadata is None:
								# Important, don't use as default param in function signature because dict is mutable and shared across function calls
								_lowerCamelCase     :			Optional[int]    =    {}
				if aliases is not None:
								_lowerCamelCase     :			Optional[Any]    =    aliases
				if help is not None:
								_lowerCamelCase     :			Optional[Any]    =    help

				return dataclasses.field(metadata=_snake_case							,    default=_snake_case							,    default_factory=_snake_case							,    **_snake_case		)






class lowerCAmelCase__	(     a__						):






			'''simple docstring'''



			lowerCamelCase__      =			42
			def __init__(  self  ,      lowercase  ,      **lowercase  ):
							# To make the default appear when using --help
							if "formatter_class" not in kwargs:
											_lowerCamelCase     :			Union[str, Any]    =    ArgumentDefaultsHelpFormatter
							super().__init__(**UpperCamelCase_  )
							if dataclasses.is_dataclass(UpperCamelCase_  ):
											_lowerCamelCase     :			Optional[int]    =    [dataclass_types]
							_lowerCamelCase     :			Optional[Any]    =    list(UpperCamelCase_  )
							for dtype in self.dataclass_types:
											self._add_dataclass_arguments(UpperCamelCase_  )
			@staticmethod
			def 					A_    (  lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    F'''--{field.name}'''
							_lowerCamelCase     :			Tuple    =    field.metadata.copy()
							# field.metadata is not used at all by Data Classes,
							# it is provided as a third-party extension mechanism.
							if isinstance(field.type  ,      UpperCamelCase_  ):
											raise RuntimeError(
											    'Unresolved type detected, which should have been done with the help of '
											    '`typing.get_type_hints` method by default'  )

							_lowerCamelCase     :			List[str]    =    kwargs.pop('aliases'  ,      []  )
							if isinstance(UpperCamelCase_  ,      UpperCamelCase_  ):
											_lowerCamelCase     :			Dict    =    [aliases]

							_lowerCamelCase     :			Tuple    =    getattr(field.type  ,      '__origin__'  ,      field.type  )
							if origin_type is Union or (hasattr(UpperCamelCase_  ,      'UnionType'  ) and isinstance(UpperCamelCase_  ,      types.UnionType  )):
											if str not in field.type.__args__ and (
											    len(field.type.__args__  ) != 2 or type(UpperCamelCase_  ) not in field.type.__args__
											):
															raise ValueError(
															    'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
															    ' the argument parser only supports one type per argument.'
															    F''' Problem encountered in field \'{field.name}\'.'''  )
											if type(UpperCamelCase_  ) not in field.type.__args__:
															# filter `str` in Union
															_lowerCamelCase     :			str    =    field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
															_lowerCamelCase     :			Tuple    =    getattr(field.type  ,      '__origin__'  ,      field.type  )
											elif bool not in field.type.__args__:
															# filter `NoneType` in Union (except for `Union[bool, NoneType]`)
															_lowerCamelCase     :			str    =    (
															    field.type.__args__[0] if isinstance(UpperCamelCase_  ,      field.type.__args__[1]  ) else field.type.__args__[1]
															)
															_lowerCamelCase     :			Union[str, Any]    =    getattr(field.type  ,      '__origin__'  ,      field.type  )

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
							_lowerCamelCase     :			Optional[Any]    =    {}
							if origin_type is Literal or (isinstance(field.type  ,      UpperCamelCase_  ) and issubclass(field.type  ,      UpperCamelCase_  )):
											if origin_type is Literal:
															_lowerCamelCase     :			Dict    =    field.type.__args__
											else:
															_lowerCamelCase     :			Tuple    =    [x.value for x in field.type]

											_lowerCamelCase     :			Tuple    =    make_choice_type_function(kwargs['choices']  )

											if field.default is not dataclasses.MISSING:
															_lowerCamelCase     :			str    =    field.default
											else:
															_lowerCamelCase     :			Optional[Any]    =    True
							elif field.type is bool or field.type == Optional[bool]:
											# Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
											# We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
											_lowerCamelCase     :			Any    =    copy(UpperCamelCase_  )

											# Hack because type=bool in argparse does not behave as we want.
											_lowerCamelCase     :			List[Any]    =    string_to_bool
											if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
															# Default value is False if we have no default when of type bool.
															_lowerCamelCase     :			int    =    False if field.default is dataclasses.MISSING else field.default
															# This is the value that will get picked if we don't include --field_name in any way
															_lowerCamelCase     :			List[str]    =    default
															# This tells argparse we accept 0 or 1 value after --field_name
															_lowerCamelCase     :			List[Any]    =    '''?'''
															# This is the value that will get picked if we do --field_name (without value)
															_lowerCamelCase     :			Optional[Any]    =    True
							elif isclass(UpperCamelCase_  ) and issubclass(UpperCamelCase_  ,      UpperCamelCase_  ):
											_lowerCamelCase     :			List[Any]    =    field.type.__args__[0]
											_lowerCamelCase     :			int    =    '''+'''
											if field.default_factory is not dataclasses.MISSING:
															_lowerCamelCase     :			int    =    field.default_factory()
											elif field.default is dataclasses.MISSING:
															_lowerCamelCase     :			Any    =    True
							else:
											_lowerCamelCase     :			Tuple    =    field.type
											if field.default is not dataclasses.MISSING:
															_lowerCamelCase     :			Union[str, Any]    =    field.default
											elif field.default_factory is not dataclasses.MISSING:
															_lowerCamelCase     :			Tuple    =    field.default_factory()
											else:
															_lowerCamelCase     :			Union[str, Any]    =    True
							parser.add_argument(UpperCamelCase_  ,      *UpperCamelCase_  ,      **UpperCamelCase_  )

							# Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
							# Order is important for arguments with the same destination!
							# We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
							# here and we do not need those changes/additional keys.
							if field.default is True and (field.type is bool or field.type == Optional[bool]):
											_lowerCamelCase     :			Optional[Any]    =    False
											parser.add_argument(F'''--no_{field.name}'''  ,      action='store_false'  ,      dest=field.name  ,      **UpperCamelCase_  )
			def 					A_    (  self  ,      lowercase  ):
							if hasattr(UpperCamelCase_  ,      '_argument_group_name'  ):
											_lowerCamelCase     :			Union[str, Any]    =    self.add_argument_group(dtype._argument_group_name  )
							else:
											_lowerCamelCase     :			Dict    =    self

							try:
											_lowerCamelCase     :			Dict[str, type]    =    get_type_hints(UpperCamelCase_  )
							except NameError:
											raise RuntimeError(
											    F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
											    'removing line of `from __future__ import annotations` which opts in Postponed '
											    'Evaluation of Annotations (PEP 563)'  )
							except TypeError as ex:
											# Remove this block when we drop Python 3.9 support
											if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(UpperCamelCase_  ):
															_lowerCamelCase     :			List[Any]    =    '''.'''.join(map(UpperCamelCase_  ,      sys.version_info[:3]  )  )
															raise RuntimeError(
															    F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
															    'line of `from __future__ import annotations` which opts in union types as '
															    '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
															    'support Python versions that lower than 3.10, you need to use '
															    '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
															    '`X | None`.'  ) from ex
											raise

							for field in dataclasses.fields(UpperCamelCase_  ):
											if not field.init:
															continue
											_lowerCamelCase     :			Any    =    type_hints[field.name]
											self._parse_dataclass_field(UpperCamelCase_  ,      UpperCamelCase_  )
			def 					A_    (  self  ,      lowercase=None  ,      lowercase=False  ,      lowercase=True  ,      lowercase=None  ,      lowercase=None  ,      ):

							if args_file_flag or args_filename or (look_for_args_file and len(sys.argv  )):
											_lowerCamelCase     :			Optional[Any]    =    []

											if args_filename:
															args_files.append(Path(UpperCamelCase_  )  )
											elif look_for_args_file and len(sys.argv  ):
															args_files.append(Path(sys.argv[0]  ).with_suffix('.args'  )  )

											# args files specified via command line flag should overwrite default args files so we add them last
											if args_file_flag:
															# Create special parser just to extract the args_file_flag values
															_lowerCamelCase     :			Any    =    ArgumentParser()
															args_file_parser.add_argument(UpperCamelCase_  ,      type=UpperCamelCase_  ,      action='append'  )

															# Use only remaining args for further parsing (remove the args_file_flag)
															_lowerCamelCase     :			Optional[Any]    =    args_file_parser.parse_known_args(args=UpperCamelCase_  )
															_lowerCamelCase     :			str    =    vars(UpperCamelCase_  ).get(args_file_flag.lstrip('-'  )  ,      UpperCamelCase_  )

															if cmd_args_file_paths:
																			args_files.extend([Path(UpperCamelCase_  ) for p in cmd_args_file_paths]  )

											_lowerCamelCase     :			str    =    []
											for args_file in args_files:
															if args_file.exists():
																			file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
											_lowerCamelCase     :			Optional[int]    =    file_args + args if args is not None else file_args + sys.argv[1:]
							_lowerCamelCase     :			Union[str, Any]    =    self.parse_known_args(args=UpperCamelCase_  )
							_lowerCamelCase     :			Any    =    []
							for dtype in self.dataclass_types:
											_lowerCamelCase     :			Tuple    =    {f.name for f in dataclasses.fields(UpperCamelCase_  ) if f.init}
											_lowerCamelCase     :			Tuple    =    {k: v for k, v in vars(UpperCamelCase_  ).items() if k in keys}
											for k in keys:
															delattr(UpperCamelCase_  ,      UpperCamelCase_  )
											_lowerCamelCase     :			Union[str, Any]    =    dtype(**UpperCamelCase_  )
											outputs.append(UpperCamelCase_  )
							if len(namespace.__dict__  ) > 0:
											# additional namespace.
											outputs.append(UpperCamelCase_  )
							if return_remaining_strings:
											return (*outputs, remaining_args)
							else:
											if remaining_args:
															raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''  )

											return (*outputs,)
			def 					A_    (  self  ,      lowercase  ,      lowercase = False  ):
							_lowerCamelCase     :			List[Any]    =    set(args.keys()  )
							_lowerCamelCase     :			Optional[int]    =    []
							for dtype in self.dataclass_types:
											_lowerCamelCase     :			int    =    {f.name for f in dataclasses.fields(UpperCamelCase_  ) if f.init}
											_lowerCamelCase     :			List[Any]    =    {k: v for k, v in args.items() if k in keys}
											unused_keys.difference_update(inputs.keys()  )
											_lowerCamelCase     :			List[Any]    =    dtype(**UpperCamelCase_  )
											outputs.append(UpperCamelCase_  )
							if not allow_extra_keys and unused_keys:
											raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(UpperCamelCase_  )}'''  )
							return tuple(UpperCamelCase_  )
			def 					A_    (  self  ,      lowercase  ,      lowercase = False  ):
							with open(Path(UpperCamelCase_  )  ,      encoding='utf-8'  ) as open_json_file:
											_lowerCamelCase     :			str    =    json.loads(open_json_file.read()  )
							_lowerCamelCase     :			Tuple    =    self.parse_dict(UpperCamelCase_  ,      allow_extra_keys=UpperCamelCase_  )
							return tuple(UpperCamelCase_  )






			def 					A_    (  self  ,      lowercase  ,      lowercase = False  ):
							_lowerCamelCase     :			Optional[int]    =    self.parse_dict(yaml.safe_load(Path(UpperCamelCase_  ).read_text()  )  ,      allow_extra_keys=UpperCamelCase_  )
							return tuple(UpperCamelCase_  )



"""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


snake_case__						:   List[Any]			 = '''bart'''
snake_case__						:   Union[str, Any]			 = True


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


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


snake_case__ ,   snake_case__ ,   snake_case__						:   Optional[Any]			 = load_indexes()
snake_case__ ,   snake_case__ ,   snake_case__ ,   snake_case__						:   str			 = load_models()
snake_case__ ,   snake_case__						:   Union[str, Any]			 = load_train_data()


def    _snake_case  (  _snake_case :	int       ,      _snake_case :	Dict=10	):
      lowerCAmelCase    :		Tuple				=      embed_questions_for_retrieval([question]       ,      _snake_case       ,      _snake_case	)
      lowerCAmelCase,					lowerCAmelCase    :		Any				=      eli5_train_q_index.search(_snake_case       ,      _snake_case	)
      lowerCAmelCase    :		str				=      [elia_train[int(_snake_case	)] for i in I[0]]
      return nn_examples


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


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


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

# Start sidebar
snake_case__						:   Dict			 = '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
snake_case__						:   Tuple			 = '''
<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
snake_case__						:   List[Any]			 = '''
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)

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

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

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

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

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


snake_case__						:   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)


