





import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
    embed_questions_for_retrieval,
    make_qa_sas_model,
    qa_sas_generate,
    query_es_index,
    query_qa_dense_index,
)

import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer


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







@st.cache(allow_output_mutation=__a    )
def    __lowerCamelCase					(							)		->				Optional[int]:






		"""simple docstring"""

		if LOAD_DENSE_INDEX:
				A__        =   AutoTokenizer.from_pretrained("""yjernite/retribert-base-uncased"""    )
				A__        =   AutoModel.from_pretrained("""yjernite/retribert-base-uncased"""    ).to("""cuda:0"""    )
				A__        =   qar_model.eval()
		else:
				A__      ,			A__        =   (None, None)
		if MODEL_TYPE == "bart":
				A__        =   AutoTokenizer.from_pretrained("""yjernite/bart_eli5"""    )
				A__        =   AutoModelForSeqaSeqLM.from_pretrained("""yjernite/bart_eli5"""    ).to("""cuda:0"""    )
				A__        =   torch.load("""seq2seq_models/eli5_bart_model_blm_2.pth"""    )
				sas_model.load_state_dict(save_dict["""model"""]    )
				A__        =   sas_model.eval()
		else:
				A__      ,			A__        =   make_qa_sas_model(
				    model_name="""t5-small"""    ,     from_file="""seq2seq_models/eli5_t5_model_1024_4.pth"""    ,     device="""cuda:0"""    )
		return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)







@st.cache(allow_output_mutation=__a    )
def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		if LOAD_DENSE_INDEX:
				A__        =   faiss.StandardGpuResources()
				A__        =   datasets.load_dataset(path="""wiki_snippets"""    ,     name="""wiki40b_en_100_0"""    )["""train"""]
				A__        =   np.memmap(
				    """wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat"""    ,     dtype="""float32"""    ,     mode="""r"""    ,     shape=(wikiaab_passages.num_rows, 1_2_8)    ,     )
				A__        =   faiss.IndexFlatIP(1_2_8    )
				A__        =   faiss.index_cpu_to_gpu(__a    ,     1    ,     __a    )
				wikiaab_gpu_index_flat.add(__a    )  # TODO fix for larger GPU
		else:
				A__      ,			A__        =   (None, None)
		A__        =   Elasticsearch([{"""host""": """localhost""", """port""": """9200"""}]    )
		return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)







@st.cache(allow_output_mutation=__a    )
def    __lowerCamelCase					(							)		->				Any:






		"""simple docstring"""

		A__        =   datasets.load_dataset("""eli5"""    ,     name="""LFQA_reddit"""    )
		A__        =   elia["""train_eli5"""]
		A__        =   np.memmap(
		    """eli5_questions_reps.dat"""    ,     dtype="""float32"""    ,     mode="""r"""    ,     shape=(elia_train.num_rows, 1_2_8)    )
		A__        =   faiss.IndexFlatIP(1_2_8    )
		eli5_train_q_index.add(__a    )
		return (elia_train, eli5_train_q_index)


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







def    __lowerCamelCase					(							__a   :int    ,     __a   :List[str]=1_0    )		->				Any:






		"""simple docstring"""

		A__        =   embed_questions_for_retrieval([question]    ,     __a    ,     __a    )
		A__      ,			A__        =   eli5_train_q_index.search(__a    ,     __a    )
		A__        =   [elia_train[int(__a    )] for i in I[0]]
		return nn_examples







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :Any="wiki40b"    ,     __a   :int="dense"    ,     __a   :Any=1_0    )		->				Optional[int]:






		"""simple docstring"""

		if source == "none":
				A__      ,			A__        =   (""" <P> """.join(["""""" for _ in range(1_1    )]    ).strip(), [])
		else:
				if method == "dense":
						A__      ,			A__        =   query_qa_dense_index(
						    __a    ,     __a    ,     __a    ,     __a    ,     __a    ,     __a    )
				else:
						A__      ,			A__        =   query_es_index(
						    __a    ,     __a    ,     index_name="""english_wiki40b_snippets_100w"""    ,     n_results=__a    ,     )
		A__        =   [
		    (res["""article_title"""], res["""section_title"""].strip(), res["""score"""], res["""passage_text"""]) for res in hit_lst
		]
		A__        =   """question: {} context: {}""".format(__a    ,     __a    )
		return question_doc, support_list







@st.cache(
    hash_funcs={
        torch.Tensor: (lambda __a    : None),
        transformers.models.bart.tokenization_bart.BartTokenizer: (lambda __a    : None),
    }    )
def    __lowerCamelCase					(							__a   :List[str]    ,     __a   :List[Any]    ,     __a   :Optional[Any]    ,     __a   :Union[str, Any]=6_4    ,     __a   :str=2_5_6    ,     __a   :Union[str, Any]=False    ,     __a   :Union[str, Any]=2    ,     __a   :int=0.95    ,     __a   :Any=0.8    )		->				List[str]:






		"""simple docstring"""

		with torch.no_grad():
				A__        =   qa_sas_generate(
				    __a    ,     __a    ,     __a    ,     num_answers=1    ,     num_beams=__a    ,     min_len=__a    ,     max_len=__a    ,     do_sample=__a    ,     temp=__a    ,     top_p=__a    ,     top_k=__a    ,     max_input_length=1_0_2_4    ,     device="""cuda:0"""    ,     )[0]
		return (answer, support_list)


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

# Start sidebar
A    :					int =      '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
A    :					Optional[int] =      '''
<html>
  <head>
    <style>
      .img-container {
        padding-left: 90px;
        padding-right: 90px;
        padding-top: 50px;
        padding-bottom: 50px;
        background-color: #f0f3f9;
      }
    </style>
  </head>
  <body>
    <span class="img-container"> <!-- Inline parent element -->
      %s
    </span>
  </body>
</html>
''' % (
    header_html,
)
st.sidebar.markdown(
    header_full,
    unsafe_allow_html=True,
)

# Long Form QA with ELI5 and Wikipedia
A    :					List[str] =      '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

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

A    :					Union[str, Any] =      st.sidebar.checkbox('''Retrieval options''')
if retrieval_options:
					A    :					Tuple =      '''
    ### Information retriever options

    The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding
    trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.
    The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.
    '''
					st.sidebar.markdown(retriever_info)
					A    :					str =      st.sidebar.selectbox('''Which Wikipedia format should the model use?''', ['''wiki40b''', '''none'''])
					A    :					int =      st.sidebar.selectbox('''Which Wikipedia indexer should the model use?''', ['''dense''', '''sparse''', '''mixed'''])
else:
					A    :					Optional[Any] =      '''wiki40b'''
					A    :					Tuple =      '''dense'''

A    :					List[str] =      '''beam'''
A    :					Optional[Any] =      2
A    :					Tuple =      6_4
A    :					List[str] =      2_5_6
A    :					str =      None
A    :					int =      None
A    :					List[Any] =      st.sidebar.checkbox('''Generation options''')
if generate_options:
					A    :					Any =      '''
    ### Answer generation options

    The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)
    weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with
    **beam** search, or **sample** from the decoder\'s output probabilities.
    '''
					st.sidebar.markdown(generate_info)
					A    :					Any =      st.sidebar.selectbox('''Would you like to use beam search or sample an answer?''', ['''beam''', '''sampled'''])
					A    :					Any =      st.sidebar.slider(
					    '''Minimum generation length''', min_value=8, max_value=2_5_6, value=6_4, step=8, format=None, key=None
					)
					A    :					Any =      st.sidebar.slider(
					    '''Maximum generation length''', min_value=6_4, max_value=5_1_2, value=2_5_6, step=1_6, format=None, key=None
					)
					if sampled == "beam":
										A    :					List[Any] =      st.sidebar.slider('''Beam size''', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
					else:
										A    :					int =      st.sidebar.slider(
										    '''Nucleus sampling p''', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
										)
										A    :					str =      st.sidebar.slider(
										    '''Temperature''', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
										)
										A    :					Tuple =      None

# start main text
A    :					Optional[Any] =      [
    '''<MY QUESTION>''',
    '''How do people make chocolate?''',
    '''Why do we get a fever when we are sick?''',
    '''How can different animals perceive different colors?''',
    '''What is natural language processing?''',
    '''What\'s the best way to treat a sunburn?''',
    '''What exactly are vitamins ?''',
    '''How does nuclear energy provide electricity?''',
    '''What\'s the difference between viruses and bacteria?''',
    '''Why are flutes classified as woodwinds when most of them are made out of metal ?''',
    '''Why do people like drinking coffee even though it tastes so bad?''',
    '''What happens when wine ages? How does it make the wine taste better?''',
    '''If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?''',
    '''How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?''',
    '''How does New Zealand have so many large bird predators?''',
]
A    :					Tuple =      st.selectbox(
    '''What would you like to ask? ---- select <MY QUESTION> to enter a new query''',
    questions_list,
    index=1,
)
if question_s == "<MY QUESTION>":
					A    :					Any =      st.text_input('''Enter your question here:''', '''''')
else:
					A    :					List[Any] =      question_s

if st.button('''Show me!'''):
					if action in [0, 1, 3]:
										if index_type == "mixed":
															A,	A    :					Dict =      make_support(question, source=wiki_source, method='''dense''', n_results=1_0)
															A,	A    :					int =      make_support(question, source=wiki_source, method='''sparse''', n_results=1_0)
															A    :					Optional[Any] =      []
															for res_d, res_s in zip(support_list_dense, support_list_sparse):
																				if tuple(res_d) not in support_list:
																									support_list += [tuple(res_d)]
																				if tuple(res_s) not in support_list:
																									support_list += [tuple(res_s)]
															A    :					Optional[Any] =      support_list[:1_0]
															A    :					List[Any] =      '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
										else:
															A,	A    :					Tuple =      make_support(question, source=wiki_source, method=index_type, n_results=1_0)
					if action in [0, 3]:
										A,	A    :					int =      answer_question(
										    question_doc,
										    sas_model,
										    sas_tokenizer,
										    min_len=min_len,
										    max_len=int(max_len),
										    sampling=(sampled == '''sampled'''),
										    n_beams=n_beams,
										    top_p=top_p,
										    temp=temp,
										)
										st.markdown('''### The model generated answer is:''')
										st.write(answer)
					if action in [0, 1, 3] and wiki_source != "none":
										st.markdown('''--- \n ### The model is drawing information from the following Wikipedia passages:''')
										for i, res in enumerate(support_list):
															A    :					Any =      '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
															A    :					Any =      res[1].strip()
															if sec_titles == "":
																				A    :					str =      '''[{}]({})'''.format(res[0], wiki_url)
															else:
																				A    :					Optional[Any] =      sec_titles.split(''' & ''')
																				A    :					str =      ''' & '''.join(
																				    ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
																				)
															st.markdown(
															    '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
															    unsafe_allow_html=True,
															)
															if show_passages:
																				st.write(
																				    '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
																				)
					if action in [2, 3]:
										A    :					str =      find_nearest_training(question)
										A    :					Union[str, Any] =      nn_train_list[0]
										st.markdown(
										    '''--- \n ### The most similar question in the ELI5 training set was: \n\n {}'''.format(train_exple['''title'''])
										)
										A    :					Any =      [
										    '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
										    for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
										    if i == 0 or sc > 2
										]
										st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


A    :					Any =      '''
---

**Disclaimer**

*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.
Evaluating biases of such a model and ensuring factual generations are still very much open research problems.
Therefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*
'''






st.sidebar.markdown(disclaimer, unsafe_allow_html=True)










from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


A    :					str =      logging.get_logger(__name__)

A    :					Union[str, Any] =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Optional[Any]							=    '''layoutlmv3'''






			def __init__(			self   :    Tuple							,	__lowerCAmelCase   :    Optional[int]=5_02_65							,	__lowerCAmelCase   :    Tuple=7_68							,	__lowerCAmelCase   :    Union[str, Any]=12							,	__lowerCAmelCase   :    Any=12							,	__lowerCAmelCase   :    List[Any]=30_72							,	__lowerCAmelCase   :    Optional[int]="gelu"							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[Any]=0.1							,	__lowerCAmelCase   :    Dict=5_12							,	__lowerCAmelCase   :    Any=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-5							,	__lowerCAmelCase   :    List[str]=1							,	__lowerCAmelCase   :    int=0							,	__lowerCAmelCase   :    Dict=2							,	__lowerCAmelCase   :    Tuple=10_24							,	__lowerCAmelCase   :    List[str]=1_28							,	__lowerCAmelCase   :    Optional[int]=1_28							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Optional[Any]=32							,	__lowerCAmelCase   :    Any=1_28							,	__lowerCAmelCase   :    str=64							,	__lowerCAmelCase   :    Optional[int]=2_56							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    int=True							,	__lowerCAmelCase   :    List[str]=True							,	__lowerCAmelCase   :    int=2_24							,	__lowerCAmelCase   :    Dict=3							,	__lowerCAmelCase   :    List[Any]=16							,	__lowerCAmelCase   :    Dict=None							,	**__lowerCAmelCase   :    Optional[Any]							,	)	->	Dict:







					"""simple docstring"""






					super().__init__(
					    vocab_size=__lowerCAmelCase							,	hidden_size=__lowerCAmelCase							,	num_hidden_layers=__lowerCAmelCase							,	num_attention_heads=__lowerCAmelCase							,	intermediate_size=__lowerCAmelCase							,	hidden_act=__lowerCAmelCase							,	hidden_dropout_prob=__lowerCAmelCase							,	attention_probs_dropout_prob=__lowerCAmelCase							,	max_position_embeddings=__lowerCAmelCase							,	type_vocab_size=__lowerCAmelCase							,	initializer_range=__lowerCAmelCase							,	layer_norm_eps=__lowerCAmelCase							,	pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase							,	)
					A__        =   max_ad_position_embeddings
					A__        =   coordinate_size
					A__        =   shape_size
					A__        =   has_relative_attention_bias
					A__        =   rel_pos_bins
					A__        =   max_rel_pos
					A__        =   has_spatial_attention_bias
					A__        =   rel_ad_pos_bins
					A__        =   max_rel_ad_pos
					A__        =   text_embed
					A__        =   visual_embed
					A__        =   input_size
					A__        =   num_channels
					A__        =   patch_size
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       List[str]							=    version.parse('''1.12'''    )






			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task in ["question-answering", "sequence-classification"]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]   )
					else:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
							    ]   )






			@property
			def    a_       (			self   :    Optional[int]   )	->	float:







					"""simple docstring"""






					return 1e-5






			@property
			def    a_       (			self   :    Tuple   )	->	int:







					"""simple docstring"""






					return 12







			def    a_       (			self   :    str							,	__lowerCAmelCase   :    "ProcessorMixin"							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    int = -1							,	__lowerCAmelCase   :    bool = False							,	__lowerCAmelCase   :    Optional["TensorType"] = None							,	__lowerCAmelCase   :    int = 3							,	__lowerCAmelCase   :    int = 40							,	__lowerCAmelCase   :    int = 40							,	)	->	Mapping[str, Any]:







					"""simple docstring"""






					setattr(processor.image_processor							,	"""apply_ocr"""							,	__lowerCAmelCase   )

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	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
					A__        =   processor.tokenizer.num_special_tokens_to_add(__lowerCAmelCase   )
					A__        =   compute_effective_axis_dimension(
					    __lowerCAmelCase							,	fixed_dimension=OnnxConfig.default_fixed_sequence							,	num_token_to_add=__lowerCAmelCase   )
					# Generate dummy inputs according to compute batch and sequence
					A__        =   [[""" """.join([processor.tokenizer.unk_token]   ) * seq_length]] * batch_size

					# Generate dummy bounding boxes
					A__        =   [[[48, 84, 73, 1_28]]] * batch_size

					# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
					# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
					A__        =   self._generate_dummy_images(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

					A__        =   dict(
					    processor(
					        __lowerCAmelCase							,	text=__lowerCAmelCase							,	boxes=__lowerCAmelCase							,	return_tensors=__lowerCAmelCase							,	)   )

					return inputs



