





"""simple docstring"""




import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}




class 			_a						(				A__    ):
				UpperCamelCase								=   'conditional_detr'
				UpperCamelCase								=   ['past_key_values']
				UpperCamelCase								=   {
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'encoder_attention_heads',
				}






				def __init__(			self		:   int,			lowerCAmelCase__		:   List[str]=True,			lowerCAmelCase__		:   Tuple=None,			lowerCAmelCase__		:   Union[str, Any]=3,			lowerCAmelCase__		:   int=3_0_0,			lowerCAmelCase__		:   Tuple=6,			lowerCAmelCase__		:   Tuple=2_0_4_8,			lowerCAmelCase__		:   Tuple=8,			lowerCAmelCase__		:   Optional[Any]=6,			lowerCAmelCase__		:   Tuple=2_0_4_8,			lowerCAmelCase__		:   Optional[Any]=8,			lowerCAmelCase__		:   Optional[Any]=0.0,			lowerCAmelCase__		:   str=0.0,			lowerCAmelCase__		:   Tuple=True,			lowerCAmelCase__		:   Any="relu",			lowerCAmelCase__		:   List[Any]=2_5_6,			lowerCAmelCase__		:   Tuple=0.1,			lowerCAmelCase__		:   Optional[int]=0.0,			lowerCAmelCase__		:   Dict=0.0,			lowerCAmelCase__		:   str=0.02,			lowerCAmelCase__		:   Optional[int]=1.0,			lowerCAmelCase__		:   Dict=False,			lowerCAmelCase__		:   Optional[int]="sine",			lowerCAmelCase__		:   Any="resnet50",			lowerCAmelCase__		:   List[str]=True,			lowerCAmelCase__		:   Optional[Any]=False,			lowerCAmelCase__		:   Optional[Any]=2,			lowerCAmelCase__		:   Optional[int]=5,			lowerCAmelCase__		:   Union[str, Any]=2,			lowerCAmelCase__		:   Union[str, Any]=1,			lowerCAmelCase__		:   Optional[Any]=1,			lowerCAmelCase__		:   List[Any]=2,			lowerCAmelCase__		:   List[Any]=5,			lowerCAmelCase__		:   int=2,			lowerCAmelCase__		:   Any=0.25,			**lowerCAmelCase__		:   Any,			)     ->				int:




										'''simple docstring'''



										if backbone_config is not None and use_timm_backbone:
																raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''							)

										if not use_timm_backbone:
																if backbone_config is None:
																						logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
																						_UpperCamelCase      : Dict         =   CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']							)
																elif isinstance(lowerCamelCase__,			lowerCamelCase__							):
																						_UpperCamelCase      : Optional[int]         =   backbone_config.get('''model_type'''							)
																						_UpperCamelCase      : str         =   CONFIG_MAPPING[backbone_model_type]
																						_UpperCamelCase      : Optional[int]         =   config_class.from_dict(lowerCamelCase__							)

										_UpperCamelCase      : Any         =   use_timm_backbone
										_UpperCamelCase      : List[Any]         =   backbone_config
										_UpperCamelCase      : List[str]         =   num_channels
										_UpperCamelCase      : Any         =   num_queries
										_UpperCamelCase      : Union[str, Any]         =   d_model
										_UpperCamelCase      : str         =   encoder_ffn_dim
										_UpperCamelCase      : Tuple         =   encoder_layers
										_UpperCamelCase      : List[str]         =   encoder_attention_heads
										_UpperCamelCase      : Optional[int]         =   decoder_ffn_dim
										_UpperCamelCase      : int         =   decoder_layers
										_UpperCamelCase      : Tuple         =   decoder_attention_heads
										_UpperCamelCase      : List[Any]         =   dropout
										_UpperCamelCase      : List[Any]         =   attention_dropout
										_UpperCamelCase      : int         =   activation_dropout
										_UpperCamelCase      : List[Any]         =   activation_function
										_UpperCamelCase      : Any         =   init_std
										_UpperCamelCase      : List[Any]         =   init_xavier_std
										_UpperCamelCase      : str         =   encoder_layerdrop
										_UpperCamelCase      : Union[str, Any]         =   decoder_layerdrop
										_UpperCamelCase      : Dict         =   encoder_layers
										_UpperCamelCase      : Tuple         =   auxiliary_loss
										_UpperCamelCase      : Optional[Any]         =   position_embedding_type
										_UpperCamelCase      : Tuple         =   backbone
										_UpperCamelCase      : Optional[int]         =   use_pretrained_backbone
										_UpperCamelCase      : str         =   dilation
										# Hungarian matcher
										_UpperCamelCase      : List[str]         =   class_cost
										_UpperCamelCase      : Any         =   bbox_cost
										_UpperCamelCase      : List[str]         =   giou_cost
										# Loss coefficients
										_UpperCamelCase      : int         =   mask_loss_coefficient
										_UpperCamelCase      : List[Any]         =   dice_loss_coefficient
										_UpperCamelCase      : List[Any]         =   cls_loss_coefficient
										_UpperCamelCase      : int         =   bbox_loss_coefficient
										_UpperCamelCase      : Optional[int]         =   giou_loss_coefficient
										_UpperCamelCase      : List[str]         =   focal_alpha
										super().__init__(is_encoder_decoder=lowerCamelCase__,			**lowerCamelCase__							)






				@property
				def     snake_case     (			self		:   Any							)     ->				List[str]:




										'''simple docstring'''



										return self.encoder_attention_heads






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				List[Any]:




										'''simple docstring'''



										return self.d_model






				def     snake_case     (			self		:   Tuple							)     ->				Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase      : str         =   copy.deepcopy(self.__dict__							)
										if self.backbone_config is not None:
																_UpperCamelCase      : Any         =   self.backbone_config.to_dict()
										_UpperCamelCase      : List[str]         =   self.__class__.model_type
										return output




class 			_a						(				A__    ):
				UpperCamelCase								=   version.parse('''1.11'''    )






				@property
				def     snake_case     (			self		:   Union[str, Any]							)     ->				List[Any]:




										'''simple docstring'''



										return OrderedDict(
										    [
										        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
										        ('''pixel_mask''', {0: '''batch'''}),
										    ]							)






				@property
				def     snake_case     (			self		:   List[Any]							)     ->				str:




										'''simple docstring'''



										return 1e-5






				@property
				def     snake_case     (			self		:   Optional[Any]							)     ->				Optional[int]:




										'''simple docstring'''



										return 1_2










"""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


UpperCamelCase_									="""bart"""
UpperCamelCase_									=True





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





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


UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_									=load_indexes()
UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_									=load_models()
UpperCamelCase_				,     UpperCamelCase_									=load_train_data()





def 	a_    (							_lowercase	,						_lowercase=10       ):
						_UpperCamelCase      : Any         =   embed_questions_for_retrieval([question]	,						_lowercase	,						_lowercase       )
						_UpperCamelCase       ,							_UpperCamelCase      : List[Any]         =   eli5_train_q_index.search(_lowercase	,						_lowercase       )
						_UpperCamelCase      : Tuple         =   [elia_train[int(_lowercase       )] for i in I[0]]
						return nn_examples





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





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


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

# Start sidebar
UpperCamelCase_									="""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
UpperCamelCase_									="""
<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
UpperCamelCase_									="""
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)

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

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

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

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

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


UpperCamelCase_									="""
---

**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)



