




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
		from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import MobileNetVaImageProcessor
class 		UpperCAmelCase   (				_a			):







						'''simple docstring'''





						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							snake_case_		    =     self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(__lowercase       ,	"tf_padding"						)						)
							self.parent.assertTrue(hasattr(__lowercase       ,	"depth_multiplier"						)						)







class 		UpperCAmelCase   :







						'''simple docstring'''





						def __init__(						self							:     Union[str, Any]       ,	__lowercase							:     Optional[Any]       ,	__lowercase							:     Optional[int]=13       ,	__lowercase							:     Optional[int]=3       ,	__lowercase							:     Tuple=32       ,	__lowercase							:     Optional[Any]=0.25       ,	__lowercase							:     Optional[int]=8       ,	__lowercase							:     List[str]=8       ,	__lowercase							:     str=6       ,	__lowercase							:     Optional[Any]=32       ,	__lowercase							:     Optional[Any]=True       ,	__lowercase							:     Union[str, Any]=True       ,	__lowercase							:     List[str]=True       ,	__lowercase							:     Optional[Any]="relu6"       ,	__lowercase							:     Union[str, Any]=12_80       ,	__lowercase							:     Dict=0.1       ,	__lowercase							:     Optional[Any]=0.02       ,	__lowercase							:     Union[str, Any]=True       ,	__lowercase							:     int=True       ,	__lowercase							:     Union[str, Any]=10       ,	__lowercase							:     Optional[int]=None       ,	):






							"""simple docstring"""






							snake_case_		    =     parent
							snake_case_		    =     batch_size
							snake_case_		    =     num_channels
							snake_case_		    =     image_size
							snake_case_		    =     depth_multiplier
							snake_case_		    =     depth_divisible_by
							snake_case_		    =     min_depth
							snake_case_		    =     expand_ratio
							snake_case_		    =     tf_padding
							snake_case_		    =     output_stride
							snake_case_		    =     first_layer_is_expansion
							snake_case_		    =     finegrained_output
							snake_case_		    =     hidden_act
							snake_case_		    =     last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier						)
							snake_case_		    =     classifier_dropout_prob
							snake_case_		    =     use_labels
							snake_case_		    =     is_training
							snake_case_		    =     num_labels
							snake_case_		    =     initializer_range
							snake_case_		    =     scope






						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							snake_case_		    =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							snake_case_		    =     None
							snake_case_		    =     None
							if self.use_labels:
								snake_case_		    =     ids_tensor([self.batch_size]       ,	self.num_labels						)
								snake_case_		    =     ids_tensor([self.batch_size, self.image_size, self.image_size]       ,	self.num_labels						)

							snake_case_		    =     self.get_config()

							return config, pixel_values, labels, pixel_labels






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							return MobileNetVaConfig(
							    num_channels=self.num_channels       ,	image_size=self.image_size       ,	depth_multiplier=self.depth_multiplier       ,	depth_divisible_by=self.depth_divisible_by       ,	min_depth=self.min_depth       ,	expand_ratio=self.expand_ratio       ,	output_stride=self.output_stride       ,	first_layer_is_expansion=self.first_layer_is_expansion       ,	finegrained_output=self.finegrained_output       ,	hidden_act=self.hidden_act       ,	tf_padding=self.tf_padding       ,	classifier_dropout_prob=self.classifier_dropout_prob       ,	initializer_range=self.initializer_range       ,	)






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     List[Any]       ,	__lowercase							:     List[Any]       ,	__lowercase							:     Any       ,	__lowercase							:     List[str]						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     model(__lowercase						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape       ,	(
							        self.batch_size,
							        self.last_hidden_size,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )       ,	)
							self.parent.assertEqual(
							    result.pooler_output.shape       ,	(self.batch_size, self.last_hidden_size)       ,	)






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     str       ,	__lowercase							:     Tuple       ,	__lowercase							:     Union[str, Any]       ,	__lowercase							:     str						):






							"""simple docstring"""






							snake_case_		    =     self.num_labels
							snake_case_		    =     MobileNetVaForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     model(__lowercase       ,	labels=__lowercase						)
							self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)






						def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     int       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.num_labels
							snake_case_		    =     MobileNetVaForSemanticSegmentation(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							snake_case_		    =     model(__lowercase						)
							self.parent.assertEqual(
							    result.logits.shape       ,	(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )       ,	)
							snake_case_		    =     model(__lowercase       ,	labels=__lowercase						)
							self.parent.assertEqual(
							    result.logits.shape       ,	(
							        self.batch_size,
							        self.num_labels,
							        self.image_size // self.output_stride,
							        self.image_size // self.output_stride,
							    )       ,	)






						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							snake_case_		    =     self.prepare_config_and_inputs()
							snake_case_		    =     config_and_inputs
							snake_case_		    =     {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class 		UpperCAmelCase   (				_a       ,   _a       ,   unittest.TestCase			):







						'''simple docstring'''





						lowerCAmelCase_     		=   (
						    (MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase_     		=   (
						    {
						        """feature-extraction""": MobileNetVaModel,
						        """image-classification""": MobileNetVaForImageClassification,
						        """image-segmentation""": MobileNetVaForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False
						lowerCAmelCase_     		=   False






						def        snake_case__						(						self							:     Optional[int]						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaModelTester(self						)
							snake_case_		    =     MobileNetVaConfigTester(self       ,	config_class=__lowercase       ,	has_text_modality=__lowercase						)






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							self.config_tester.run_common_tests()






						@unittest.skip(reason="MobileNetV2 does not use inputs_embeds"						)
						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							pass






						@unittest.skip(reason="MobileNetV2 does not support input and output embeddings"						)
						def        snake_case__						(						self							:     str						):






							"""simple docstring"""






							pass






						@unittest.skip(reason="MobileNetV2 does not output attentions"						)
						def        snake_case__						(						self							:     int						):






							"""simple docstring"""






							pass






						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		    =     model_class(__lowercase						)
								snake_case_		    =     inspect.signature(model.forward						)
								# signature.parameters is an OrderedDict => so arg_names order is deterministic
								snake_case_		    =     [*signature.parameters.keys()]

								snake_case_		    =     ['pixel_values']
								self.assertListEqual(arg_names[:1]       ,	__lowercase						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)






						def        snake_case__						(						self							:     Any						):






							"""simple docstring"""






							def check_hidden_states_output(__lowercase							:     Tuple       ,	__lowercase							:     int       ,	__lowercase							:     str						):
								snake_case_		    =     model_class(__lowercase						)
								model.to(__lowercase						)
								model.eval()

								with torch.no_grad():
									snake_case_		    =     model(**self._prepare_for_class(__lowercase       ,	__lowercase						)						)

								snake_case_		    =     outputs.hidden_states

								snake_case_		    =     16
								self.assertEqual(len(__lowercase						)       ,	__lowercase						)

							snake_case_		    =     self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								snake_case_		    =     True
								check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase						)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								snake_case_		    =     True

								check_hidden_states_output(__lowercase       ,	__lowercase       ,	__lowercase						)






						def        snake_case__						(						self							:     Tuple						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)






						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase						)






						@slow
						def        snake_case__						(						self							:     Union[str, Any]						):






							"""simple docstring"""






							for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								snake_case_		    =     MobileNetVaModel.from_pretrained(__lowercase						)
								self.assertIsNotNone(__lowercase						)







def 						lowerCamelCase__    (		):







	'''simple docstring'''





	snake_case_		    =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






@require_torch
@require_vision
class 		UpperCAmelCase   (				unittest.TestCase			):







						'''simple docstring'''





						@cached_property
						def        snake_case__						(						self							:     Dict						):






							"""simple docstring"""






							return (
							    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v2_1.0_224"						) if is_vision_available() else None
							)






						@slow
						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v2_1.0_224"						).to(__lowercase						)

							snake_case_		    =     self.default_image_processor
							snake_case_		    =     prepare_img()
							snake_case_		    =     image_processor(images=__lowercase       ,	return_tensors="pt"						).to(__lowercase						)

							# forward pass
							with torch.no_grad():
								snake_case_		    =     model(**__lowercase						)

							# verify the logits
							snake_case_		    =     torch.Size((1, 10_01)						)
							self.assertEqual(outputs.logits.shape       ,	__lowercase						)

							snake_case_		    =     torch.tensor([0.2445, -1.1993, 0.1905]						).to(__lowercase						)

							self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,	__lowercase       ,	atol=1E-4						)						)






						@slow
						def        snake_case__						(						self							:     List[Any]						):






							"""simple docstring"""






							snake_case_		    =     MobileNetVaForSemanticSegmentation.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"						)
							snake_case_		    =     model.to(__lowercase						)

							snake_case_		    =     MobileNetVaImageProcessor.from_pretrained("google/deeplabv3_mobilenet_v2_1.0_513"						)

							snake_case_		    =     prepare_img()
							snake_case_		    =     image_processor(images=__lowercase       ,	return_tensors="pt"						).to(__lowercase						)

							# forward pass
							with torch.no_grad():
								snake_case_		    =     model(**__lowercase						)
							snake_case_		    =     outputs.logits

							# verify the logits
							snake_case_		    =     torch.Size((1, 21, 65, 65)						)
							self.assertEqual(logits.shape       ,	__lowercase						)

							snake_case_		    =     torch.tensor(
							    [
							        [[17.57_90, 17.75_81, 18.33_55], [18.32_57, 18.42_30, 18.89_73], [18.61_69, 18.86_50, 19.21_87]],
							        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
							        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
							    ]       ,	device=__lowercase       ,	)

							self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,	__lowercase       ,	atol=1E-4						)						)




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)


