
"""simple docstring"""





import inspect
import tempfile
import unittest

from huggingface_hub import hf_hub_download

from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device

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


_a								=						1E-4

if is_torch_available():
				import torch

				from transformers import AutoformerConfig, AutoformerForPrediction, AutoformerModel
				from transformers.models.autoformer.modeling_autoformer import AutoformerDecoder, AutoformerEncoder






@require_torch
class        _UpperCAmelCase:



		def __init__(   self							,						__a							,						__a=16							,						__a=13							,						__a=7							,						__a=14							,						__a=10							,						__a=19							,						__a=5							,						__a=4							,						__a=True							,						__a=16							,						__a=2							,						__a=4							,						__a=4							,						__a="gelu"							,						__a=0.1							,						__a=0.1							,						__a=[1, 2, 3, 4, 5]							,						__a=25							,						__a=5							,						)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    d_model
							_UpperCamelCase									=    parent
							_UpperCamelCase									=    batch_size
							_UpperCamelCase									=    prediction_length
							_UpperCamelCase									=    context_length
							_UpperCamelCase									=    cardinality
							_UpperCamelCase									=    num_time_features
							_UpperCamelCase									=    lags_sequence
							_UpperCamelCase									=    embedding_dimension
							_UpperCamelCase									=    is_training
							_UpperCamelCase									=    hidden_size
							_UpperCamelCase									=    num_hidden_layers
							_UpperCamelCase									=    num_attention_heads
							_UpperCamelCase									=    intermediate_size
							_UpperCamelCase									=    hidden_act
							_UpperCamelCase									=    hidden_dropout_prob
							_UpperCamelCase									=    attention_probs_dropout_prob

							_UpperCamelCase									=    context_length
							_UpperCamelCase									=    prediction_length + label_length
							_UpperCamelCase									=    label_length

							_UpperCamelCase									=    moving_average
							_UpperCamelCase									=    autocorrelation_factor



		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							return AutoformerConfig(
							    d_model=self.d_model							,						encoder_layers=self.num_hidden_layers							,						decoder_layers=self.num_hidden_layers							,						encoder_attention_heads=self.num_attention_heads							,						decoder_attention_heads=self.num_attention_heads							,						encoder_ffn_dim=self.intermediate_size							,						decoder_ffn_dim=self.intermediate_size							,						dropout=self.hidden_dropout_prob							,						attention_dropout=self.attention_probs_dropout_prob							,						prediction_length=self.prediction_length							,						context_length=self.context_length							,						label_length=self.label_length							,						lags_sequence=self.lags_sequence							,						num_time_features=self.num_time_features							,						num_static_categorical_features=1							,						cardinality=[self.cardinality]							,						embedding_dimension=[self.embedding_dimension]							,						moving_average=self.moving_average							,						)



		def 		UpperCAmelCase				(   self							,						__a)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    config.context_length + max(config.lags_sequence)

							_UpperCamelCase									=    ids_tensor([self.batch_size, 1]							,						config.cardinality[0])
							_UpperCamelCase									=    floats_tensor([self.batch_size, _past_length, config.num_time_features])
							_UpperCamelCase									=    floats_tensor([self.batch_size, _past_length])
							_UpperCamelCase									=    floats_tensor([self.batch_size, _past_length]) > 0.5

							# decoder inputs
							_UpperCamelCase									=    floats_tensor([self.batch_size, config.prediction_length, config.num_time_features])
							_UpperCamelCase									=    floats_tensor([self.batch_size, config.prediction_length])

							_UpperCamelCase									=    {
							    '''past_values''': past_values,
							    '''static_categorical_features''': static_categorical_features,
							    '''past_time_features''': past_time_features,
							    '''past_observed_mask''': past_observed_mask,
							    '''future_time_features''': future_time_features,
							    '''future_values''': future_values,
							}
							return inputs_dict



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.get_config()
							_UpperCamelCase									=    self.prepare_autoformer_inputs_dict(__a)
							return config, inputs_dict



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.prepare_config_and_inputs()
							return config, inputs_dict




		def 		UpperCAmelCase				(   self							,						__a							,						__a)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerModel(config=__a).to(__a).eval()
							_UpperCamelCase									=    model(**__a)

							_UpperCamelCase									=    outputs.encoder_last_hidden_state
							_UpperCamelCase									=    outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase									=    model.get_encoder()
												encoder.save_pretrained(__a)
												_UpperCamelCase									=    AutoformerEncoder.from_pretrained(__a).to(__a)

							_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    model.create_network_inputs(**__a)
							_UpperCamelCase					,    _UpperCamelCase									=    model.decomposition_layer(transformer_inputs[:, : config.context_length, ...])

							_UpperCamelCase									=    torch.cat(
							    (transformer_inputs[:, : config.context_length, ...], feature[:, : config.context_length, ...])							,						dim=-1							,						)
							_UpperCamelCase									=    encoder(inputs_embeds=__a)[0]
							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3)

							_UpperCamelCase									=    (
							    torch.mean(transformer_inputs[:, : config.context_length, ...]							,						dim=1)
							    .unsqueeze(1)
							    .repeat(1							,						config.prediction_length							,						1)
							)
							_UpperCamelCase									=    torch.zeros(
							    [transformer_inputs.shape[0], config.prediction_length, transformer_inputs.shape[2]]							,						device=enc_input.device							,						)

							_UpperCamelCase									=    torch.cat(
							    (
							        torch.cat((seasonal_input[:, -config.label_length :, ...], zeros)							,						dim=1),
							        feature[:, config.context_length - config.label_length :, ...],
							    )							,						dim=-1							,						)
							_UpperCamelCase									=    torch.cat(
							    (
							        torch.cat((trend_input[:, -config.label_length :, ...], mean)							,						dim=1),
							        feature[:, config.context_length - config.label_length :, ...],
							    )							,						dim=-1							,						)

							with tempfile.TemporaryDirectory() as tmpdirname:
												_UpperCamelCase									=    model.get_decoder()
												decoder.save_pretrained(__a)
												_UpperCamelCase									=    AutoformerDecoder.from_pretrained(__a).to(__a)

							_UpperCamelCase									=    decoder(
							    trend=__a							,						inputs_embeds=__a							,						encoder_hidden_states=__a							,						)[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3)






@require_torch
class        _UpperCAmelCase(       lowerCamelCase ,	lowerCamelCase ,	unittest.TestCase      ):
		lowercase__         = (AutoformerModel, AutoformerForPrediction) if is_torch_available() else ()
		lowercase__         = (AutoformerForPrediction,) if is_torch_available() else ()
		lowercase__         = {'feature-extraction': AutoformerModel} if is_torch_available() else {}
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False
		lowercase__         = False



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerModelTester(self)
							_UpperCamelCase									=    ConfigTester(self							,						config_class=__a							,						has_text_modality=__a)



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							self.config_tester.run_common_tests()



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												_UpperCamelCase									=    model_class(__a)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(__a)
																	_UpperCamelCase					,    _UpperCamelCase									=    model_class.from_pretrained(__a							,						output_loading_info=__a)
												self.assertEqual(info['''missing_keys''']							,						[])



		def 		UpperCAmelCase				(   self)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*__a)



		@unittest.skip(reason='''Model has no tokens embeddings''')
		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							pass



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							_UpperCamelCase									=    inspect.signature(getattr(__a							,						'''forward'''))
							# The main input is the name of the argument after `self`
							_UpperCamelCase									=    list(model_signature.parameters.keys())[1]
							self.assertEqual(AutoformerModel.main_input_name							,						__a)



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()

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

												_UpperCamelCase									=    [
												    '''past_values''',
												    '''past_time_features''',
												    '''past_observed_mask''',
												    '''static_categorical_features''',
												    '''static_real_features''',
												    '''future_values''',
												    '''future_time_features''',
												]

												if model.__class__.__name__ in ["AutoformerForPrediction"]:
																	expected_arg_names.append('''future_observed_mask''')

												expected_arg_names.extend(
												    [
												        '''decoder_attention_mask''',
												        '''head_mask''',
												        '''decoder_head_mask''',
												        '''cross_attn_head_mask''',
												        '''encoder_outputs''',
												        '''past_key_values''',
												        '''output_hidden_states''',
												        '''output_attentions''',
												        '''use_cache''',
												        '''return_dict''',
												    ])

												self.assertListEqual(arg_names[: len(__a)]							,						__a)



		def 		UpperCAmelCase				(   self)			->			List[Any]:






							'''simple docstring'''
							_UpperCamelCase					,    _UpperCamelCase									=    self.model_tester.prepare_config_and_inputs_for_common()
							_UpperCamelCase									=    True

							_UpperCamelCase									=    getattr(self.model_tester							,						'''seq_length'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''decoder_seq_length'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''encoder_seq_length'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''d_model'''							,						__a)
							_UpperCamelCase									=    getattr(self.model_tester							,						'''num_attention_heads'''							,						__a)
							_UpperCamelCase									=    d_model // num_attention_heads

							for model_class in self.all_model_classes:
												_UpperCamelCase									=    True
												_UpperCamelCase									=    False
												_UpperCamelCase									=    True
												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))
												_UpperCamelCase									=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
												self.assertEqual(len(__a)							,						self.model_tester.num_hidden_layers)

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												_UpperCamelCase									=    True
												_UpperCamelCase									=    model_class(__a)
												model.to(__a)
												model.eval()
												with torch.no_grad():
																	_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))
												_UpperCamelCase									=    outputs.encoder_attentions
												self.assertEqual(len(__a)							,						self.model_tester.num_hidden_layers)

												self.assertListEqual(
												    list(attentions[0].shape[-3:])							,						[self.model_tester.num_attention_heads, encoder_seq_length, dim]							,						)
												_UpperCamelCase									=    len(__a)

												_UpperCamelCase									=    7

												if "last_hidden_state" in outputs:
																	correct_outlen += 1

												if "trend" in outputs:
																	correct_outlen += 1

												if "past_key_values" in outputs:
																	correct_outlen += 1  # past_key_values have been returned

												if "loss" in outputs:
																	correct_outlen += 1

												if "params" in outputs:
																	correct_outlen += 1

												self.assertEqual(__a							,						__a)

												# decoder attentions
												_UpperCamelCase									=    outputs.decoder_attentions
												self.assertIsInstance(__a							,						(list, tuple))
												self.assertEqual(len(__a)							,						self.model_tester.num_hidden_layers)
												self.assertListEqual(
												    list(decoder_attentions[0].shape[-3:])							,						[self.model_tester.num_attention_heads, decoder_seq_length, dim]							,						)

												# cross attentions
												_UpperCamelCase									=    outputs.cross_attentions
												self.assertIsInstance(__a							,						(list, tuple))
												self.assertEqual(len(__a)							,						self.model_tester.num_hidden_layers)
												self.assertListEqual(
												    list(cross_attentions[0].shape[-3:])							,						[self.model_tester.num_attention_heads, decoder_seq_length, dim]							,						)

							# Check attention is always last and order is fine
							_UpperCamelCase									=    True
							_UpperCamelCase									=    True
							_UpperCamelCase									=    model_class(__a)
							model.to(__a)
							model.eval()
							with torch.no_grad():
												_UpperCamelCase									=    model(**self._prepare_for_class(__a							,						__a))

							self.assertEqual(out_len + 2							,						len(__a))

							_UpperCamelCase									=    outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions

							self.assertEqual(len(__a)							,						self.model_tester.num_hidden_layers)
							self.assertListEqual(
							    list(self_attentions[0].shape[-3:])							,						[self.model_tester.num_attention_heads, encoder_seq_length, dim]							,						)




		@is_flaky()
		def 		UpperCAmelCase				(   self)			->			Optional[Any]:






							'''simple docstring'''
							super().test_retain_grad_hidden_states_attentions()





def  lowerCamelCase__						(     __snake_case="train-batch.pt"      )				->						Tuple:


					"""simple docstring"""





					_UpperCamelCase									=    hf_hub_download(repo_id='''hf-internal-testing/tourism-monthly-batch''',	filename=__snake_case,	repo_type='''dataset'''      )
					_UpperCamelCase									=    torch.load(__snake_case,	map_location=__snake_case      )
					return batch






@require_torch
@slow
class        _UpperCAmelCase(       unittest.TestCase      ):



		def 		UpperCAmelCase				(   self)			->			Tuple:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerModel.from_pretrained('''huggingface/autoformer-tourism-monthly''').to(__a)
							_UpperCamelCase									=    prepare_batch()

							with torch.no_grad():
												_UpperCamelCase									=    model(
												    past_values=batch['''past_values''']							,						past_time_features=batch['''past_time_features''']							,						past_observed_mask=batch['''past_observed_mask''']							,						static_categorical_features=batch['''static_categorical_features''']							,						future_values=batch['''future_values''']							,						future_time_features=batch['''future_time_features''']							,						)[0]

							_UpperCamelCase									=    torch.Size(
							    (64, model.config.prediction_length + model.config.label_length, model.config.feature_size))
							self.assertEqual(output.shape							,						__a)

							_UpperCamelCase									=    torch.tensor(
							    [[0.3593, -1.3398, 0.6330], [0.2279, 1.5396, -0.1792], [0.0450, 1.3225, -0.2335]]							,						device=__a)
							self.assertTrue(torch.allclose(output[0, :3, :3]							,						__a							,						atol=__a))



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''').to(__a)
							_UpperCamelCase									=    prepare_batch('''val-batch.pt''')
							with torch.no_grad():
												_UpperCamelCase									=    model(
												    past_values=batch['''past_values''']							,						past_time_features=batch['''past_time_features''']							,						past_observed_mask=batch['''past_observed_mask''']							,						static_categorical_features=batch['''static_categorical_features''']							,						).encoder_last_hidden_state
							_UpperCamelCase									=    torch.Size((64, model.config.context_length, model.config.d_model))
							self.assertEqual(output.shape							,						__a)

							_UpperCamelCase									=    torch.tensor(
							    [[-0.0734, -0.9036, 0.8358], [4.7186, 2.4113, 1.9581], [1.7953, 2.3558, 1.2970]]							,						device=__a)
							self.assertTrue(torch.allclose(output[0, :3, :3]							,						__a							,						atol=__a))




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    AutoformerForPrediction.from_pretrained('''huggingface/autoformer-tourism-monthly''').to(__a)
							_UpperCamelCase									=    prepare_batch('''val-batch.pt''')
							with torch.no_grad():
												_UpperCamelCase									=    model.generate(
												    static_categorical_features=batch['''static_categorical_features''']							,						past_time_features=batch['''past_time_features''']							,						past_values=batch['''past_values''']							,						future_time_features=batch['''future_time_features''']							,						past_observed_mask=batch['''past_observed_mask''']							,						)
							_UpperCamelCase									=    torch.Size((64, model.config.num_parallel_samples, model.config.prediction_length))
							self.assertEqual(outputs.sequences.shape							,						__a)

							_UpperCamelCase									=    torch.tensor([3130.6763, 4056.5293, 7053.0786]							,						device=__a)
							_UpperCamelCase									=    outputs.sequences.mean(dim=1)
							self.assertTrue(torch.allclose(mean_prediction[0, -3:]							,						__a							,						rtol=1e-1))








"""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


_a								=						"""bart"""
_a								=						True



@st.cache(allow_output_mutation=__snake_case      )
def  lowerCamelCase__						(     )				->						Dict:


					"""simple docstring"""





					if LOAD_DENSE_INDEX:
										_UpperCamelCase									=    AutoTokenizer.from_pretrained('''yjernite/retribert-base-uncased'''      )
										_UpperCamelCase									=    AutoModel.from_pretrained('''yjernite/retribert-base-uncased'''      ).to('''cuda:0'''      )
										_UpperCamelCase									=    qar_model.eval()
					else:
										_UpperCamelCase					,    _UpperCamelCase									=    (None, None)
					if MODEL_TYPE == "bart":
										_UpperCamelCase									=    AutoTokenizer.from_pretrained('''yjernite/bart_eli5'''      )
										_UpperCamelCase									=    AutoModelForSeqaSeqLM.from_pretrained('''yjernite/bart_eli5'''      ).to('''cuda:0'''      )
										_UpperCamelCase									=    torch.load('''seq2seq_models/eli5_bart_model_blm_2.pth'''      )
										sas_model.load_state_dict(save_dict['''model''']      )
										_UpperCamelCase									=    sas_model.eval()
					else:
										_UpperCamelCase					,    _UpperCamelCase									=    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  lowerCamelCase__						(     )				->						Tuple:


					"""simple docstring"""





					if LOAD_DENSE_INDEX:
										_UpperCamelCase									=    faiss.StandardGpuResources()
										_UpperCamelCase									=    datasets.load_dataset(path='''wiki_snippets''',	name='''wiki40b_en_100_0'''      )['''train''']
										_UpperCamelCase									=    np.memmap(
										    '''wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat''',	dtype='''float32''',	mode='''r''',	shape=(wikiaab_passages.num_rows, 1_28),	)
										_UpperCamelCase									=    faiss.IndexFlatIP(1_28      )
										_UpperCamelCase									=    faiss.index_cpu_to_gpu(__snake_case,	1,	__snake_case      )
										wikiaab_gpu_index_flat.add(__snake_case      )  # TODO fix for larger GPU
					else:
										_UpperCamelCase					,    _UpperCamelCase									=    (None, None)
					_UpperCamelCase									=    Elasticsearch([{'''host''': '''localhost''', '''port''': '''9200'''}]      )
					return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)



@st.cache(allow_output_mutation=__snake_case      )
def  lowerCamelCase__						(     )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    datasets.load_dataset('''eli5''',	name='''LFQA_reddit'''      )
					_UpperCamelCase									=    elia['''train_eli5''']
					_UpperCamelCase									=    np.memmap(
					    '''eli5_questions_reps.dat''',	dtype='''float32''',	mode='''r''',	shape=(elia_train.num_rows, 1_28)      )
					_UpperCamelCase									=    faiss.IndexFlatIP(1_28      )
					eli5_train_q_index.add(__snake_case      )
					return (elia_train, eli5_train_q_index)


_a				,				_a				,				_a								=						load_indexes()
_a				,				_a				,				_a				,				_a								=						load_models()
_a				,				_a								=						load_train_data()



def  lowerCamelCase__						(     __snake_case,	__snake_case=10      )				->						List[Any]:


					"""simple docstring"""





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



def  lowerCamelCase__						(     __snake_case,	__snake_case="wiki40b",	__snake_case="dense",	__snake_case=10      )				->						List[str]:


					"""simple docstring"""





					if source == "none":
										_UpperCamelCase					,    _UpperCamelCase									=    (''' <P> '''.join(['''''' for _ in range(11      )]      ).strip(), [])
					else:
										if method == "dense":
															_UpperCamelCase					,    _UpperCamelCase									=    query_qa_dense_index(
															    __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )
										else:
															_UpperCamelCase					,    _UpperCamelCase									=    query_es_index(
															    __snake_case,	__snake_case,	index_name='''english_wiki40b_snippets_100w''',	n_results=__snake_case,	)
					_UpperCamelCase									=    [
					    (res['''article_title'''], res['''section_title'''].strip(), res['''score'''], res['''passage_text''']) for res in hit_lst
					]
					_UpperCamelCase									=    '''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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case=64,	__snake_case=2_56,	__snake_case=False,	__snake_case=2,	__snake_case=0.95,	__snake_case=0.8      )				->						Dict:


					"""simple docstring"""





					with torch.no_grad():
										_UpperCamelCase									=    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=10_24,	device='''cuda:0''',	)[0]
					return (answer, support_list)


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

# Start sidebar
_a								=						"""<img src='https://huggingface.co/front/assets/huggingface_logo.svg'>"""
_a								=						"""
<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								=						"""
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								=						[
    """Answer the question""",
    """View the retrieved document only""",
    """View the most similar ELI5 question and answer""",
    """Show me everything, please!""",
]
_a								=						st.sidebar.checkbox("""Demo options""")
if demo_options:
				_a								=						st.sidebar.selectbox(
				    """""",
				    action_list,
				    index=3,
				)
				_a								=						action_list.index(action_st)
				_a								=						st.sidebar.selectbox(
				    """""",
				    ["""Show full text of passages""", """Show passage section titles"""],
				    index=0,
				)
				_a								=						show_type == """Show full text of passages"""
else:
				_a								=						3
				_a								=						True

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

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

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

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

**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)






