from __future__ import annotations

import unittest

from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import (
	    TF_MODEL_FOR_PRETRAINING_MAPPING,
	    TFMobileBertForMaskedLM,
	    TFMobileBertForMultipleChoice,
	    TFMobileBertForNextSentencePrediction,
	    TFMobileBertForPreTraining,
	    TFMobileBertForQuestionAnswering,
	    TFMobileBertForSequenceClassification,
	    TFMobileBertForTokenClassification,
	    TFMobileBertModel,
	)




@require_tf
class   lowercase_    (    SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	unittest.TestCase		):



	"""simple docstring"""





	UpperCAmelCase_    :   Tuple  =							(
	    (
	        TFMobileBertModel,
	        TFMobileBertForMaskedLM,
	        TFMobileBertForNextSentencePrediction,
	        TFMobileBertForPreTraining,
	        TFMobileBertForQuestionAnswering,
	        TFMobileBertForSequenceClassification,
	        TFMobileBertForTokenClassification,
	        TFMobileBertForMultipleChoice,
	    )
	    if is_tf_available()
	    else ()
	)
	UpperCAmelCase_    :   Optional[int]  =							(
	    {
	        """feature-extraction""": TFMobileBertModel,
	        """fill-mask""": TFMobileBertForMaskedLM,
	        """question-answering""": TFMobileBertForQuestionAnswering,
	        """text-classification""": TFMobileBertForSequenceClassification,
	        """token-classification""": TFMobileBertForTokenClassification,
	        """zero-shot""": TFMobileBertForSequenceClassification,
	    }
	    if is_tf_available()
	    else {}
	)
	UpperCAmelCase_    :   Optional[Any]  =							False
	UpperCAmelCase_    :   List[Any]  =							False


	def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE=False							)		->Any:
								lowerCAmelCase      						=						super()._prepare_for_class(snake_case__						,     snake_case__						,     return_labels=snake_case__							)

								if return_labels:
															if model_class in get_values(snake_case__							):
																						lowerCAmelCase      						=						tf.zeros(self.model_tester.batch_size						,     dtype=tf.intaa							)

								return inputs_dict




	class   lowercase_    (    SCREAMING_SNAKE_CASE__		):



		"""simple docstring"""





		def __init__(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE=13						,     __SCREAMING_SNAKE_CASE=7						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=True						,     __SCREAMING_SNAKE_CASE=99						,     __SCREAMING_SNAKE_CASE=32						,     __SCREAMING_SNAKE_CASE=32						,     __SCREAMING_SNAKE_CASE=2						,     __SCREAMING_SNAKE_CASE=4						,     __SCREAMING_SNAKE_CASE=37						,     __SCREAMING_SNAKE_CASE="gelu"						,     __SCREAMING_SNAKE_CASE=0.1						,     __SCREAMING_SNAKE_CASE=0.1						,     __SCREAMING_SNAKE_CASE=512						,     __SCREAMING_SNAKE_CASE=16						,     __SCREAMING_SNAKE_CASE=2						,     __SCREAMING_SNAKE_CASE=0.0_2						,     __SCREAMING_SNAKE_CASE=3						,     __SCREAMING_SNAKE_CASE=4						,     __SCREAMING_SNAKE_CASE=None						,     )		->Optional[Any]:
									lowerCAmelCase      						=						parent
									lowerCAmelCase      						=						batch_size
									lowerCAmelCase      						=						seq_length
									lowerCAmelCase      						=						is_training
									lowerCAmelCase      						=						use_input_mask
									lowerCAmelCase      						=						use_token_type_ids
									lowerCAmelCase      						=						use_labels
									lowerCAmelCase      						=						vocab_size
									lowerCAmelCase      						=						hidden_size
									lowerCAmelCase      						=						num_hidden_layers
									lowerCAmelCase      						=						num_attention_heads
									lowerCAmelCase      						=						intermediate_size
									lowerCAmelCase      						=						hidden_act
									lowerCAmelCase      						=						hidden_dropout_prob
									lowerCAmelCase      						=						attention_probs_dropout_prob
									lowerCAmelCase      						=						max_position_embeddings
									lowerCAmelCase      						=						type_vocab_size
									lowerCAmelCase      						=						type_sequence_label_size
									lowerCAmelCase      						=						initializer_range
									lowerCAmelCase      						=						num_labels
									lowerCAmelCase      						=						num_choices
									lowerCAmelCase      						=						scope
									lowerCAmelCase      						=						embedding_size


		def      SCREAMING_SNAKE_CASE_							(				self							)		->str:
									lowerCAmelCase      						=						ids_tensor([self.batch_size, self.seq_length]						,     self.vocab_size							)

									lowerCAmelCase      						=						None
									if self.use_input_mask:
																lowerCAmelCase      						=						random_attention_mask([self.batch_size, self.seq_length]							)

									lowerCAmelCase      						=						None
									if self.use_token_type_ids:
																lowerCAmelCase      						=						ids_tensor([self.batch_size, self.seq_length]						,     self.type_vocab_size							)

									lowerCAmelCase      						=						None
									lowerCAmelCase      						=						None
									lowerCAmelCase      						=						None
									if self.use_labels:
																lowerCAmelCase      						=						ids_tensor([self.batch_size]						,     self.type_sequence_label_size							)
																lowerCAmelCase      						=						ids_tensor([self.batch_size, self.seq_length]						,     self.num_labels							)
																lowerCAmelCase      						=						ids_tensor([self.batch_size]						,     self.num_choices							)

									lowerCAmelCase      						=						MobileBertConfig(
									    vocab_size=self.vocab_size						,     hidden_size=self.hidden_size						,     num_hidden_layers=self.num_hidden_layers						,     num_attention_heads=self.num_attention_heads						,     intermediate_size=self.intermediate_size						,     hidden_act=self.hidden_act						,     hidden_dropout_prob=self.hidden_dropout_prob						,     attention_probs_dropout_prob=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     type_vocab_size=self.type_vocab_size						,     initializer_range=self.initializer_range						,     embedding_size=self.embedding_size						,     )

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->List[Any]:
									lowerCAmelCase      						=						TFMobileBertModel(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)

									lowerCAmelCase      						=						[input_ids, input_mask]
									lowerCAmelCase      						=						model(snake_case__							)

									lowerCAmelCase      						=						model(snake_case__							)

									self.parent.assertEqual(
									    result.last_hidden_state.shape						,     (self.batch_size, self.seq_length, self.hidden_size)							)
									self.parent.assertEqual(result.pooler_output.shape						,     (self.batch_size, self.hidden_size)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->Optional[int]:
									lowerCAmelCase      						=						TFMobileBertForMaskedLM(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->Optional[int]:
									lowerCAmelCase      						=						TFMobileBertForNextSentencePrediction(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, 2)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->Union[str, Any]:
									lowerCAmelCase      						=						TFMobileBertForPreTraining(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(
									    result.prediction_logits.shape						,     (self.batch_size, self.seq_length, self.vocab_size)							)
									self.parent.assertEqual(result.seq_relationship_logits.shape						,     (self.batch_size, 2)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->List[Any]:
									lowerCAmelCase      						=						self.num_labels
									lowerCAmelCase      						=						TFMobileBertForSequenceClassification(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->Optional[Any]:
									lowerCAmelCase      						=						self.num_choices
									lowerCAmelCase      						=						TFMobileBertForMultipleChoice(config=snake_case__							)
									lowerCAmelCase      						=						tf.tile(tf.expand_dims(snake_case__						,     1							)						,     (1, self.num_choices, 1)							)
									lowerCAmelCase      						=						tf.tile(tf.expand_dims(snake_case__						,     1							)						,     (1, self.num_choices, 1)							)
									lowerCAmelCase      						=						tf.tile(tf.expand_dims(snake_case__						,     1							)						,     (1, self.num_choices, 1)							)
									lowerCAmelCase      						=						{
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									    '''token_type_ids''': multiple_choice_token_type_ids,
									}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_choices)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->List[Any]:
									lowerCAmelCase      						=						self.num_labels
									lowerCAmelCase      						=						TFMobileBertForTokenClassification(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.seq_length, self.num_labels)							)


		def      SCREAMING_SNAKE_CASE_							(				self						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE							)		->List[str]:
									lowerCAmelCase      						=						TFMobileBertForQuestionAnswering(config=snake_case__							)
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									lowerCAmelCase      						=						model(snake_case__							)
									self.parent.assertEqual(result.start_logits.shape						,     (self.batch_size, self.seq_length)							)
									self.parent.assertEqual(result.end_logits.shape						,     (self.batch_size, self.seq_length)							)




		def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
									lowerCAmelCase      						=						self.prepare_config_and_inputs()
									(
									    (
									    lowerCAmelCase				
									)						, (
									    lowerCAmelCase				
									)						, (
									    lowerCAmelCase				
									)						, (
									    lowerCAmelCase				
									)						, (
									    lowerCAmelCase				
									)						, (
									    lowerCAmelCase				
									)						, (
									    lowerCAmelCase				
									)						, 				
									)      						=						config_and_inputs
									lowerCAmelCase      						=						{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
									return config, inputs_dict


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Any:
								lowerCAmelCase      						=						TFMobileBertModelTest.TFMobileBertModelTester(self							)
								lowerCAmelCase      						=						ConfigTester(self						,     config_class=snake_case__						,     hidden_size=37							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Union[str, Any]:
								self.config_tester.run_common_tests()


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_model(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Dict:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_masked_lm(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->str:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_multiple_choice(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->str:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_pretraining(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->int:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_question_answering(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Any:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_sequence_classification(*snake_case__							)


	def      SCREAMING_SNAKE_CASE_							(				self							)		->Optional[int]:
								lowerCAmelCase      						=						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_mobilebert_for_token_classification(*snake_case__							)




	@slow
	def      SCREAMING_SNAKE_CASE_							(				self							)		->int:

								for model_name in ["google/mobilebert-uncased"]:
															lowerCAmelCase      						=						TFMobileBertModel.from_pretrained(snake_case__							)
															self.assertIsNotNone(snake_case__							)




@require_tf
class   lowercase_    (    unittest.TestCase		):



	"""simple docstring"""





	@slow
	def      SCREAMING_SNAKE_CASE_							(				self							)		->Tuple:
								lowerCAmelCase      						=						TFMobileBertForPreTraining.from_pretrained('''google/mobilebert-uncased'''							)
								lowerCAmelCase      						=						tf.constant([[0, 1, 2, 3, 4, 5]]							)
								lowerCAmelCase      						=						model(snake_case__							)[0]

								lowerCAmelCase      						=						[1, 6, 30522]
								self.assertEqual(output.shape						,     snake_case__							)

								lowerCAmelCase      						=						tf.constant(
								    [
								        [
								            [-4.5_9_1_9_5_4_7, -9.2_4_8_2_9_5, -9.6_4_5_2_5_6],
								            [-6.7_3_0_6_1_7_5, -6.4_4_0_2_8_4, -6.6_0_5_2_8_3_7],
								            [-7.2_7_4_3_5_0_6, -6.7_8_4_7_9_1_5, -6.0_2_4_6_7_3],
								        ]
								    ]							)
								tf.debugging.assert_near(output[:, :3, :3]						,     snake_case__						,     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:     int									= True


@st.cache(allow_output_mutation=lowerCAmelCase )
def 					_a							(					)->	int:
		if LOAD_DENSE_INDEX:
				SCREAMING_SNAKE_CASE_					      =							AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased' )
				SCREAMING_SNAKE_CASE_					      =							AutoModel.from_pretrained('yjernite/retribert-base-uncased' ).to('cuda:0' )
				SCREAMING_SNAKE_CASE_					      =							qar_model.eval()
		else:
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							(None, None)
		if MODEL_TYPE == "bart":
				SCREAMING_SNAKE_CASE_					      =							AutoTokenizer.from_pretrained('yjernite/bart_eli5' )
				SCREAMING_SNAKE_CASE_					      =							AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5' ).to('cuda:0' )
				SCREAMING_SNAKE_CASE_					      =							torch.load('seq2seq_models/eli5_bart_model_blm_2.pth' )
				sas_model.load_state_dict(save_dict['model'] )
				SCREAMING_SNAKE_CASE_					      =							sas_model.eval()
		else:
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							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 					_a							(					)->	Union[str, Any]:
		if LOAD_DENSE_INDEX:
				SCREAMING_SNAKE_CASE_					      =							faiss.StandardGpuResources()
				SCREAMING_SNAKE_CASE_					      =							datasets.load_dataset(path='wiki_snippets'    ,						name='wiki40b_en_100_0' )['train']
				SCREAMING_SNAKE_CASE_					      =							np.memmap(
				    'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat'    ,						dtype='float32'    ,						mode='r'    ,						shape=(wikiaab_passages.num_rows, 128)    ,						)
				SCREAMING_SNAKE_CASE_					      =							faiss.IndexFlatIP(128 )
				SCREAMING_SNAKE_CASE_					      =							faiss.index_cpu_to_gpu(lowerCAmelCase    ,						1    ,						lowerCAmelCase )
				wikiaab_gpu_index_flat.add(lowerCAmelCase )  # TODO fix for larger GPU
		else:
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							(None, None)
		SCREAMING_SNAKE_CASE_					      =							Elasticsearch([{'host': 'localhost', 'port': '9200'}] )
		return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)


@st.cache(allow_output_mutation=lowerCAmelCase )
def 					_a							(					)->	Optional[Any]:
		SCREAMING_SNAKE_CASE_					      =							datasets.load_dataset('eli5'    ,						name='LFQA_reddit' )
		SCREAMING_SNAKE_CASE_					      =							elia['train_eli5']
		SCREAMING_SNAKE_CASE_					      =							np.memmap(
		    'eli5_questions_reps.dat'    ,						dtype='float32'    ,						mode='r'    ,						shape=(elia_train.num_rows, 128) )
		SCREAMING_SNAKE_CASE_					      =							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:     Optional[Any]									= load_indexes()
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     List[Any]									= load_models()
SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     List[str]									= load_train_data()


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase=10 )->	Tuple:
		SCREAMING_SNAKE_CASE_					      =							embed_questions_for_retrieval([question]    ,						lowerCAmelCase    ,						lowerCAmelCase )
		SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							eli5_train_q_index.search(lowerCAmelCase    ,						lowerCAmelCase )
		SCREAMING_SNAKE_CASE_					      =							[elia_train[int(lowerCAmelCase )] for i in I[0]]
		return nn_examples


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase="wiki40b"    ,						lowerCAmelCase="dense"    ,						lowerCAmelCase=10 )->	Union[str, Any]:
		if source == "none":
				SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							(' <P> '.join(['' for _ in range(11 )] ).strip(), [])
		else:
				if method == "dense":
						SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							query_qa_dense_index(
						    lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase )
				else:
						SCREAMING_SNAKE_CASE_      ,				SCREAMING_SNAKE_CASE_					      =							query_es_index(
						    lowerCAmelCase    ,						lowerCAmelCase    ,						index_name='english_wiki40b_snippets_100w'    ,						n_results=lowerCAmelCase    ,						)
		SCREAMING_SNAKE_CASE_					      =							[
		    (res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
		]
		SCREAMING_SNAKE_CASE_					      =							'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 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=64    ,						lowerCAmelCase=256    ,						lowerCAmelCase=False    ,						lowerCAmelCase=2    ,						lowerCAmelCase=0.9_5    ,						lowerCAmelCase=0.8 )->	Tuple:
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_					      =							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:     List[Any]									= '''<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'''
SCREAMING_SNAKE_CASE:     List[Any]									= '''
<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
SCREAMING_SNAKE_CASE:     Union[str, Any]									= '''
This demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).
First, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,
a pre-processed fixed snapshot of Wikipedia.
'''
st.sidebar.markdown(description, unsafe_allow_html=True)

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:     str									= st.sidebar.checkbox('''Demo options''')
if demo_options:
						SCREAMING_SNAKE_CASE:     Union[str, Any]									= st.sidebar.selectbox(
						    '''''',
						    action_list,
						    index=3,
						)
						SCREAMING_SNAKE_CASE:     Optional[Any]									= 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:     Optional[int]									= show_type == '''Show full text of passages'''
else:
						SCREAMING_SNAKE_CASE:     List[str]									= 3
						SCREAMING_SNAKE_CASE:     Tuple									= True

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

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

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

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

if st.button('''Show me!'''):
						if action in [0, 1, 3]:
												if index_type == "mixed":
																		SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     List[str]									= make_support(question, source=wiki_source, method='''dense''', n_results=1_0)
																		SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     List[Any]									= make_support(question, source=wiki_source, method='''sparse''', n_results=1_0)
																		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:     List[Any]									= support_list[:1_0]
																		SCREAMING_SNAKE_CASE:     Union[str, Any]									= '''<P> ''' + ''' <P> '''.join([res[-1] for res in support_list])
												else:
																		SCREAMING_SNAKE_CASE				,					SCREAMING_SNAKE_CASE:     Optional[int]									= make_support(question, source=wiki_source, method=index_type, n_results=1_0)
						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:     Dict									= '''https://en.wikipedia.org/wiki/{}'''.format(res[0].replace(''' ''', '''_'''))
																		SCREAMING_SNAKE_CASE:     Dict									= res[1].strip()
																		if sec_titles == "":
																								SCREAMING_SNAKE_CASE:     List[Any]									= '''[{}]({})'''.format(res[0], wiki_url)
																		else:
																								SCREAMING_SNAKE_CASE:     Optional[int]									= sec_titles.split(''' & ''')
																								SCREAMING_SNAKE_CASE:     str									= ''' & '''.join(
																								    ['''[{}]({}#{})'''.format(sec.strip(), wiki_url, sec.strip().replace(''' ''', '''_''')) for sec in sec_list]
																								)
																		st.markdown(
																		    '''{0:02d} - **Article**: {1:<18} <br>  _Section_: {2}'''.format(i + 1, res[0], sections),
																		    unsafe_allow_html=True,
																		)
																		if show_passages:
																								st.write(
																								    '''> <span style="font-family:arial; font-size:10pt;">''' + res[-1] + '''</span>''', unsafe_allow_html=True
																								)
						if action in [2, 3]:
												SCREAMING_SNAKE_CASE:     Dict									= find_nearest_training(question)
												SCREAMING_SNAKE_CASE:     int									= 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:     List[Any]									= [
												    '''{}. {}'''.format(i + 1, '''  \n'''.join([line.strip() for line in ans.split('''\n''') if line.strip() != '''''']))
												    for i, (ans, sc) in enumerate(zip(train_exple['''answers''']['''text'''], train_exple['''answers''']['''score''']))
												    if i == 0 or sc > 2
												]
												st.markdown('''##### Its answers were: \n\n {}'''.format('''\n'''.join(answers_st)))


SCREAMING_SNAKE_CASE:     str									= '''
---

**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)