




import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


SCREAMING_SNAKE_CASE:     Optional[int]									= logging.getLogger(__name__)

require_version('''pytorch_lightning>=1.0.4''')

SCREAMING_SNAKE_CASE:     Tuple									= {
    '''base''': AutoModel,
    '''sequence-classification''': AutoModelForSequenceClassification,
    '''question-answering''': AutoModelForQuestionAnswering,
    '''pretraining''': AutoModelForPreTraining,
    '''token-classification''': AutoModelForTokenClassification,
    '''language-modeling''': AutoModelWithLMHead,
    '''summarization''': AutoModelForSeqaSeqLM,
    '''translation''': AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
SCREAMING_SNAKE_CASE:     Optional[int]									= {
    '''linear''': get_linear_schedule_with_warmup,
    '''cosine''': get_cosine_schedule_with_warmup,
    '''cosine_w_restarts''': get_cosine_with_hard_restarts_schedule_with_warmup,
    '''polynomial''': get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
SCREAMING_SNAKE_CASE:     Tuple									= sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE:     str									= '''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class    lowercase_		(pl.LightningModule ):



						def __init__(			self	:   Tuple					,							snake_case__	:   argparse.Namespace					,							snake_case__	:   List[Any]=None					,							snake_case__	:   str="base"					,							snake_case__	:   str=None					,							snake_case__	:   Any=None					,							snake_case__	:   List[Any]=None					,							**snake_case__	:   Optional[int]					,							):

								"""simple docstring"""

								super().__init__()
								# TODO: move to self.save_hyperparameters()
								# self.save_hyperparameters()
								# can also expand arguments into trainer signature for easier reading

								self.save_hyperparameters(snake_case__  )
								SCREAMING_SNAKE_CASE_					      =							0
								SCREAMING_SNAKE_CASE_					      =							Path(self.hparams.output_dir  )
								SCREAMING_SNAKE_CASE_					      =							self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
										SCREAMING_SNAKE_CASE_					      =							AutoConfig.from_pretrained(
										    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path					,							**({'num_labels': num_labels} if num_labels is not None else {})					,							cache_dir=snake_case__					,							**snake_case__					,							)
								else:
										SCREAMING_SNAKE_CASE_					      =							config

								SCREAMING_SNAKE_CASE_					      =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
								for p in extra_model_params:
										if getattr(self.hparams					,							snake_case__					,							snake_case__  ):
												assert hasattr(self.config					,							snake_case__  ), f'''model config doesn\'t have a `{p}` attribute'''
												setattr(self.config					,							snake_case__					,							getattr(self.hparams					,							snake_case__  )  )

								if tokenizer is None:
										SCREAMING_SNAKE_CASE_					      =							AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,							cache_dir=snake_case__					,							)
								else:
										SCREAMING_SNAKE_CASE_					      =							tokenizer
								SCREAMING_SNAKE_CASE_					      =							MODEL_MODES[mode]
								if model is None:
										SCREAMING_SNAKE_CASE_					      =							self.model_type.from_pretrained(
										    self.hparams.model_name_or_path					,							from_tf=bool('.ckpt' in self.hparams.model_name_or_path  )					,							config=self.config					,							cache_dir=snake_case__					,							)
								else:
										SCREAMING_SNAKE_CASE_					      =							model



						def 	__a					(			self	:   List[Any]					,							*snake_case__	:   Any					,							**snake_case__	:   Dict  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model_type.from_pretrained(*snake_case__					,							**snake_case__  )



						def 	__a					(			self	:   int  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							arg_to_scheduler[self.hparams.lr_scheduler]
								SCREAMING_SNAKE_CASE_					      =							get_schedule_func(
								    self.opt					,							num_warmup_steps=self.hparams.warmup_steps					,							num_training_steps=self.total_steps()  )
								SCREAMING_SNAKE_CASE_					      =							{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
								return scheduler



						def 	__a					(			self	:   Optional[int]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.model
								SCREAMING_SNAKE_CASE_					      =							['bias', 'LayerNorm.weight']
								SCREAMING_SNAKE_CASE_					      =							[
								    {
								        'params': [
								            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay  )
								        ],  # check this named paramters
								        'weight_decay': self.hparams.weight_decay,
								    },
								    {
								        'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay  )],
								        'weight_decay': 0.0,
								    },
								]
								if self.hparams.adafactor:
										SCREAMING_SNAKE_CASE_					      =							Adafactor(
										    snake_case__					,							lr=self.hparams.learning_rate					,							scale_parameter=snake_case__					,							relative_step=snake_case__  )

								else:
										SCREAMING_SNAKE_CASE_					      =							AdamW(
										    snake_case__					,							lr=self.hparams.learning_rate					,							eps=self.hparams.adam_epsilon  )
								SCREAMING_SNAKE_CASE_					      =							optimizer

								SCREAMING_SNAKE_CASE_					      =							self.get_lr_scheduler()

								return [optimizer], [scheduler]



						def 	__a					(			self	:   str					,							snake_case__	:   Optional[int]					,							snake_case__	:   str  ):

								"""simple docstring"""

								return self.validation_step(snake_case__					,							snake_case__  )



						def 	__a					(			self	:   Tuple					,							snake_case__	:   List[Any]  ):

								"""simple docstring"""

								return self.validation_end(snake_case__  )



						def 	__a					(			self	:   Optional[Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							max(1					,							self.hparams.gpus  )  # TODO: consider num_tpu_cores
								SCREAMING_SNAKE_CASE_					      =							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
								return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



						def 	__a					(			self	:   str					,							snake_case__	:   Optional[int]  ):

								"""simple docstring"""

								if stage == "test":
										SCREAMING_SNAKE_CASE_					      =							len(self.test_dataloader().dataset  )
								else:
										SCREAMING_SNAKE_CASE_					      =							self.get_dataloader('train'					,							self.hparams.train_batch_size					,							shuffle=snake_case__  )
										SCREAMING_SNAKE_CASE_					      =							len(self.train_dataloader().dataset  )



						def 	__a					(			self	:   List[str]					,							snake_case__	:   str					,							snake_case__	:   int					,							snake_case__	:   bool = False  ):

								"""simple docstring"""

								raise NotImplementedError('You must implement this for your task'  )



						def 	__a					(			self	:   str  ):

								"""simple docstring"""

								return self.train_loader



						def 	__a					(			self	:   Any  ):

								"""simple docstring"""

								return self.get_dataloader('dev'					,							self.hparams.eval_batch_size					,							shuffle=snake_case__  )



						def 	__a					(			self	:   Dict  ):

								"""simple docstring"""

								return self.get_dataloader('test'					,							self.hparams.eval_batch_size					,							shuffle=snake_case__  )



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

								"""simple docstring"""

								return os.path.join(
								    self.hparams.data_dir					,							'cached_{}_{}_{}'.format(
								        snake_case__					,							list(filter(snake_case__					,							self.hparams.model_name_or_path.split('/'  )  )  ).pop()					,							str(self.hparams.max_seq_length  )					,							)					,							)



						@pl.utilities.rank_zero_only
						def 	__a					(			self	:   Tuple					,							snake_case__	:   Dict[str, Any]  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							self.output_dir.joinpath('best_tfmr'  )
								SCREAMING_SNAKE_CASE_					      =							self.step_count
								self.model.save_pretrained(snake_case__  )
								self.tokenizer.save_pretrained(snake_case__  )






						@staticmethod
						def 	__a					(			snake_case__	:   Dict					,							snake_case__	:   Tuple  ):

								"""simple docstring"""

								parser.add_argument(
								    '--model_name_or_path'					,							default=snake_case__					,							type=snake_case__					,							required=snake_case__					,							help='Path to pretrained model or model identifier from huggingface.co/models'					,							)
								parser.add_argument(
								    '--config_name'					,							default=''					,							type=snake_case__					,							help='Pretrained config name or path if not the same as model_name'  )
								parser.add_argument(
								    '--tokenizer_name'					,							default=snake_case__					,							type=snake_case__					,							help='Pretrained tokenizer name or path if not the same as model_name'					,							)
								parser.add_argument(
								    '--cache_dir'					,							default=str(Path(snake_case__  ).parent / 'test_run' / 'cache'  )					,							type=snake_case__					,							help='Where do you want to store the pre-trained models downloaded from huggingface.co'					,							)
								parser.add_argument(
								    '--encoder_layerdrop'					,							type=snake_case__					,							help='Encoder layer dropout probability (Optional). Goes into model.config'					,							)
								parser.add_argument(
								    '--decoder_layerdrop'					,							type=snake_case__					,							help='Decoder layer dropout probability (Optional). Goes into model.config'					,							)
								parser.add_argument(
								    '--dropout'					,							type=snake_case__					,							help='Dropout probability (Optional). Goes into model.config'					,							)
								parser.add_argument(
								    '--attention_dropout'					,							type=snake_case__					,							help='Attention dropout probability (Optional). Goes into model.config'					,							)
								parser.add_argument('--learning_rate'					,							default=5e-5					,							type=snake_case__					,							help='The initial learning rate for Adam.'  )
								parser.add_argument(
								    '--lr_scheduler'					,							default='linear'					,							choices=snake_case__					,							metavar=snake_case__					,							type=snake_case__					,							help='Learning rate scheduler'					,							)
								parser.add_argument('--weight_decay'					,							default=0.0					,							type=snake_case__					,							help='Weight decay if we apply some.'  )
								parser.add_argument('--adam_epsilon'					,							default=1e-8					,							type=snake_case__					,							help='Epsilon for Adam optimizer.'  )
								parser.add_argument('--warmup_steps'					,							default=0					,							type=snake_case__					,							help='Linear warmup over warmup_steps.'  )
								parser.add_argument('--num_workers'					,							default=4					,							type=snake_case__					,							help='kwarg passed to DataLoader'  )
								parser.add_argument('--num_train_epochs'					,							dest='max_epochs'					,							default=3					,							type=snake_case__  )
								parser.add_argument('--train_batch_size'					,							default=32					,							type=snake_case__  )
								parser.add_argument('--eval_batch_size'					,							default=32					,							type=snake_case__  )
								parser.add_argument('--adafactor'					,							action='store_true'  )



class    lowercase_		(pl.Callback ):



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

								"""simple docstring"""

								if (
								    trainer.is_global_zero and trainer.global_rank == 0
								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
										pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class    lowercase_		(pl.Callback ):



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

								"""simple docstring"""

								for name, param in pl_module.model.rag.named_parameters():
										if param.grad is None:
												print(snake_case__  )



class    lowercase_		(pl.Callback ):



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

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							trainer.lr_schedulers[0]['scheduler']
								SCREAMING_SNAKE_CASE_					      =							{f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
								pl_module.logger.log_metrics(snake_case__  )



						def 	__a					(			self	:   str					,							snake_case__	:   pl.Trainer					,							snake_case__	:   pl.LightningModule  ):

								"""simple docstring"""

								rank_zero_info('***** Validation results *****'  )
								SCREAMING_SNAKE_CASE_					      =							trainer.callback_metrics
								# Log results
								for key in sorted(snake_case__  ):
										if key not in ["log", "progress_bar"]:
												rank_zero_info('{} = {}\n'.format(snake_case__					,							str(metrics[key]  )  )  )






						def 	__a					(			self	:   int					,							snake_case__	:   pl.Trainer					,							snake_case__	:   pl.LightningModule  ):

								"""simple docstring"""

								rank_zero_info('***** Test results *****'  )
								SCREAMING_SNAKE_CASE_					      =							trainer.callback_metrics
								# Log and save results to file
								SCREAMING_SNAKE_CASE_					      =							os.path.join(pl_module.hparams.output_dir					,							'test_results.txt'  )
								with open(snake_case__					,							'w'  ) as writer:
										for key in sorted(snake_case__  ):
												if key not in ["log", "progress_bar"]:
														rank_zero_info('{} = {}\n'.format(snake_case__					,							str(metrics[key]  )  )  )
														writer.write('{} = {}\n'.format(snake_case__					,							str(metrics[key]  )  )  )


def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase )->	None:
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    '--output_dir'    ,						default=str(Path(lowerCAmelCase ).parent / 'test_run' / 'model_checkpoints' )    ,						type=lowerCAmelCase    ,						help='The output directory where the model predictions and checkpoints will be written.'    ,						)
		parser.add_argument(
		    '--fp16'    ,						action='store_true'    ,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'    ,						)

		parser.add_argument(
		    '--fp16_opt_level'    ,						type=lowerCAmelCase    ,						default='O2'    ,						help=(
		        'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
		        'See details at https://nvidia.github.io/apex/amp.html'
		    )    ,						)
		parser.add_argument('--n_tpu_cores'    ,						dest='tpu_cores'    ,						type=lowerCAmelCase )
		parser.add_argument('--max_grad_norm'    ,						dest='gradient_clip_val'    ,						default=1.0    ,						type=lowerCAmelCase    ,						help='Max gradient norm' )
		parser.add_argument('--do_train'    ,						action='store_true'    ,						help='Whether to run training.' )
		parser.add_argument('--do_predict'    ,						action='store_true'    ,						help='Whether to run predictions on the test set.' )
		parser.add_argument(
		    '--gradient_accumulation_steps'    ,						dest='accumulate_grad_batches'    ,						type=lowerCAmelCase    ,						default=1    ,						help='Number of updates steps to accumulate before performing a backward/update pass.'    ,						)
		parser.add_argument('--seed'    ,						type=lowerCAmelCase    ,						default=42    ,						help='random seed for initialization' )
		parser.add_argument(
		    '--data_dir'    ,						default=str(Path(lowerCAmelCase ).parent / 'test_run' / 'dummy-train-data' )    ,						type=lowerCAmelCase    ,						help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'    ,						)




def 					_a							(					lowerCAmelCase    ,						lowerCAmelCase    ,						lowerCAmelCase=None    ,						lowerCAmelCase=True    ,						lowerCAmelCase=[]    ,						lowerCAmelCase=None    ,						lowerCAmelCase=None    ,						**lowerCAmelCase    ,						)->	Union[str, Any]:
		pl.seed_everything(args.seed )

		# init model
		SCREAMING_SNAKE_CASE_					      =							Path(model.hparams.output_dir )
		odir.mkdir(exist_ok=lowerCAmelCase )

		# add custom checkpoints
		if checkpoint_callback is None:
				SCREAMING_SNAKE_CASE_					      =							pl.callbacks.ModelCheckpoint(
				    filepath=args.output_dir    ,						prefix='checkpoint'    ,						monitor='val_loss'    ,						mode='min'    ,						save_top_k=1 )
		if early_stopping_callback:
				extra_callbacks.append(lowerCAmelCase )
		if logging_callback is None:
				SCREAMING_SNAKE_CASE_					      =							LoggingCallback()

		SCREAMING_SNAKE_CASE_					      =							{}

		if args.fpaa:
				SCREAMING_SNAKE_CASE_					      =							16

		if args.gpus > 1:
				SCREAMING_SNAKE_CASE_					      =							'auto'
				SCREAMING_SNAKE_CASE_					      =							'ddp'

		SCREAMING_SNAKE_CASE_					      =							args.accumulate_grad_batches
		SCREAMING_SNAKE_CASE_					      =							None
		SCREAMING_SNAKE_CASE_					      =							'auto'

		SCREAMING_SNAKE_CASE_					      =							pl.Trainer.from_argparse_args(
		    lowerCAmelCase    ,						weights_summary=lowerCAmelCase    ,						callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]    ,						logger=lowerCAmelCase    ,						val_check_interval=1    ,						num_sanity_val_steps=2    ,						**lowerCAmelCase    ,						)

		if args.do_train:
				trainer.fit(lowerCAmelCase )

		else:
				print('RAG modeling tests with new set functions successfuly executed!' )
		return trainer





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)