


"""simple docstring"""




import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast






def 						_A						(						_lowercase			,       _lowercase=1_00			,       _lowercase=" "				)			->				Any:


     """simple docstring"""


     __UpperCamelCase				=	text.split(A__				)
     return [character.join(text[i : i + n]				).strip() for i in range(0			,       len(A__				)			,       A__				)]






def 						_A						(						_lowercase				)			->				Union[str, Any]:


     """simple docstring"""


     __UpperCamelCase				=	[], []
     for title, text in zip(documents['title']			,       documents['text']				):
          if text is not None:
               for passage in split_text(A__				):
                    titles.append(title if title is not None else ''				)
                    texts.append(A__				)
     return {"title": titles, "text": texts}






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				)			->				Tuple:


     """simple docstring"""


     __UpperCamelCase				=	load_dataset(
         'csv'			,       data_files=[csv_path]			,       split='train'			,       delimiter='\t'			,       column_names=['title', 'text']				)
     __UpperCamelCase				=	kb_dataset.map(
         A__			,       batched=A__			,       num_proc=1				)  # if you want you can load already splitted csv.
     __UpperCamelCase				=	[kb_dataset.shard(A__			,       A__			,       contiguous=A__				) for i in range(A__				)]
     __UpperCamelCase				=	kb_list[process_num]

     __UpperCamelCase				=	'data_' + str(A__				)
     __UpperCamelCase				=	os.path.join(A__			,       A__				)

     __UpperCamelCase				=	DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'				)
     __UpperCamelCase				=	ctx_encoder.to(device=A__				)

     def embed(
         _lowercase			,       _lowercase			,       _lowercase			,       _lowercase				) -> dict:
          __UpperCamelCase				=	ctx_tokenizer(
              documents['title']			,       documents['text']			,       truncation=A__			,       padding='longest'			,       return_tensors='pt'				)['input_ids']
          __UpperCamelCase				=	ctx_encoder(input_ids.to(device=A__				)			,       return_dict=A__				).pooler_output
          return {"embeddings": embeddings.detach().cpu().numpy()}

     __UpperCamelCase				=	Features(
         {'text': Value('string'				), 'title': Value('string'				), 'embeddings': Sequence(Value('float32'				)				)}				)  # optional, save as float32 instead of float64 to save space

     __UpperCamelCase				=	data_shrad.map(
         partial(A__			,       ctx_encoder=A__			,       ctx_tokenizer=A__			,       device=A__				)			,       batched=A__			,       batch_size=16			,       features=A__			,       )
     dataset.save_to_disk(A__				)






def 						_A						(						_lowercase			,       _lowercase				)			->				Optional[int]:


     """simple docstring"""


     __UpperCamelCase				=	[]

     for shard_address in glob(str(A__				) + '/*/'				):
          data_shard_list.append(load_from_disk(A__				)				)

     __UpperCamelCase				=	concatenate_datasets(A__				)
     faiss.omp_set_num_threads(96				)

     __UpperCamelCase				=	faiss.IndexHNSWFlat(7_68			,       1_28			,       faiss.METRIC_INNER_PRODUCT				)
     concat.add_faiss_index('embeddings'			,       custom_index=A__				)
     concat.get_index('embeddings'				).save(
         A__				)  # since we load the index in to memory,we can directly update the index in the disk






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


__snake_case     =		logging.getLogger(__name__)

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

__snake_case     =		{
    '''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
__snake_case     =		{
    '''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
}
__snake_case     =		sorted(arg_to_scheduler.keys())
__snake_case     =		'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''



class 					__lowerCamelCase	(pl.LightningModule		):







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






            '''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(A_			)
            __UpperCamelCase				=	0
            __UpperCamelCase				=	Path(self.hparams.output_dir			)
            __UpperCamelCase				=	self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                 __UpperCamelCase				=	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=A_,**A_,)
            else:
                 __UpperCamelCase				=	config

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

            if tokenizer is None:
                 __UpperCamelCase				=	AutoTokenizer.from_pretrained(
                     self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,cache_dir=A_,)
            else:
                 __UpperCamelCase				=	tokenizer
            __UpperCamelCase				=	MODEL_MODES[mode]
            if model is None:
                 __UpperCamelCase				=	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=A_,)
            else:
                 __UpperCamelCase				=	model







       def 						snake_case_					(	self:				Dict,*A_:				Union[str, Any],**A_:				int			):






            '''simple docstring'''




            __UpperCamelCase				=	self.model_type.from_pretrained(*A_,**A_			)







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Optional[Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	self.model
            __UpperCamelCase				=	['bias', 'LayerNorm.weight']
            __UpperCamelCase				=	[
                {
                    'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay			)],
                    '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:
                 __UpperCamelCase				=	Adafactor(
                     A_,lr=self.hparams.learning_rate,scale_parameter=A_,relative_step=A_			)

            else:
                 __UpperCamelCase				=	AdamW(
                     A_,lr=self.hparams.learning_rate,eps=self.hparams.adam_epsilon			)
            __UpperCamelCase				=	optimizer

            __UpperCamelCase				=	self.get_lr_scheduler()

            return [optimizer], [scheduler]







       def 						snake_case_					(	self:				Any,A_:				List[Any],A_:				Dict			):






            '''simple docstring'''




            return self.validation_step(A_,A_			)







       def 						snake_case_					(	self:				List[Any],A_:				Dict			):






            '''simple docstring'''




            return self.validation_end(A_			)







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				int,A_:				Tuple			):






            '''simple docstring'''




            if mode == "test":
                 __UpperCamelCase				=	len(self.test_dataloader().dataset			)
            else:
                 __UpperCamelCase				=	self.get_dataloader('train',self.hparams.train_batch_size,shuffle=A_			)
                 __UpperCamelCase				=	len(self.train_dataloader().dataset			)







       def 						snake_case_					(	self:				List[str],A_:				str,A_:				int,A_:				bool = False			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Optional[int]			):






            '''simple docstring'''




            return self.train_loader







       def 						snake_case_					(	self:				int			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				str			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Any,A_:				Union[str, Any]			):






            '''simple docstring'''




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







       @pl.utilities.rank_zero_only
       def 						snake_case_					(	self:				Any,A_:				Dict[str, Any]			):






            '''simple docstring'''




            __UpperCamelCase				=	self.output_dir.joinpath('best_tfmr'			)
            __UpperCamelCase				=	self.step_count
            self.model.save_pretrained(A_			)
            self.tokenizer.save_pretrained(A_			)







       @staticmethod
       def 						snake_case_					(	A_:				int,A_:				List[Any]			):






            '''simple docstring'''




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



class 					__lowerCamelCase	(pl.Callback		):







       def 						snake_case_					(	self:				List[str],A_:				Any,A_:				List[str]			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				Optional[Any],A_:				pl.Trainer,A_:				pl.LightningModule			):






            '''simple docstring'''




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







       def 						snake_case_					(	self:				List[str],A_:				pl.Trainer,A_:				pl.LightningModule			):






            '''simple docstring'''




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






def 						_A						(						_lowercase			,       _lowercase				)			->				None:


     """simple docstring"""


     parser.add_argument(
         '--output_dir'			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       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=_lowercase			,       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=_lowercase				)
     parser.add_argument('--max_grad_norm'			,       dest='gradient_clip_val'			,       default=1.0			,       type=_lowercase			,       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=_lowercase			,       default=1			,       help='Number of updates steps to accumulate before performing a backward/update pass.'			,       )
     parser.add_argument('--seed'			,       type=_lowercase			,       default=42			,       help='random seed for initialization'				)
     parser.add_argument(
         '--data_dir'			,       default=_lowercase			,       type=_lowercase			,       required=_lowercase			,       help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.'			,       )






def 						_A						(						_lowercase			,       _lowercase			,       _lowercase=None			,       _lowercase=True			,       _lowercase=[]			,       _lowercase=None			,       _lowercase=None			,       **_lowercase			,       )			->				int:


     """simple docstring"""


     pl.seed_everything(args.seed				)

     # init model
     __UpperCamelCase				=	Path(model.hparams.output_dir				)
     odir.mkdir(exist_ok=_lowercase				)

     # add custom checkpoints
     if checkpoint_callback is None:
          __UpperCamelCase				=	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(_lowercase				)
     if logging_callback is None:
          __UpperCamelCase				=	LoggingCallback()

     __UpperCamelCase				=	{}

     # TODO: remove with PyTorch 1.6 since pl uses native amp
     if args.fpaa:
          __UpperCamelCase				=	16
          __UpperCamelCase				=	args.fpaa_opt_level

     if args.gpus > 1:
          __UpperCamelCase				=	'ddp'

     __UpperCamelCase				=	args.accumulate_grad_batches
     __UpperCamelCase				=	extra_train_kwargs.get('accelerator'			,       _lowercase				)
     __UpperCamelCase				=	extra_train_kwargs.get('profiler'			,       _lowercase				)

     __UpperCamelCase				=	pl.Trainer.from_argparse_args(
         _lowercase			,       weights_summary=_lowercase			,       callbacks=[logging_callback] + extra_callbacks			,       logger=_lowercase			,       checkpoint_callback=_lowercase			,       **_lowercase			,       )

     if args.do_train:
          trainer.fit(_lowercase				)

     return trainer


