


import numpy as np






def 						_A						(						_lowercase				)			->				List[str]:


     """simple docstring"""


     __UpperCamelCase,  __UpperCamelCase				=	a.shape
     __UpperCamelCase				=	min(_lowercase			,       _lowercase				)
     __UpperCamelCase				=	np.eye(_lowercase				)
     __UpperCamelCase				=	a.copy()

     for k in range(t - 1				):
          # select a column of modified matrix A':
          __UpperCamelCase				=	r[k:, [k]]
          # construct first basis vector
          __UpperCamelCase				=	np.zeros_like(_lowercase				)
          __UpperCamelCase				=	1.0
          # determine scaling factor
          __UpperCamelCase				=	np.linalg.norm(_lowercase				)
          # construct vector v for Householder reflection
          __UpperCamelCase				=	x + np.sign(x[0]				) * alpha * ea
          v /= np.linalg.norm(_lowercase				)

          # construct the Householder matrix
          __UpperCamelCase				=	np.eye(m - k				) - 2.0 * v @ v.T
          # pad with ones and zeros as necessary
          __UpperCamelCase				=	np.block([[np.eye(_lowercase				), np.zeros((k, m - k)				)], [np.zeros((m - k, k)				), q_k]]				)

          __UpperCamelCase				=	q @ q_k.T
          __UpperCamelCase				=	q_k @ r

     return q, r


if __name__ == "__main__":
    import doctest

    doctest.testmod()






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


