






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


lowerCAmelCase_     =			logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

lowerCAmelCase_     =			{
    '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
lowerCAmelCase_     =			{
    '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
}
lowerCAmelCase_     =			sorted(arg_to_scheduler.keys())
lowerCAmelCase_     =			'{' + ', '.join(arg_to_scheduler_choices) + '}'
class   UpperCamelCase			(					pl.LightningModule			):







		"""simple docstring"""



		def __init__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							argparse.Namespace    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							List[Any]="base"    ,_SCREAMING_SNAKE_CASE		:							List[Any]=None    ,_SCREAMING_SNAKE_CASE		:							int=None    ,_SCREAMING_SNAKE_CASE		:							int=None    ,**_SCREAMING_SNAKE_CASE		:							Any    ,)     -> Tuple:


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

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

						if tokenizer is None:
										A									=   AutoTokenizer.from_pretrained(
										    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,cache_dir=_SCREAMING_SNAKE_CASE    ,)
						else:
										A									=   tokenizer
						A									=   MODEL_MODES[mode]
						if model is None:
										A									=   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=_SCREAMING_SNAKE_CASE    ,)
						else:
										A									=   model
		def    A(	self		:							Dict    ,*_SCREAMING_SNAKE_CASE		:							List[Any]    ,**_SCREAMING_SNAKE_CASE		:							Any		)     -> List[str]:


						'''simple docstring'''
						A									=   self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE    ,**_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							int		)     -> Dict:


						'''simple docstring'''
						A									=   arg_to_scheduler[self.hparams.lr_scheduler]
						A									=   get_schedule_func(
						    self.opt    ,num_warmup_steps=self.hparams.warmup_steps    ,num_training_steps=self.total_steps()		)
						A									=   {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler
		def    A(	self		:							Union[str, Any]		)     -> List[Any]:


						'''simple docstring'''
						A									=   self.model
						A									=   ['bias', 'LayerNorm.weight']
						A									=   [
						    {
						        '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:
										A									=   Adafactor(
										    _SCREAMING_SNAKE_CASE    ,lr=self.hparams.learning_rate    ,scale_parameter=_SCREAMING_SNAKE_CASE    ,relative_step=_SCREAMING_SNAKE_CASE		)

						else:
										A									=   AdamW(
										    _SCREAMING_SNAKE_CASE    ,lr=self.hparams.learning_rate    ,eps=self.hparams.adam_epsilon		)
						A									=   optimizer

						A									=   self.get_lr_scheduler()

						return [optimizer], [scheduler]
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]		)     -> Dict:


						'''simple docstring'''
						return self.validation_step(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[int]		)     -> Dict:


						'''simple docstring'''
						return self.validation_end(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							List[str]		)     -> int:


						'''simple docstring'''
						A									=   max(1    ,self.hparams.gpus		)  # TODO: consider num_tpu_cores
						A									=   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		:							int    ,_SCREAMING_SNAKE_CASE		:							str		)     -> List[str]:


						'''simple docstring'''
						if mode == "test":
										A									=   len(self.test_dataloader().dataset		)
						else:
										A									=   self.get_dataloader('train'    ,self.hparams.train_batch_size    ,shuffle=_SCREAMING_SNAKE_CASE		)
										A									=   len(self.train_dataloader().dataset		)
		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							bool = False		)     -> List[Any]:


						'''simple docstring'''
						raise NotImplementedError('You must implement this for your task'		)
		def    A(	self		:							Dict		)     -> Any:


						'''simple docstring'''
						return self.train_loader
		def    A(	self		:							Optional[int]		)     -> List[Any]:


						'''simple docstring'''
						return self.get_dataloader('dev'    ,self.hparams.eval_batch_size    ,shuffle=_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							List[str]		)     -> str:


						'''simple docstring'''
						return self.get_dataloader('test'    ,self.hparams.eval_batch_size    ,shuffle=_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							int		)     -> Any:


						'''simple docstring'''
						return os.path.join(
						    self.hparams.data_dir    ,'cached_{}_{}_{}'.format(
						        _SCREAMING_SNAKE_CASE    ,list(filter(_SCREAMING_SNAKE_CASE    ,self.hparams.model_name_or_path.split('/'		)		)		).pop()    ,str(self.hparams.max_seq_length		)    ,)    ,)
		@pl.utilities.rank_zero_only
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Dict[str, Any]		)     -> None:


						'''simple docstring'''
						A									=   self.output_dir.joinpath('best_tfmr'		)
						A									=   self.step_count
						self.model.save_pretrained(_SCREAMING_SNAKE_CASE		)
						self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)







		@staticmethod
		def    A(	_SCREAMING_SNAKE_CASE		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						parser.add_argument(
						    '--model_name_or_path'    ,default=_SCREAMING_SNAKE_CASE    ,type=_SCREAMING_SNAKE_CASE    ,required=_SCREAMING_SNAKE_CASE    ,help='Path to pretrained model or model identifier from huggingface.co/models'    ,)
						parser.add_argument(
						    '--config_name'    ,default=''    ,type=_SCREAMING_SNAKE_CASE    ,help='Pretrained config name or path if not the same as model_name'		)
						parser.add_argument(
						    '--tokenizer_name'    ,default=_SCREAMING_SNAKE_CASE    ,type=_SCREAMING_SNAKE_CASE    ,help='Pretrained tokenizer name or path if not the same as model_name'    ,)
						parser.add_argument(
						    '--cache_dir'    ,default=''    ,type=_SCREAMING_SNAKE_CASE    ,help='Where do you want to store the pre-trained models downloaded from huggingface.co'    ,)
						parser.add_argument(
						    '--encoder_layerdrop'    ,type=_SCREAMING_SNAKE_CASE    ,help='Encoder layer dropout probability (Optional). Goes into model.config'    ,)
						parser.add_argument(
						    '--decoder_layerdrop'    ,type=_SCREAMING_SNAKE_CASE    ,help='Decoder layer dropout probability (Optional). Goes into model.config'    ,)
						parser.add_argument(
						    '--dropout'    ,type=_SCREAMING_SNAKE_CASE    ,help='Dropout probability (Optional). Goes into model.config'    ,)
						parser.add_argument(
						    '--attention_dropout'    ,type=_SCREAMING_SNAKE_CASE    ,help='Attention dropout probability (Optional). Goes into model.config'    ,)
						parser.add_argument('--learning_rate'    ,default=5E-5    ,type=_SCREAMING_SNAKE_CASE    ,help='The initial learning rate for Adam.'		)
						parser.add_argument(
						    '--lr_scheduler'    ,default='linear'    ,choices=_SCREAMING_SNAKE_CASE    ,metavar=_SCREAMING_SNAKE_CASE    ,type=_SCREAMING_SNAKE_CASE    ,help='Learning rate scheduler'    ,)
						parser.add_argument('--weight_decay'    ,default=0.0    ,type=_SCREAMING_SNAKE_CASE    ,help='Weight decay if we apply some.'		)
						parser.add_argument('--adam_epsilon'    ,default=1E-8    ,type=_SCREAMING_SNAKE_CASE    ,help='Epsilon for Adam optimizer.'		)
						parser.add_argument('--warmup_steps'    ,default=0    ,type=_SCREAMING_SNAKE_CASE    ,help='Linear warmup over warmup_steps.'		)
						parser.add_argument('--num_workers'    ,default=4    ,type=_SCREAMING_SNAKE_CASE    ,help='kwarg passed to DataLoader'		)
						parser.add_argument('--num_train_epochs'    ,dest='max_epochs'    ,default=3    ,type=_SCREAMING_SNAKE_CASE		)
						parser.add_argument('--train_batch_size'    ,default=3_2    ,type=_SCREAMING_SNAKE_CASE		)
						parser.add_argument('--eval_batch_size'    ,default=3_2    ,type=_SCREAMING_SNAKE_CASE		)
						parser.add_argument('--adafactor'    ,action='store_true'		)


class   UpperCamelCase			(					pl.Callback			):







		"""simple docstring"""



		def    A(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   trainer.lr_schedulers[0]['scheduler']
						A									=   {f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()		)}
						pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							pl.Trainer    ,_SCREAMING_SNAKE_CASE		:							pl.LightningModule		)     -> List[Any]:


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







		def    A(	self		:							List[Any]    ,_SCREAMING_SNAKE_CASE		:							pl.Trainer    ,_SCREAMING_SNAKE_CASE		:							pl.LightningModule		)     -> str:


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







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							int ):
				#  To allow all pl args uncomment the following line
				#  parser = pl.Trainer.add_argparse_args(parser)
				parser.add_argument(
				    '--output_dir',    default=UpperCAmelCase,    type=UpperCAmelCase,    required=UpperCAmelCase,    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=UpperCAmelCase,    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=UpperCAmelCase )
				parser.add_argument('--max_grad_norm',    dest='gradient_clip_val',    default=1.0,    type=UpperCAmelCase,    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=UpperCAmelCase,    default=1,    help='Number of updates steps to accumulate before performing a backward/update pass.',    )
				parser.add_argument('--seed',    type=UpperCAmelCase,    default=42,    help='random seed for initialization' )
				parser.add_argument(
				    '--data_dir',    default=UpperCAmelCase,    type=UpperCAmelCase,    required=UpperCAmelCase,    help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.',    )







def   snake_case		(				UpperCAmelCase						:							BaseTransformer,    UpperCAmelCase						:							argparse.Namespace,    UpperCAmelCase						:							List[Any]=None,    UpperCAmelCase						:							int=True,    UpperCAmelCase						:							Union[str, Any]=[],    UpperCAmelCase						:							str=None,    UpperCAmelCase						:							Tuple=None,    **UpperCAmelCase						:							Optional[int],    ):
				pl.seed_everything(args.seed )

				# init model
				A									=   Path(model.hparams.output_dir )
				odir.mkdir(exist_ok=UpperCAmelCase )

				# add custom checkpoints
				if checkpoint_callback is None:
								A									=   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(UpperCAmelCase )
				if logging_callback is None:
								A									=   LoggingCallback()

				A									=   {}

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

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

				A									=   args.accumulate_grad_batches
				A									=   extra_train_kwargs.get('accelerator',    UpperCAmelCase )
				A									=   extra_train_kwargs.get('profiler',    UpperCAmelCase )

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

				if args.do_train:
								trainer.fit(UpperCAmelCase )

				return trainer













from __future__ import annotations







def   snake_case		(				UpperCAmelCase						:							int,    UpperCAmelCase						:							list[int],    UpperCAmelCase						:							list[int],    UpperCAmelCase						:							int ):

				# Base Case
				if counter == 0 or capacity == 0:
								return 0

				# If weight of the nth item is more than Knapsack of capacity,
				#   then this item cannot be included in the optimal solution,
				# else return the maximum of two cases:
				#   (1) nth item included
				#   (2) not included
				if weights[counter - 1] > capacity:
								return knapsack(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
				else:
								A									=   capacity - weights[counter - 1]
								A									=   values[counter - 1] + knapsack(
								    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
								A									=   knapsack(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    counter - 1 )
								return max(UpperCAmelCase,    UpperCAmelCase )


if __name__ == "__main__":
					import doctest

					doctest.testmod()





