

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


__A	:   Dict	=					logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

__A	:   Union[str, Any]	=					{
    '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
__A	:   Optional[Any]	=					{
    '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
}
__A	:   int	=					sorted(arg_to_scheduler.keys())
__A	:   Tuple	=					'{' + ', '.join(arg_to_scheduler_choices) + '}'


class 			_SCREAMING_SNAKE_CASE      (  pl.LightningModule):







						def __init__(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE="base"     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						_SCREAMING_SNAKE_CASE=None     ,						**_SCREAMING_SNAKE_CASE     ,						)->							Dict:
													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							)
													lowerCamelCase_								=0
													lowerCamelCase_								=Path(self.hparams.output_dir							)
													lowerCamelCase_								=self.hparams.cache_dir if self.hparams.cache_dir else None
													if config is None:
																				lowerCamelCase_								=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:
																				lowerCamelCase_								=config

													lowerCamelCase_								=("""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:
																				lowerCamelCase_								=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:
																				lowerCamelCase_								=tokenizer
													lowerCamelCase_								=MODEL_MODES[mode]
													if model is None:
																				lowerCamelCase_								=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:
																				lowerCamelCase_								=model







						def        _snake_case				(			self     ,						*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)->							int:
													lowerCamelCase_								=self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE     ,						**_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							int:
													lowerCamelCase_								=arg_to_scheduler[self.hparams.lr_scheduler]
													lowerCamelCase_								=get_schedule_func(
													    self.opt     ,						num_warmup_steps=self.hparams.warmup_steps     ,						num_training_steps=self.total_steps()							)
													lowerCamelCase_								={"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
													return scheduler







						def        _snake_case				(			self							)->							Any:
													lowerCamelCase_								=self.model
													lowerCamelCase_								=["""bias""", """LayerNorm.weight"""]
													lowerCamelCase_								=[
													    {
													        """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:
																				lowerCamelCase_								=Adafactor(
																				    _SCREAMING_SNAKE_CASE     ,						lr=self.hparams.learning_rate     ,						scale_parameter=_SCREAMING_SNAKE_CASE     ,						relative_step=_SCREAMING_SNAKE_CASE							)

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

													lowerCamelCase_								=self.get_lr_scheduler()

													return [optimizer], [scheduler]







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Optional[Any]:
													return self.validation_step(_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							List[str]:
													return self.validation_end(_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							int:
													lowerCamelCase_								=max(1     ,						self.hparams.gpus							)  # TODO: consider num_tpu_cores
													lowerCamelCase_								=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     ,						_SCREAMING_SNAKE_CASE							)->							Tuple:
													if mode == "test":
																				lowerCamelCase_								=len(self.test_dataloader().dataset							)
													else:
																				lowerCamelCase_								=self.get_dataloader("""train"""     ,						self.hparams.train_batch_size     ,						shuffle=_SCREAMING_SNAKE_CASE							)
																				lowerCamelCase_								=len(self.train_dataloader().dataset							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE = False							)->							int:
													raise NotImplementedError("""You must implement this for your task"""							)







						def        _snake_case				(			self							)->							Tuple:
													return self.train_loader







						def        _snake_case				(			self							)->							List[str]:
													return self.get_dataloader("""dev"""     ,						self.hparams.eval_batch_size     ,						shuffle=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self							)->							List[str]:
													return self.get_dataloader("""test"""     ,						self.hparams.eval_batch_size     ,						shuffle=_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							str:
													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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE							)->							None:
													lowerCamelCase_								=self.output_dir.joinpath("""best_tfmr"""							)
													lowerCamelCase_								=self.step_count
													self.model.save_pretrained(_SCREAMING_SNAKE_CASE							)
													self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE							)







						@staticmethod
						def        _snake_case				(			_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Dict:
													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=32     ,						type=_SCREAMING_SNAKE_CASE							)
													parser.add_argument("""--eval_batch_size"""     ,						default=32     ,						type=_SCREAMING_SNAKE_CASE							)
													parser.add_argument("""--adafactor"""     ,						action="""store_true"""							)


class 			_SCREAMING_SNAKE_CASE      (  pl.Callback):







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Tuple:
													lowerCamelCase_								=trainer.lr_schedulers[0]["""scheduler"""]
													lowerCamelCase_								={f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
													pl_module.logger.log_metrics(_SCREAMING_SNAKE_CASE							)







						def        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							Tuple:
													rank_zero_info("""***** Validation results *****"""							)
													lowerCamelCase_								=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        _snake_case				(			self     ,						_SCREAMING_SNAKE_CASE     ,						_SCREAMING_SNAKE_CASE							)->							List[Any]:
													rank_zero_info("""***** Test results *****"""							)
													lowerCamelCase_								=trainer.callback_metrics
													# Log and save results to file
													lowerCamelCase_								=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        __UpperCamelCase     (      _A					:							Dict      ,  _A					:							Any		)				->None:



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






def        __UpperCamelCase     (      _A					:							BaseTransformer      ,  _A					:							argparse.Namespace      ,  _A					:							List[Any]=None      ,  _A					:							Any=True      ,  _A					:							str=[]      ,  _A					:							Optional[Any]=None      ,  _A					:							Optional[int]=None      ,  **_A					:							Union[str, Any]      ,  )				->Dict:



							"""simple docstring"""
							pl.seed_everything(args.seed		)

							# init model
							lowerCamelCase_								=Path(model.hparams.output_dir		)
							odir.mkdir(exist_ok=_A		)

							# add custom checkpoints
							if checkpoint_callback is None:
														lowerCamelCase_								=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(_A		)
							if logging_callback is None:
														lowerCamelCase_								=LoggingCallback()

							lowerCamelCase_								={}

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

							if args.gpus > 1:
														lowerCamelCase_								="""ddp"""

							lowerCamelCase_								=args.accumulate_grad_batches
							lowerCamelCase_								=extra_train_kwargs.get("""accelerator"""      ,  _A		)
							lowerCamelCase_								=extra_train_kwargs.get("""profiler"""      ,  _A		)

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

							if args.do_train:
														trainer.fit(_A		)

							return trainer



from __future__ import annotations




def        __UpperCamelCase     (      _A					:							int      ,  _A					:							list[int]      ,  _A					:							list[int]      ,  _A					:							int		)				->int:



							"""simple docstring"""

							# 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(_A      ,  _A      ,  _A      ,  counter - 1		)
							else:
														lowerCamelCase_								=capacity - weights[counter - 1]
														lowerCamelCase_								=values[counter - 1] + knapsack(
														    _A      ,  _A      ,  _A      ,  counter - 1		)
														lowerCamelCase_								=knapsack(_A      ,  _A      ,  _A      ,  counter - 1		)
														return max(_A      ,  _A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
