

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


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

require_version("pytorch_lightning>=1.0.4")

lowercase     :       List[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
lowercase     :       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
}
lowercase     :       List[Any]        =       sorted(arg_to_scheduler.keys())
lowercase     :       Dict        =       "{" + ", ".join(arg_to_scheduler_choices) + "}"
class 		SCREAMING_SNAKE_CASE__					(		pl.LightningModule   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase , __UpperCamelCase=None , __UpperCamelCase="base" , __UpperCamelCase=None , __UpperCamelCase=None , __UpperCamelCase=None , **__UpperCamelCase , )							->	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(__UpperCamelCase    )
									__UpperCamelCase    :    Optional[Any]								=      0
									__UpperCamelCase    :    int								=      Path(self.hparams.output_dir    )
									__UpperCamelCase    :    Any								=      self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
												__UpperCamelCase    :    str								=      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=__UpperCamelCase , **__UpperCamelCase , )
									else:
												__UpperCamelCase    :    PretrainedConfig								=      config

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

									if tokenizer is None:
												__UpperCamelCase    :    int								=      AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path , cache_dir=__UpperCamelCase , )
									else:
												__UpperCamelCase    :    PreTrainedTokenizer								=      tokenizer
									__UpperCamelCase    :    Tuple								=      MODEL_MODES[mode]
									if model is None:
												__UpperCamelCase    :    List[Any]								=      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=__UpperCamelCase , )
									else:
												__UpperCamelCase    :    Optional[int]								=      model

						def __lowerCamelCase					(				self , *__UpperCamelCase , **__UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      self.model_type.from_pretrained(*__UpperCamelCase , **__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      self.model
									__UpperCamelCase    :    Dict								=      ["bias", "LayerNorm.weight"]
									__UpperCamelCase    :    List[Any]								=      [
									    {
									        "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    :    Union[str, Any]								=      Adafactor(
												    __UpperCamelCase , lr=self.hparams.learning_rate , scale_parameter=__UpperCamelCase , relative_step=__UpperCamelCase    )

									else:
												__UpperCamelCase    :    List[Any]								=      AdamW(
												    __UpperCamelCase , lr=self.hparams.learning_rate , eps=self.hparams.adam_epsilon    )
									__UpperCamelCase    :    Any								=      optimizer

									__UpperCamelCase    :    List[str]								=      self.get_lr_scheduler()

									return [optimizer], [scheduler]

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


									return self.validation_step(__UpperCamelCase , __UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	Any:




									'''simple docstring'''


									return self.validation_end(__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									if mode == "test":
												__UpperCamelCase    :    str								=      len(self.test_dataloader().dataset    )
									else:
												__UpperCamelCase    :    Optional[Any]								=      self.get_dataloader("train" , self.hparams.train_batch_size , shuffle=__UpperCamelCase    )
												__UpperCamelCase    :    int								=      len(self.train_dataloader().dataset    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase = False    )							->	Any:




									'''simple docstring'''


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

						def __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									return self.train_loader

						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									return self.get_dataloader("dev" , self.hparams.eval_batch_size , shuffle=__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									return self.get_dataloader("test" , self.hparams.eval_batch_size , shuffle=__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	List[Any]:




									'''simple docstring'''


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

						@pl.utilities.rank_zero_only
						def __lowerCamelCase					(				self , __UpperCamelCase    )							->	None:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      self.output_dir.joinpath("best_tfmr"    )
									__UpperCamelCase    :    Dict								=      self.step_count
									self.model.save_pretrained(__UpperCamelCase    )
									self.tokenizer.save_pretrained(__UpperCamelCase    )




						@staticmethod
						def __lowerCamelCase					(				__UpperCamelCase , __UpperCamelCase    )							->	Any:




									'''simple docstring'''


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







class 		SCREAMING_SNAKE_CASE__					(		pl.Callback   ):







						"""simple docstring"""




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      trainer.lr_schedulers[0]["scheduler"]
									__UpperCamelCase    :    List[str]								=      {f'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()    )}
									pl_module.logger.log_metrics(__UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	Any:




									'''simple docstring'''


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




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase    )							->	Optional[Any]:




									'''simple docstring'''


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







def  UpperCAmelCase_   (_lowerCAmelCase :		Dict     ,			_lowerCAmelCase :		Optional[Any]		):
			#  To allow all pl args uncomment the following line
			#  parser = pl.Trainer.add_argparse_args(parser)
			parser.add_argument(
			    "--output_dir"     ,			default=_lowerCAmelCase     ,			type=_lowerCAmelCase     ,			required=_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=_lowerCAmelCase     ,			type=_lowerCAmelCase     ,			required=_lowerCAmelCase     ,			help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."     ,			)







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

			# init model
			__UpperCamelCase    :    Any								=      Path(model.hparams.output_dir		)
			odir.mkdir(exist_ok=_lowerCAmelCase		)

			# add custom checkpoints
			if checkpoint_callback is None:
						__UpperCamelCase    :    Optional[int]								=      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:
						__UpperCamelCase    :    Dict								=      LoggingCallback()

			__UpperCamelCase    :    str								=      {}

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

			if args.gpus > 1:
						__UpperCamelCase    :    int								=      "ddp"

			__UpperCamelCase    :    Dict								=      args.accumulate_grad_batches
			__UpperCamelCase    :    int								=      extra_train_kwargs.get("accelerator"     ,			_lowerCAmelCase		)
			__UpperCamelCase    :    List[str]								=      extra_train_kwargs.get("profiler"     ,			_lowerCAmelCase		)

			__UpperCamelCase    :    List[Any]								=      pl.Trainer.from_argparse_args(
			    _lowerCAmelCase     ,			weights_summary=_lowerCAmelCase     ,			callbacks=[logging_callback] + extra_callbacks     ,			logger=_lowerCAmelCase     ,			checkpoint_callback=_lowerCAmelCase     ,			**_lowerCAmelCase     ,			)

			if args.do_train:
						trainer.fit(_lowerCAmelCase		)

			return trainer


from __future__ import annotations







def  UpperCAmelCase_   (_lowerCAmelCase :		int     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		list[int]     ,			_lowerCAmelCase :		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(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
			else:
						__UpperCamelCase    :    Tuple								=      capacity - weights[counter - 1]
						__UpperCamelCase    :    List[str]								=      values[counter - 1] + knapsack(
						    _lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						__UpperCamelCase    :    Dict								=      knapsack(_lowerCAmelCase     ,			_lowerCAmelCase     ,			_lowerCAmelCase     ,			counter - 1		)
						return max(_lowerCAmelCase     ,			_lowerCAmelCase		)


if __name__ == "__main__":
					import doctest

					doctest.testmod()