


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


__magic_name__       					=      logging.getLogger(__name__)

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

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





class    lowerCAmelCase__				(			pl.LightningModule       ):






						"""simple docstring"""




						def __init__(   self				,							a_				,							a_=None				,							a_="base"				,							a_=None				,							a_=None				,							a_=None				,							**a_				,							):
							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_     )
							lowerCamelCase_			:							str      =       0
							lowerCamelCase_			:							Tuple      =       Path(self.hparams.output_dir     )
							lowerCamelCase_			:							str      =       self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
								lowerCamelCase_			:							Any      =       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:
								lowerCamelCase_			:							PretrainedConfig      =       config

							lowerCamelCase_			:							Union[str, Any]      =       ("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:
								lowerCamelCase_			:							Union[str, Any]      =       AutoTokenizer.from_pretrained(
								    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path				,							cache_dir=a_				,							)
							else:
								lowerCamelCase_			:							PreTrainedTokenizer      =       tokenizer
							lowerCamelCase_			:							int      =       MODEL_MODES[mode]
							if model is None:
								lowerCamelCase_			:							List[str]      =       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:
								lowerCamelCase_			:							List[str]      =       model




						def 					_UpperCamelCase			(   self				,							*a_				,							**a_     ):
							lowerCamelCase_			:							Dict      =       self.model_type.from_pretrained(*a_				,							**a_     )




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




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							Tuple      =       self.model
							lowerCamelCase_			:							Optional[Any]      =       ["bias", "LayerNorm.weight"]
							lowerCamelCase_			:							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:
								lowerCamelCase_			:							Dict      =       Adafactor(
								    a_				,							lr=self.hparams.learning_rate				,							scale_parameter=a_				,							relative_step=a_     )

							else:
								lowerCamelCase_			:							Union[str, Any]      =       AdamW(
								    a_				,							lr=self.hparams.learning_rate				,							eps=self.hparams.adam_epsilon     )
							lowerCamelCase_			:							Tuple      =       optimizer

							lowerCamelCase_			:							Optional[int]      =       self.get_lr_scheduler()

							return [optimizer], [scheduler]




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							return self.validation_step(a_				,							a_     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							return self.validation_end(a_     )




						def 					_UpperCamelCase			(   self     ):
							lowerCamelCase_			:							List[str]      =       max(1				,							self.hparams.gpus     )  # TODO: consider num_tpu_cores
							lowerCamelCase_			:							Optional[Any]      =       self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
							return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs




						def 					_UpperCamelCase			(   self				,							a_     ):
							if mode == "test":
								lowerCamelCase_			:							Any      =       len(self.test_dataloader().dataset     )
							else:
								lowerCamelCase_			:							Optional[Any]      =       self.get_dataloader("train"				,							self.hparams.train_batch_size				,							shuffle=a_     )
								lowerCamelCase_			:							List[Any]      =       len(self.train_dataloader().dataset     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_				,							a_ = False     ):
							raise NotImplementedError("You must implement this for your task"     )




						def 					_UpperCamelCase			(   self     ):
							return self.train_loader




						def 					_UpperCamelCase			(   self     ):
							return self.get_dataloader("dev"				,							self.hparams.eval_batch_size				,							shuffle=a_     )




						def 					_UpperCamelCase			(   self     ):
							return self.get_dataloader("test"				,							self.hparams.eval_batch_size				,							shuffle=a_     )




						def 					_UpperCamelCase			(   self				,							a_     ):
							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 					_UpperCamelCase			(   self				,							a_     ):
							lowerCamelCase_			:							Optional[Any]      =       self.output_dir.joinpath("best_tfmr"     )
							lowerCamelCase_			:							Dict      =       self.step_count
							self.model.save_pretrained(a_     )
							self.tokenizer.save_pretrained(a_     )




						@staticmethod
						def 					_UpperCamelCase			(   a_				,							a_     ):
							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       ):






						"""simple docstring"""




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							lowerCamelCase_			:							List[Any]      =       trainer.lr_schedulers[0]["scheduler"]
							lowerCamelCase_			:							Any      =       {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()     )}
							pl_module.logger.log_metrics(a_     )




						def 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							rank_zero_info("***** Validation results *****"     )
							lowerCamelCase_			:							int      =       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 					_UpperCamelCase			(   self				,							a_				,							a_     ):
							rank_zero_info("***** Test results *****"     )
							lowerCamelCase_			:							str      =       trainer.callback_metrics
							# Log and save results to file
							lowerCamelCase_			:							Any      =       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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_):
	'''simple docstring'''


	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     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_      ,					lowerCAmelCase_=None      ,					lowerCAmelCase_=True      ,					lowerCAmelCase_=[]      ,					lowerCAmelCase_=None      ,					lowerCAmelCase_=None      ,					**lowerCAmelCase_      ,					):
	'''simple docstring'''


	pl.seed_everything(args.seed)

	# init model
	lowerCamelCase_			:							Optional[Any]      =       Path(model.hparams.output_dir)
	odir.mkdir(exist_ok=lowerCAmelCase_)

	# add custom checkpoints
	if checkpoint_callback is None:
		lowerCamelCase_			:							Any      =       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:
		lowerCamelCase_			:							List[str]      =       LoggingCallback()

	lowerCamelCase_			:							Dict      =       {}

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

	if args.gpus > 1:
		lowerCamelCase_			:							Union[str, Any]      =       "ddp"

	lowerCamelCase_			:							Any      =       args.accumulate_grad_batches
	lowerCamelCase_			:							int      =       extra_train_kwargs.get("accelerator"      ,					lowerCAmelCase_)
	lowerCamelCase_			:							Tuple      =       extra_train_kwargs.get("profiler"      ,					lowerCAmelCase_)

	lowerCamelCase_			:							Dict      =       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





__magic_name__       					=      {
    '''0''': 1,
    '''1''': 1,
    '''2''': 2,
    '''3''': 6,
    '''4''': 2_4,
    '''5''': 1_2_0,
    '''6''': 7_2_0,
    '''7''': 5_0_4_0,
    '''8''': 4_0_3_2_0,
    '''9''': 3_6_2_8_8_0,
}

__magic_name__       					=      {1_4_5: 1_4_5}

__magic_name__       					=      {
    1_4_5: 0,
    1_6_9: 3,
    3_6_3_0_1: 3,
    1_4_5_4: 3,
    8_7_1: 2,
    4_5_3_6_1: 2,
    8_7_2: 2,
}







def     __magic_name__  (      lowerCAmelCase_):
	'''simple docstring'''


	if n in CACHE_SUM_DIGIT_FACTORIALS:
		return CACHE_SUM_DIGIT_FACTORIALS[n]
	lowerCamelCase_			:							List[str]      =       sum(DIGIT_FACTORIALS[let] for let in str(lowerCAmelCase_))
	lowerCamelCase_			:							List[str]      =       ret
	return ret







def     __magic_name__  (      lowerCAmelCase_      ,					lowerCAmelCase_ = None):
	'''simple docstring'''


	lowerCamelCase_			:							List[str]      =       previous or set()
	if n in CHAIN_LENGTH_CACHE:
		return CHAIN_LENGTH_CACHE[n]
	lowerCamelCase_			:							str      =       sum_digit_factorials(lowerCAmelCase_)
	if next_number in previous:
		lowerCamelCase_			:							List[str]      =       0
		return 0
	else:
		previous.add(lowerCAmelCase_)
		lowerCamelCase_			:							List[Any]      =       1 + chain_length(lowerCAmelCase_      ,					lowerCAmelCase_)
		lowerCamelCase_			:							Optional[int]      =       ret
		return ret







def     __magic_name__  (      lowerCAmelCase_ = 60      ,					lowerCAmelCase_ = 100_0000):
	'''simple docstring'''


	return sum(1 for i in range(1      ,					lowerCAmelCase_) if chain_length(lowerCAmelCase_) == num_terms)


if __name__ == "__main__":
							print(f'''{solution() = }''')

