



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	:   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
_lowercase	:   List[str]    	={
    "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	:   Union[str, Any]    	=sorted(arg_to_scheduler.keys())
_lowercase	:   List[str]    	="{" + ", ".join(arg_to_scheduler_choices) + "}"

class      snake_case__       (pl.LightningModule	):







						"""simple docstring"""




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

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

									if tokenizer is None:
												a__     :		Dict		 =						AutoTokenizer.from_pretrained(
												    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path      ,     cache_dir=__lowercase      ,     )
									else:
												a__     :		PreTrainedTokenizer		 =						tokenizer
									a__     :		int		 =						MODEL_MODES[mode]
									if model is None:
												a__     :		Optional[int]		 =						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=__lowercase      ,     )
									else:
												a__     :		str		 =						model

						def 							SCREAMING_SNAKE_CASE__(     self      ,     *__lowercase      ,     **__lowercase						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		Union[str, Any]		 =						self.model_type.from_pretrained(*__lowercase      ,     **__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									a__     :		Dict		 =						self.model
									a__     :		Tuple		 =						["""bias""", """LayerNorm.weight"""]
									a__     :		List[str]		 =						[
									    {
									        """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__     :		Tuple		 =						Adafactor(
												    __lowercase      ,     lr=self.hparams.learning_rate      ,     scale_parameter=__lowercase      ,     relative_step=__lowercase						)

									else:
												a__     :		int		 =						AdamW(
												    __lowercase      ,     lr=self.hparams.learning_rate      ,     eps=self.hparams.adam_epsilon						)
									a__     :		List[str]		 =						optimizer

									a__     :		Dict		 =						self.get_lr_scheduler()

									return [optimizer], [scheduler]

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   Optional[Any]:
									"""simple docstring"""




									return self.validation_step(__lowercase      ,     __lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   List[str]:
									"""simple docstring"""




									return self.validation_end(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   Any:
									"""simple docstring"""




									if stage == "test":
												a__     :		List[str]		 =						len(self.test_dataloader().dataset						)
									else:
												a__     :		Union[str, Any]		 =						self.get_dataloader("""train"""      ,     self.hparams.train_batch_size      ,     shuffle=__lowercase						)
												a__     :		Dict		 =						len(self.train_dataloader().dataset						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase      ,     __lowercase = False						)      ->   List[str]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
									"""simple docstring"""




									return self.train_loader

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   int:
									"""simple docstring"""




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

						@pl.utilities.rank_zero_only
						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase						)      ->   None:
									"""simple docstring"""




									a__     :		Dict		 =						self.output_dir.joinpath("""best_tfmr"""						)
									a__     :		Optional[int]		 =						self.step_count
									self.model.save_pretrained(__lowercase						)
									self.tokenizer.save_pretrained(__lowercase						)

						@staticmethod
						def 							SCREAMING_SNAKE_CASE__(     __lowercase      ,     __lowercase						)      ->   List[Any]:
									"""simple docstring"""




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




class      snake_case__       (pl.Callback	):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   Any:
									"""simple docstring"""




									if (
									    trainer.is_global_zero and trainer.global_rank == 0
									):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
												pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.




class      snake_case__       (pl.Callback	):







						"""simple docstring"""




						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   Optional[int]:
									"""simple docstring"""




									a__     :		Dict		 =						trainer.lr_schedulers[0]["""scheduler"""]
									a__     :		Tuple		 =						{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()						)}
									pl_module.logger.log_metrics(__lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   int:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(     self      ,     __lowercase      ,     __lowercase						)      ->   str:
									"""simple docstring"""




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




def 			__A  (						_lowercase       :     int		,     _lowercase       :     Union[str, Any])							-> 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       :     BaseTransformer		,     _lowercase       :     argparse.Namespace		,     _lowercase       :     Tuple=None		,     _lowercase       :     int=True		,     _lowercase       :     List[Any]=None		,     _lowercase       :     Tuple=[]		,     _lowercase       :     Any=None		,     _lowercase       :     Any=None		,     **_lowercase       :     Tuple		,     )							-> List[Any]:







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

			# init model
			a__     :		Optional[int]		 =						Path(model.hparams.output_dir)
			odir.mkdir(exist_ok=_lowercase)

			# add custom checkpoints
			if checkpoint_callback is None:
						a__     :		Tuple		 =						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:
						a__     :		Union[str, Any]		 =						LoggingCallback()

			a__     :		Dict		 =						{}

			# TODO: remove with PyTorch 1.6 since pl uses native amp
			if args.fpaa:
						a__     :		str		 =						16
						# train_params["amp_level"] = args.fp16_opt_level

			if args.gpus > 1:
						a__     :		List[str]		 =						"""auto"""  # "ddp"
						a__     :		Tuple		 =						"""ddp"""

			a__     :		str		 =						args.accumulate_grad_batches
			a__     :		List[str]		 =						None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
			a__     :		Optional[int]		 =						"""auto"""

			a__     :		int		 =						pl.Trainer.from_argparse_args(
			    _lowercase		,     weights_summary=_lowercase		,     callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]		,     logger=_lowercase		,     **_lowercase		,     )

			if args.do_train:
						trainer.fit(_lowercase)

			return trainer









import decimal

def 			lowerCAmelCase_  (						)							-> int:







			"""simple docstring"""
			a__     :		Optional[Any]		 =						0
			a__     :		Optional[int]		 =						decimal.Context(prec=105)
			for i in range(2		,     100):
						a__     :		int		 =						decimal.Decimal(_lowercase)
						a__     :		Optional[int]		 =						number.sqrt(_lowercase)
						if len(str(_lowercase)) > 1:
									answer += int(str(_lowercase)[0])
									a__     :		Tuple		 =						str(_lowercase)[2:101]
									answer += sum(int(_lowercase) for x in sqrt_number_str)
			return answer


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f'{solution() = }')




