import math
from timeit import timeit



def    lowerCamelCase_   (  _lowercase    )							->	int:
  __A							:   Optional[Any]	  =				0
  __A							:   int	  =				abs(_lowercase    )
  while True:
    __A							:   str	  =				n // 10
    digits += 1
    if n == 0:
      break
  return digits



def    lowerCamelCase_   (  _lowercase    )							->	int:
  return 1 if n == 0 else math.floor(math.log(abs(_lowercase    ) ,     10    ) + 1    )



def    lowerCamelCase_   (  _lowercase    )							->	int:
  return len(str(abs(_lowercase    )    )    )



def    lowerCamelCase_   (  )							->	None:
  from collections.abc import Callable

  def benchmark_a_function(_lowercase ,     _lowercase    ) -> None:
    __A							:   List[Any]	  =				F"{func.__name__}({value})"
    __A							:   int	  =				timeit(F"__main__.{call}" ,     setup="import __main__"    )
    print(F"{call}: {func(_lowercase    )} -- {timing} seconds"    )

  for value in (262_144, 1_125_899_906_842_624, 1_267_650_600_228_229_401_496_703_205_376):
    for func in (num_digits, num_digits_fast, num_digits_faster):
      benchmark_a_function(_lowercase ,     _lowercase    )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()






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


UpperCamelCase      			=     logging.getLogger(__name__)

require_version('pytorch_lightning>=1.0.4')

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


class _a       (	pl.LightningModule ):

	'''simple docstring'''



	def __init__(       self							,			__UpperCAmelCase							,			__UpperCAmelCase=None							,			__UpperCAmelCase="base"							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			__UpperCAmelCase=None							,			**__UpperCAmelCase							,			):
			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   )
			__A							:   str	  =				0
			__A							:   Dict	  =				Path(self.hparams.output_dir   )
			__A							:   Any	  =				self.hparams.cache_dir if self.hparams.cache_dir else None
			if config is None:
					__A							:   Union[str, 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=__UpperCAmelCase							,			**__UpperCAmelCase							,			)
			else:
					__A							:   PretrainedConfig	  =				config

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

	def 					__UpperCAmelCase(       self							,			*__UpperCAmelCase							,			**__UpperCAmelCase   ):
			__A							:   Union[str, Any]	  =				self.model_type.from_pretrained(*__UpperCAmelCase							,			**__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   Any	  =				arg_to_scheduler[self.hparams.lr_scheduler]
			__A							:   Union[str, Any]	  =				get_schedule_func(
			    self.opt							,			num_warmup_steps=self.hparams.warmup_steps							,			num_training_steps=self.total_steps()   )
			__A							:   Tuple	  =				{"scheduler": scheduler, "interval": "step", "frequency": 1}
			return scheduler

	def 					__UpperCAmelCase(       self   ):
			__A							:   int	  =				self.model
			__A							:   Optional[int]	  =				["bias", "LayerNorm.weight"]
			__A							:   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							:   Optional[int]	  =				Adafactor(
					    __UpperCAmelCase							,			lr=self.hparams.learning_rate							,			scale_parameter=__UpperCAmelCase							,			relative_step=__UpperCAmelCase   )

			else:
					__A							:   Tuple	  =				AdamW(
					    __UpperCAmelCase							,			lr=self.hparams.learning_rate							,			eps=self.hparams.adam_epsilon   )
			__A							:   Optional[Any]	  =				optimizer

			__A							:   str	  =				self.get_lr_scheduler()

			return [optimizer], [scheduler]

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			return self.validation_step(__UpperCAmelCase							,			__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			return self.validation_end(__UpperCAmelCase   )

	def 					__UpperCAmelCase(       self   ):
			__A							:   List[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 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			if mode == "test":
					__A							:   List[str]	  =				len(self.test_dataloader().dataset   )
			else:
					__A							:   List[str]	  =				self.get_dataloader("train"							,			self.hparams.train_batch_size							,			shuffle=__UpperCAmelCase   )
					__A							:   str	  =				len(self.train_dataloader().dataset   )

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase							,			__UpperCAmelCase = 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=__UpperCAmelCase   )

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

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			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 					__UpperCAmelCase(       self							,			__UpperCAmelCase   ):
			__A							:   List[Any]	  =				self.output_dir.joinpath("best_tfmr"   )
			__A							:   Union[str, Any]	  =				self.step_count
			self.model.save_pretrained(__UpperCAmelCase   )
			self.tokenizer.save_pretrained(__UpperCAmelCase   )







	@staticmethod
	def 					__UpperCAmelCase(       __UpperCAmelCase							,			__UpperCAmelCase   ):
			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 _a       (	pl.Callback ):

	'''simple docstring'''



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

	def 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			rank_zero_info("***** Validation results *****"   )
			__A							:   Any	  =				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 					__UpperCAmelCase(       self							,			__UpperCAmelCase							,			__UpperCAmelCase   ):
			rank_zero_info("***** Test results *****"   )
			__A							:   Dict	  =				trainer.callback_metrics
			# Log and save results to file
			__A							:   Tuple	  =				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    lowerCamelCase_   (  _lowercase ,     _lowercase    )							->	None:
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		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    lowerCamelCase_   (  _lowercase ,     _lowercase ,     _lowercase=None ,     _lowercase=True ,     _lowercase=[] ,     _lowercase=None ,     _lowercase=None ,     **_lowercase ,     )							->	Union[str, Any]:
		pl.seed_everything(args.seed    )

		# init model
		__A							:   int	  =				Path(model.hparams.output_dir    )
		odir.mkdir(exist_ok=_lowercase    )

		# add custom checkpoints
		if checkpoint_callback is None:
				__A							:   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(_lowercase    )
		if logging_callback is None:
				__A							:   int	  =				LoggingCallback()

		__A							:   List[str]	  =				{}

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

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

		__A							:   int	  =				args.accumulate_grad_batches
		__A							:   Dict	  =				extra_train_kwargs.get("accelerator" ,     _lowercase    )
		__A							:   Optional[int]	  =				extra_train_kwargs.get("profiler" ,     _lowercase    )

		__A							:   List[str]	  =				pl.Trainer.from_argparse_args(
		    _lowercase ,     weights_summary=_lowercase ,     callbacks=[logging_callback] + extra_callbacks ,     logger=_lowercase ,     checkpoint_callback=_lowercase ,     **_lowercase ,     )

		if args.do_train:
				trainer.fit(_lowercase    )

		return trainer





