

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   __lowercase				(pl.LightningModule			):
	def __init__(							self				,							A_				,							A_=None				,							A_="base"				,							A_=None				,							A_=None				,							A_=None				,							**A_				,							) ->List[Any]:



							'''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(A_ )
							__lowerCAmelCase :						int = 0
							__lowerCAmelCase :						List[str] = Path(self.hparams.output_dir )
							__lowerCAmelCase :						int = self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
													__lowerCAmelCase :						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=A_				,							**A_				,							)
							else:
													__lowerCAmelCase :						str = config

							__lowerCAmelCase :						List[str] = ('''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 :						Optional[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 :						List[str] = tokenizer
							__lowerCAmelCase :						Any = MODEL_MODES[mode]
							if model is None:
													__lowerCAmelCase :						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=A_				,							)
							else:
													__lowerCAmelCase :						List[str] = model
	def      UpperCamelCase__  (							self				,							*A_				,							**A_ ) ->Tuple:



							'''simple docstring'''





							__lowerCAmelCase :						Union[str, Any] = self.model_type.from_pretrained(*A_				,							**A_ )
	def      UpperCamelCase__  (							self ) ->Optional[Any]:



							'''simple docstring'''





							__lowerCAmelCase :						int = 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 :						List[str] = {'''scheduler''': scheduler, '''interval''': '''step''', '''frequency''': 1}
							return scheduler
	def      UpperCamelCase__  (							self ) ->int:



							'''simple docstring'''





							__lowerCAmelCase :						Optional[Any] = self.model
							__lowerCAmelCase :						Optional[int] = ['''bias''', '''LayerNorm.weight''']
							__lowerCAmelCase :						Optional[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 :						Any = Adafactor(
													    A_				,							lr=self.hparams.learning_rate				,							scale_parameter=A_				,							relative_step=A_ )

							else:
													__lowerCAmelCase :						Optional[Any] = AdamW(
													    A_				,							lr=self.hparams.learning_rate				,							eps=self.hparams.adam_epsilon )
							__lowerCAmelCase :						int = optimizer

							__lowerCAmelCase :						Any = self.get_lr_scheduler()

							return [optimizer], [scheduler]
	def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->int:



							'''simple docstring'''





							return self.validation_step(A_				,							A_ )
	def      UpperCamelCase__  (							self				,							A_ ) ->str:



							'''simple docstring'''





							return self.validation_end(A_ )
	def      UpperCamelCase__  (							self ) ->int:



							'''simple docstring'''





							__lowerCAmelCase :						Optional[int] = max(1				,							self.hparams.gpus )  # TODO: consider num_tpu_cores
							__lowerCAmelCase :						str = 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_ ) ->Any:



							'''simple docstring'''





							if mode == "test":
													__lowerCAmelCase :						Optional[int] = len(self.test_dataloader().dataset )
							else:
													__lowerCAmelCase :						List[Any] = self.get_dataloader('''train'''				,							self.hparams.train_batch_size				,							shuffle=A_ )
													__lowerCAmelCase :						Dict = len(self.train_dataloader().dataset )
	def      UpperCamelCase__  (							self				,							A_				,							A_				,							A_ = False ) ->Tuple:



							'''simple docstring'''





							raise NotImplementedError('''You must implement this for your task''' )
	def      UpperCamelCase__  (							self ) ->Dict:



							'''simple docstring'''





							return self.train_loader
	def      UpperCamelCase__  (							self ) ->Optional[Any]:



							'''simple docstring'''





							return self.get_dataloader('''dev'''				,							self.hparams.eval_batch_size				,							shuffle=A_ )
	def      UpperCamelCase__  (							self ) ->Optional[Any]:



							'''simple docstring'''





							return self.get_dataloader('''test'''				,							self.hparams.eval_batch_size				,							shuffle=A_ )
	def      UpperCamelCase__  (							self				,							A_ ) ->Optional[Any]:



							'''simple docstring'''





							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_ ) ->None:



							'''simple docstring'''





							__lowerCAmelCase :						Union[str, Any] = self.output_dir.joinpath('''best_tfmr''' )
							__lowerCAmelCase :						List[Any] = self.step_count
							self.model.save_pretrained(A_ )
							self.tokenizer.save_pretrained(A_ )





	@staticmethod
	def      UpperCamelCase__  (							A_				,							A_ ) ->Union[str, Any]:



							'''simple docstring'''





							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   __lowercase				(pl.Callback			):
	def      UpperCamelCase__  (							self				,							A_				,							A_ ) ->str:



							'''simple docstring'''





							__lowerCAmelCase :						int = trainer.lr_schedulers[0]['''scheduler''']
							__lowerCAmelCase :						List[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_ ) ->str:



							'''simple docstring'''





							rank_zero_info('''***** Validation results *****''' )
							__lowerCAmelCase :						Optional[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_ ) ->Tuple:



							'''simple docstring'''





							rank_zero_info('''***** Test results *****''' )
							__lowerCAmelCase :						List[str] = trainer.callback_metrics
							# Log and save results to file
							__lowerCAmelCase :						Dict = 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 		_lowercase			(					lowercase__       ,     lowercase__						):
						parser.add_argument(
						    '''--output_dir'''       ,     default=SCREAMING_SNAKE_CASE_       ,     type=SCREAMING_SNAKE_CASE_       ,     required=SCREAMING_SNAKE_CASE_       ,     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=SCREAMING_SNAKE_CASE_       ,     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=SCREAMING_SNAKE_CASE_						)
						parser.add_argument('''--max_grad_norm'''       ,     dest='''gradient_clip_val'''       ,     default=1.0       ,     type=SCREAMING_SNAKE_CASE_       ,     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=SCREAMING_SNAKE_CASE_       ,     default=1       ,     help='''Number of updates steps to accumulate before performing a backward/update pass.'''       ,     )
						parser.add_argument('''--seed'''       ,     type=SCREAMING_SNAKE_CASE_       ,     default=4_2       ,     help='''random seed for initialization'''						)
						parser.add_argument(
						    '''--data_dir'''       ,     default=SCREAMING_SNAKE_CASE_       ,     type=SCREAMING_SNAKE_CASE_       ,     required=SCREAMING_SNAKE_CASE_       ,     help='''The input data dir. Should contain the training files for the CoNLL-2003 NER task.'''       ,     )







def 		_lowercase			(					lowercase__       ,     lowercase__       ,     lowercase__=None       ,     lowercase__=True       ,     lowercase__=[]       ,     lowercase__=None       ,     lowercase__=None       ,     **lowercase__       ,     ):
						pl.seed_everything(args.seed						)

						# init model
						__lowerCAmelCase :						Union[str, Any] = Path(model.hparams.output_dir						)
						odir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_						)

						# add custom checkpoints
						if checkpoint_callback is None:
												__lowerCAmelCase :						str = 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(SCREAMING_SNAKE_CASE_						)
						if logging_callback is None:
												__lowerCAmelCase :						Tuple = LoggingCallback()

						__lowerCAmelCase :						Tuple = {}

						# TODO: remove with PyTorch 1.6 since pl uses native amp
						if args.fpaa:
												__lowerCAmelCase :						Tuple = 1_6
												__lowerCAmelCase :						Any = args.fpaa_opt_level

						if args.gpus > 1:
												__lowerCAmelCase :						Union[str, Any] = '''ddp'''

						__lowerCAmelCase :						Dict = args.accumulate_grad_batches
						__lowerCAmelCase :						str = extra_train_kwargs.get('''accelerator'''       ,     SCREAMING_SNAKE_CASE_						)
						__lowerCAmelCase :						Dict = extra_train_kwargs.get('''profiler'''       ,     SCREAMING_SNAKE_CASE_						)

						__lowerCAmelCase :						Dict = pl.Trainer.from_argparse_args(
						    SCREAMING_SNAKE_CASE_       ,     weights_summary=SCREAMING_SNAKE_CASE_       ,     callbacks=[logging_callback] + extra_callbacks       ,     logger=SCREAMING_SNAKE_CASE_       ,     checkpoint_callback=SCREAMING_SNAKE_CASE_       ,     **SCREAMING_SNAKE_CASE_       ,     )

						if args.do_train:
												trainer.fit(SCREAMING_SNAKE_CASE_						)

						return trainer





def 		_lowercase			(					lowercase__       ,     lowercase__						):
      if len(str(lowercase__						)						) == 1 or len(str(lowercase__						)						) == 1:
            return a * b

      __lowerCAmelCase :						Any = max(len(str(lowercase__						)						)       ,     len(str(lowercase__						)						)						)
      __lowerCAmelCase :						Optional[int] = ma // 2

      __lowerCAmelCase,							__lowerCAmelCase :						Tuple = divmod(lowercase__       ,     1_0**ma						)
      __lowerCAmelCase,							__lowerCAmelCase :						Any = divmod(lowercase__       ,     1_0**ma						)

      __lowerCAmelCase :						str = karatsuba(lowercase__       ,     lowercase__						)
      __lowerCAmelCase :						Optional[int] = karatsuba((aa + aa)       ,     (ba + ba)						)
      __lowerCAmelCase :						List[Any] = karatsuba(lowercase__       ,     lowercase__						)

      return (z * 1_0 ** (2 * ma)) + ((y - z - x) * 1_0 ** (ma)) + (x)







def 		_lowercase			(					):
      print(karatsuba(1_5_4_6_3       ,     2_3_4_8_9						)						)


if __name__ == "__main__":
 main()


