


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       : Union[str, Any] ,						lowerCAmelCase_       : argparse.Namespace ,						lowerCAmelCase_       : Dict=None ,						lowerCAmelCase_       : Tuple="base" ,						lowerCAmelCase_       : str=None ,						lowerCAmelCase_       : List[Any]=None ,						lowerCAmelCase_       : List[str]=None ,						**lowerCAmelCase_       : Optional[Any] ,						)			->		List[Any]:
						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=  0
						__A=  Path(self.hparams.output_dir  )
						__A=  self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
									__A=  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=  config

						__A=  ('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=  AutoTokenizer.from_pretrained(
									    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path ,						cache_dir=UpperCAmelCase_ ,						)
						else:
									__A=  tokenizer
						__A=  MODEL_MODES[mode]
						if model is None:
									__A=  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=  model
			def 				lowerCAmelCase    (     self       : Tuple ,						*lowerCAmelCase_       : Optional[Any] ,						**lowerCAmelCase_       : Any  )			->		Dict:
						__A=  self.model_type.from_pretrained(*UpperCAmelCase_ ,						**UpperCAmelCase_  )
			def 				lowerCAmelCase    (     self       : str  )			->		int:
						__A=  arg_to_scheduler[self.hparams.lr_scheduler]
						__A=  get_schedule_func(
						    self.opt ,						num_warmup_steps=self.hparams.warmup_steps ,						num_training_steps=self.total_steps()  )
						__A=  {'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
						return scheduler
			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Any:
						__A=  self.model
						__A=  ['bias', 'LayerNorm.weight']
						__A=  [
						    {
						        '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=  Adafactor(
									    UpperCAmelCase_ ,						lr=self.hparams.learning_rate ,						scale_parameter=UpperCAmelCase_ ,						relative_step=UpperCAmelCase_  )

						else:
									__A=  AdamW(
									    UpperCAmelCase_ ,						lr=self.hparams.learning_rate ,						eps=self.hparams.adam_epsilon  )
						__A=  optimizer

						__A=  self.get_lr_scheduler()

						return [optimizer], [scheduler]
			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : str  )			->		Tuple:
						return self.validation_step(UpperCAmelCase_ ,						UpperCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		List[str]:
						return self.validation_end(UpperCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Optional[Any]:
						__A=  max(1 ,						self.hparams.gpus  )  # TODO: consider num_tpu_cores
						__A=  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       : Tuple ,						lowerCAmelCase_       : Union[str, Any]  )			->		int:
						if mode == "test":
									__A=  len(self.test_dataloader().dataset  )
						else:
									__A=  self.get_dataloader('train' ,						self.hparams.train_batch_size ,						shuffle=UpperCAmelCase_  )
									__A=  len(self.train_dataloader().dataset  )
			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : bool = False  )			->		List[str]:
						raise NotImplementedError('You must implement this for your task'  )
			def 				lowerCAmelCase    (     self       : Optional[int]  )			->		int:
						return self.train_loader
			def 				lowerCAmelCase    (     self       : int  )			->		Any:
						return self.get_dataloader('dev' ,						self.hparams.eval_batch_size ,						shuffle=UpperCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		Optional[Any]:
						return self.get_dataloader('test' ,						self.hparams.eval_batch_size ,						shuffle=UpperCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Dict  )			->		str:
						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       : Optional[Any] ,						lowerCAmelCase_       : Dict[str, Any]  )			->		str:
						__A=  self.output_dir.joinpath('best_tfmr'  )
						__A=  self.step_count
						self.model.save_pretrained(UpperCAmelCase_  )
						self.tokenizer.save_pretrained(UpperCAmelCase_  )

			@staticmethod
			def 				lowerCAmelCase    (     lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any  )			->		Any:
						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 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Optional[Any]  )			->		Tuple:
						__A=  trainer.lr_schedulers[0]['scheduler']
						__A=  {F"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()  )}
						pl_module.logger.log_metrics(UpperCAmelCase_  )
			def 				lowerCAmelCase    (     self       : Dict ,						lowerCAmelCase_       : pl.Trainer ,						lowerCAmelCase_       : pl.LightningModule  )			->		Optional[Any]:
						rank_zero_info('***** Validation results *****'  )
						__A=  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       : Dict ,						lowerCAmelCase_       : pl.Trainer ,						lowerCAmelCase_       : pl.LightningModule  )			->		str:
						rank_zero_info('***** Test results *****'  )
						__A=  trainer.callback_metrics
						# Log and save results to file
						__A=  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__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[Any]   ):






			"""simple docstring"""




			parser.add_argument(
			    '--output_dir',default=_UpperCAmelCase,type=_UpperCAmelCase,required=_UpperCAmelCase,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=_UpperCAmelCase,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=_UpperCAmelCase   )
			parser.add_argument('--max_grad_norm',dest='gradient_clip_val',default=1.0,type=_UpperCAmelCase,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=_UpperCAmelCase,default=1,help='Number of updates steps to accumulate before performing a backward/update pass.',)
			parser.add_argument('--seed',type=_UpperCAmelCase,default=42,help='random seed for initialization'   )
			parser.add_argument(
			    '--data_dir',default=_UpperCAmelCase,type=_UpperCAmelCase,required=_UpperCAmelCase,help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.',)






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[Any],_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   str=None,_SCREAMING_SNAKE_CASE				:   Tuple=True,_SCREAMING_SNAKE_CASE				:   Optional[Any]=[],_SCREAMING_SNAKE_CASE				:   Any=None,_SCREAMING_SNAKE_CASE				:   int=None,**_SCREAMING_SNAKE_CASE				:   str,):






			"""simple docstring"""




			pl.seed_everything(args.seed   )

			# init model
			__A=  Path(model.hparams.output_dir   )
			odir.mkdir(exist_ok=_UpperCAmelCase   )

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

			__A=  {}

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

			if args.gpus > 1:
						__A=  'ddp'

			__A=  args.accumulate_grad_batches
			__A=  extra_train_kwargs.get('accelerator',_UpperCAmelCase   )
			__A=  extra_train_kwargs.get('profiler',_UpperCAmelCase   )

			__A=  pl.Trainer.from_argparse_args(
			    _UpperCAmelCase,weights_summary=_UpperCAmelCase,callbacks=[logging_callback] + extra_callbacks,logger=_UpperCAmelCase,checkpoint_callback=_UpperCAmelCase,**_UpperCAmelCase,)

			if args.do_train:
						trainer.fit(_UpperCAmelCase   )

			return trainer







'''simple docstring'''





from typing import Any
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Any  )			->		int:
						__A=  []
						__A=  0
			def __str__(     self       : Tuple  )			->		Dict:
						__A=  '<' + str(self.stack  )[1:-1] + '>'
						return printed
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
						self.stack.append(lowerCAmelCase_  )
						__A=  self.length + 1
			def 				lowerCAmelCase    (     self       : int  )			->		Any:
						self.rotate(1  )
						__A=  self.stack[self.length - 1]
						__A=  self.stack[:-1]
						self.rotate(self.length - 1  )
						__A=  self.length - 1
						return dequeued
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
						for _ in range(lowerCAmelCase_  ):
									__A=  self.stack[0]
									__A=  self.stack[1:]
									self.put(lowerCAmelCase_  )
									__A=  self.length - 1
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
						__A=  self.get()
						self.put(lowerCAmelCase_  )
						self.rotate(self.length - 1  )
						return front
			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
						return self.length



