




'''simple docstring'''




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


snake_case__ 							=    logging.getLogger(__name__)

require_version("""pytorch_lightning>=1.0.4""")

snake_case__ 							=    {
    """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
snake_case__ 							=    {
    """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
}
snake_case__ 							=    sorted(arg_to_scheduler.keys())
snake_case__ 							=    """{""" + """, """.join(arg_to_scheduler_choices) + """}"""






class      UpperCamelCase_  (pl.LightningModule  ):







						"""simple docstring"""


						def __init__(  self   :							List[Any]				,			_lowerCamelCase   :							List[Any]				,			_lowerCamelCase   :							Union[str, Any]=None				,			_lowerCamelCase   :							int="base"				,			_lowerCamelCase   :							Any=None				,			_lowerCamelCase   :							Union[str, Any]=None				,			_lowerCamelCase   :							Dict=None				,			**_lowerCamelCase   :							Optional[int]				,			):





											"""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(lowerCAmelCase_     )
											A_			:  Any			=				0
											A_			:  Dict			=				Path(self.hparams.output_dir     )
											A_			:  str			=				self.hparams.cache_dir if self.hparams.cache_dir else None
											if config is None:
																A_			:  int			=				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=lowerCAmelCase_				,			**lowerCAmelCase_				,			)
											else:
																A_			:  Union[str, Any]			=				config

											A_			:  Dict			=				('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
											for p in extra_model_params:
																if getattr(self.hparams				,			lowerCAmelCase_				,			lowerCAmelCase_     ):
																					assert hasattr(self.config				,			lowerCAmelCase_     ), f'model config doesn\'t have a `{p}` attribute'
																					setattr(self.config				,			lowerCAmelCase_				,			getattr(self.hparams				,			lowerCAmelCase_     )     )

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







						def    _a     (  self   :							Optional[int]				,			*_lowerCamelCase   :							Dict				,			**_lowerCamelCase   :							Any     ):





											"""simple docstring"""







											A_			:  List[str]			=				self.model_type.from_pretrained(*lowerCAmelCase_				,			**lowerCAmelCase_     )







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







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







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											A_			:  Union[str, Any]			=				self.model
											A_			:  Any			=				['''bias''', '''LayerNorm.weight''']
											A_			:  List[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:
																A_			:  str			=				Adafactor(
																    lowerCAmelCase_				,			lr=self.hparams.learning_rate				,			scale_parameter=lowerCAmelCase_				,			relative_step=lowerCAmelCase_     )

											else:
																A_			:  List[Any]			=				AdamW(
																    lowerCAmelCase_				,			lr=self.hparams.learning_rate				,			eps=self.hparams.adam_epsilon     )
											A_			:  str			=				optimizer

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

											return [optimizer], [scheduler]







						def    _a     (  self   :							Optional[int]				,			_lowerCamelCase   :							Optional[Any]				,			_lowerCamelCase   :							Any     ):





											"""simple docstring"""







											return self.validation_step(lowerCAmelCase_				,			lowerCAmelCase_     )







						def    _a     (  self   :							str				,			_lowerCamelCase   :							str     ):





											"""simple docstring"""







											return self.validation_end(lowerCAmelCase_     )







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				max(1				,			self.hparams.gpus     )  # TODO: consider num_tpu_cores
											A_			:  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    _a     (  self   :							str				,			_lowerCamelCase   :							str     ):





											"""simple docstring"""







											if mode == "test":
																A_			:  int			=				len(self.test_dataloader().dataset     )
											else:
																A_			:  Optional[int]			=				self.get_dataloader('''train'''				,			self.hparams.train_batch_size				,			shuffle=lowerCAmelCase_     )
																A_			:  Tuple			=				len(self.train_dataloader().dataset     )







						def    _a     (  self   :							str				,			_lowerCamelCase   :							List[Any]				,			_lowerCamelCase   :							str				,			_lowerCamelCase   :							Optional[int] = False     ):





											"""simple docstring"""







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







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											return self.train_loader







						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											return self.get_dataloader('''dev'''				,			self.hparams.eval_batch_size				,			shuffle=lowerCAmelCase_     )







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											return self.get_dataloader('''test'''				,			self.hparams.eval_batch_size				,			shuffle=lowerCAmelCase_     )







						def    _a     (  self   :							Any				,			_lowerCamelCase   :							Any     ):





											"""simple docstring"""







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







						@pl.utilities.rank_zero_only
						def    _a     (  self   :							Optional[int]				,			_lowerCamelCase   :							int     ):





											"""simple docstring"""







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







						@staticmethod
						def    _a     (  _lowerCamelCase   :							Optional[Any]				,			_lowerCamelCase   :							int     ):





											"""simple docstring"""







											parser.add_argument(
											    '''--model_name_or_path'''				,			default=lowerCAmelCase_				,			type=lowerCAmelCase_				,			required=lowerCAmelCase_				,			help='''Path to pretrained model or model identifier from huggingface.co/models'''				,			)
											parser.add_argument(
											    '''--config_name'''				,			default=''''''				,			type=lowerCAmelCase_				,			help='''Pretrained config name or path if not the same as model_name'''     )
											parser.add_argument(
											    '''--tokenizer_name'''				,			default=lowerCAmelCase_				,			type=lowerCAmelCase_				,			help='''Pretrained tokenizer name or path if not the same as model_name'''				,			)
											parser.add_argument(
											    '''--cache_dir'''				,			default=''''''				,			type=lowerCAmelCase_				,			help='''Where do you want to store the pre-trained models downloaded from huggingface.co'''				,			)
											parser.add_argument(
											    '''--encoder_layerdrop'''				,			type=lowerCAmelCase_				,			help='''Encoder layer dropout probability (Optional). Goes into model.config'''				,			)
											parser.add_argument(
											    '''--decoder_layerdrop'''				,			type=lowerCAmelCase_				,			help='''Decoder layer dropout probability (Optional). Goes into model.config'''				,			)
											parser.add_argument(
											    '''--dropout'''				,			type=lowerCAmelCase_				,			help='''Dropout probability (Optional). Goes into model.config'''				,			)
											parser.add_argument(
											    '''--attention_dropout'''				,			type=lowerCAmelCase_				,			help='''Attention dropout probability (Optional). Goes into model.config'''				,			)
											parser.add_argument('''--learning_rate'''				,			default=5E-5				,			type=lowerCAmelCase_				,			help='''The initial learning rate for Adam.'''     )
											parser.add_argument(
											    '''--lr_scheduler'''				,			default='''linear'''				,			choices=lowerCAmelCase_				,			metavar=lowerCAmelCase_				,			type=lowerCAmelCase_				,			help='''Learning rate scheduler'''				,			)
											parser.add_argument('''--weight_decay'''				,			default=0.0				,			type=lowerCAmelCase_				,			help='''Weight decay if we apply some.'''     )
											parser.add_argument('''--adam_epsilon'''				,			default=1E-8				,			type=lowerCAmelCase_				,			help='''Epsilon for Adam optimizer.'''     )
											parser.add_argument('''--warmup_steps'''				,			default=0				,			type=lowerCAmelCase_				,			help='''Linear warmup over warmup_steps.'''     )
											parser.add_argument('''--num_workers'''				,			default=4				,			type=lowerCAmelCase_				,			help='''kwarg passed to DataLoader'''     )
											parser.add_argument('''--num_train_epochs'''				,			dest='''max_epochs'''				,			default=3				,			type=lowerCAmelCase_     )
											parser.add_argument('''--train_batch_size'''				,			default=32				,			type=lowerCAmelCase_     )
											parser.add_argument('''--eval_batch_size'''				,			default=32				,			type=lowerCAmelCase_     )
											parser.add_argument('''--adafactor'''				,			action='''store_true'''     )






class      UpperCamelCase_  (pl.Callback  ):







						"""simple docstring"""


						def    _a     (  self   :							List[str]				,			_lowerCamelCase   :							Tuple				,			_lowerCamelCase   :							Optional[int]     ):





											"""simple docstring"""







											A_			:  Dict			=				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(lowerCAmelCase_     )







						def    _a     (  self   :							Any				,			_lowerCamelCase   :							List[str]				,			_lowerCamelCase   :							List[str]     ):





											"""simple docstring"""







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







						def    _a     (  self   :							Union[str, Any]				,			_lowerCamelCase   :							Optional[Any]				,			_lowerCamelCase   :							Dict     ):





											"""simple docstring"""







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






def     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: Optional[Any]      ) -> None:
					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=4_2      ,		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     snake_case__     (			lowerCamelCase__					: BaseTransformer      ,		lowerCamelCase__					: argparse.Namespace      ,		lowerCamelCase__					: Any=None      ,		lowerCamelCase__					: str=True      ,		lowerCamelCase__					: int=[]      ,		lowerCamelCase__					: Any=None      ,		lowerCamelCase__					: Optional[int]=None      ,		**lowerCamelCase__					: Optional[int]      ,		) -> str:
					pl.seed_everything(args.seed      )

					# init model
					A_			:  Tuple			=				Path(model.hparams.output_dir      )
					odir.mkdir(exist_ok=UpperCamelCase__      )

					# add custom checkpoints
					if checkpoint_callback is None:
										A_			:  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(UpperCamelCase__      )
					if logging_callback is None:
										A_			:  Optional[int]			=				LoggingCallback()

					A_			:  int			=				{}

					# TODO: remove with PyTorch 1.6 since pl uses native amp
					if args.fpaa:
										A_			:  List[str]			=				1_6
										A_			:  Optional[Any]			=				args.fpaa_opt_level

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

					A_			:  Dict			=				args.accumulate_grad_batches
					A_			:  List[Any]			=				extra_train_kwargs.get('''accelerator'''      ,		UpperCamelCase__      )
					A_			:  Dict			=				extra_train_kwargs.get('''profiler'''      ,		UpperCamelCase__      )

					A_			:  List[str]			=				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










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       UpperCamelCase_		(	pl.LightningModule       ):




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=None					,    lowerCAmelCase_="base"					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    lowerCAmelCase_=None					,    **lowerCAmelCase_					,    )		->				Union[str, 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(lowerCAmelCase_	)
										_snake_case						=					0
										_snake_case						=					Path(self.hparams.output_dir	)
										_snake_case						=					self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
															_snake_case						=					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=lowerCAmelCase_					,    **lowerCAmelCase_					,    )
										else:
															_snake_case						=					config

										_snake_case						=					('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
										for p in extra_model_params:
															if getattr(self.hparams					,    lowerCAmelCase_					,    lowerCAmelCase_	):
																				assert hasattr(self.config					,    lowerCAmelCase_	), F'''model config doesn\'t have a `{p}` attribute'''
																				setattr(self.config					,    lowerCAmelCase_					,    getattr(self.hparams					,    lowerCAmelCase_	)	)

										if tokenizer is None:
															_snake_case						=					AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path					,    cache_dir=lowerCAmelCase_					,    )
										else:
															_snake_case						=					tokenizer
										_snake_case						=					MODEL_MODES[mode]
										if model is None:
															_snake_case						=					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=lowerCAmelCase_					,    )
										else:
															_snake_case						=					model




					def 						lowerCAmelCase      (		self					,    *lowerCAmelCase_					,    **lowerCAmelCase_	)		->				Union[str, Any]:
										_snake_case						=					self.model_type.from_pretrained(*lowerCAmelCase_					,    **lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					arg_to_scheduler[self.hparams.lr_scheduler]
										_snake_case						=					get_schedule_func(
										    self.opt					,    num_warmup_steps=self.hparams.warmup_steps					,    num_training_steps=self.total_steps()	)
										_snake_case						=					{'scheduler': scheduler, 'interval': 'step', 'frequency': 1}
										return scheduler




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case						=					self.model
										_snake_case						=					['bias', 'LayerNorm.weight']
										_snake_case						=					[
										    {
										        '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:
															_snake_case						=					Adafactor(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    scale_parameter=lowerCAmelCase_					,    relative_step=lowerCAmelCase_	)

										else:
															_snake_case						=					AdamW(
															    lowerCAmelCase_					,    lr=self.hparams.learning_rate					,    eps=self.hparams.adam_epsilon	)
										_snake_case						=					optimizer

										_snake_case						=					self.get_lr_scheduler()

										return [optimizer], [scheduler]




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				int:
										return self.validation_step(lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return self.validation_end(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				int:
										_snake_case						=					max(1					,    self.hparams.gpus	)  # TODO: consider num_tpu_cores
										_snake_case						=					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					,    lowerCAmelCase_	)		->				str:
										if mode == "test":
															_snake_case						=					len(self.test_dataloader().dataset	)
										else:
															_snake_case						=					self.get_dataloader('train'					,    self.hparams.train_batch_size					,    shuffle=lowerCAmelCase_	)
															_snake_case						=					len(self.train_dataloader().dataset	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_ = False	)		->				int:
										raise NotImplementedError('You must implement this for your task'	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										return self.train_loader




					def 						lowerCAmelCase      (		self	)		->				Dict:
										return self.get_dataloader('dev'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										return self.get_dataloader('test'					,    self.hparams.eval_batch_size					,    shuffle=lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				Union[str, Any]:
										return os.path.join(
										    self.hparams.data_dir					,    'cached_{}_{}_{}'.format(
										        lowerCAmelCase_					,    list(filter(lowerCAmelCase_					,    self.hparams.model_name_or_path.split('/'	)	)	).pop()					,    str(self.hparams.max_seq_length	)					,    )					,    )




					@pl.utilities.rank_zero_only
					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				None:
										_snake_case						=					self.output_dir.joinpath('best_tfmr'	)
										_snake_case						=					self.step_count
										self.model.save_pretrained(lowerCAmelCase_	)
										self.tokenizer.save_pretrained(lowerCAmelCase_	)






					@staticmethod
					def 						lowerCAmelCase      (		lowerCAmelCase_					,    lowerCAmelCase_	)		->				Optional[int]:
										parser.add_argument(
										    '--model_name_or_path'					,    default=lowerCAmelCase_					,    type=lowerCAmelCase_					,    required=lowerCAmelCase_					,    help='Path to pretrained model or model identifier from huggingface.co/models'					,    )
										parser.add_argument(
										    '--config_name'					,    default=''					,    type=lowerCAmelCase_					,    help='Pretrained config name or path if not the same as model_name'	)
										parser.add_argument(
										    '--tokenizer_name'					,    default=lowerCAmelCase_					,    type=lowerCAmelCase_					,    help='Pretrained tokenizer name or path if not the same as model_name'					,    )
										parser.add_argument(
										    '--cache_dir'					,    default=''					,    type=lowerCAmelCase_					,    help='Where do you want to store the pre-trained models downloaded from huggingface.co'					,    )
										parser.add_argument(
										    '--encoder_layerdrop'					,    type=lowerCAmelCase_					,    help='Encoder layer dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument(
										    '--decoder_layerdrop'					,    type=lowerCAmelCase_					,    help='Decoder layer dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument(
										    '--dropout'					,    type=lowerCAmelCase_					,    help='Dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument(
										    '--attention_dropout'					,    type=lowerCAmelCase_					,    help='Attention dropout probability (Optional). Goes into model.config'					,    )
										parser.add_argument('--learning_rate'					,    default=5E-5					,    type=lowerCAmelCase_					,    help='The initial learning rate for Adam.'	)
										parser.add_argument(
										    '--lr_scheduler'					,    default='linear'					,    choices=lowerCAmelCase_					,    metavar=lowerCAmelCase_					,    type=lowerCAmelCase_					,    help='Learning rate scheduler'					,    )
										parser.add_argument('--weight_decay'					,    default=0.0					,    type=lowerCAmelCase_					,    help='Weight decay if we apply some.'	)
										parser.add_argument('--adam_epsilon'					,    default=1E-8					,    type=lowerCAmelCase_					,    help='Epsilon for Adam optimizer.'	)
										parser.add_argument('--warmup_steps'					,    default=0					,    type=lowerCAmelCase_					,    help='Linear warmup over warmup_steps.'	)
										parser.add_argument('--num_workers'					,    default=4					,    type=lowerCAmelCase_					,    help='kwarg passed to DataLoader'	)
										parser.add_argument('--num_train_epochs'					,    dest='max_epochs'					,    default=3					,    type=lowerCAmelCase_	)
										parser.add_argument('--train_batch_size'					,    default=32					,    type=lowerCAmelCase_	)
										parser.add_argument('--eval_batch_size'					,    default=32					,    type=lowerCAmelCase_	)
										parser.add_argument('--adafactor'					,    action='store_true'	)





class       UpperCamelCase_		(	pl.Callback       ):




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					trainer.lr_schedulers[0]['scheduler']
										_snake_case						=					{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()	)}
										pl_module.logger.log_metrics(lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										rank_zero_info('***** Validation results *****'	)
										_snake_case						=					trainer.callback_metrics
										# Log results
										for key in sorted(lowerCAmelCase_	):
															if key not in ["log", "progress_bar"]:
																				rank_zero_info('{} = {}\n'.format(lowerCAmelCase_					,    str(metrics[key]	)	)	)






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





def     lowerCamelCase__  (  UpperCamelCase__      :							int							,     UpperCamelCase__      :							Optional[Any]    )      ->			None:



					'''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     lowerCamelCase__  (  UpperCamelCase__      :							BaseTransformer							,     UpperCamelCase__      :							argparse.Namespace							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							str=True							,     UpperCamelCase__      :							int=[]							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							Optional[int]							,     )      ->			str:



					'''simple docstring'''




					pl.seed_everything(args.seed    )

					# init model
					_snake_case						=					Path(model.hparams.output_dir    )
					odir.mkdir(exist_ok=UpperCamelCase__    )

					# add custom checkpoints
					if checkpoint_callback is None:
										_snake_case						=					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:
										_snake_case						=					LoggingCallback()

					_snake_case						=					{}

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

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

					_snake_case						=					args.accumulate_grad_batches
					_snake_case						=					extra_train_kwargs.get('accelerator'							,     UpperCamelCase__    )
					_snake_case						=					extra_train_kwargs.get('profiler'							,     UpperCamelCase__    )

					_snake_case						=					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



