


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


SCREAMING_SNAKE_CASE : Optional[int]          =    logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

SCREAMING_SNAKE_CASE : Dict          =    {
    "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
SCREAMING_SNAKE_CASE : 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
}
SCREAMING_SNAKE_CASE : str          =    sorted(arg_to_scheduler.keys())
SCREAMING_SNAKE_CASE : List[str]          =    "{" + ", ".join(arg_to_scheduler_choices) + "}"





class 					snake_case__  ( pl.LightningModule       ):





	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_=None				,    UpperCamelCase_="base"				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    **UpperCamelCase_				,    )		->			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(a_							)
								a_       :   str			=	0
								a_       :   List[Any]			=	Path(self.hparams.output_dir							)
								a_       :   List[str]			=	self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
															a_       :   List[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=a_				,    **a_				,    )
								else:
															a_       :   List[Any]			=	config

								a_       :   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:
															a_       :   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:
															a_       :   List[str]			=	tokenizer
								a_       :   Optional[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=a_				,    )
								else:
															a_       :   int			=	model





	def     A				(  self				,    *UpperCamelCase_				,    **UpperCamelCase_							)		->			int:
								"""simple docstring"""






								a_       :   List[Any]			=	self.model_type.from_pretrained(*a_				,    **a_							)





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






								a_       :   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_       :   Dict			=	{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
								return scheduler





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






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

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

								a_       :   Optional[Any]			=	self.get_lr_scheduler()

								return [optimizer], [scheduler]





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






								return self.validation_step(a_				,    a_							)





	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
								"""simple docstring"""






								return self.validation_end(a_							)





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






								a_       :   Dict			=	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				,    UpperCamelCase_							)		->			int:
								"""simple docstring"""






								if mode == "test":
															a_       :   str			=	len(self.test_dataloader().dataset							)
								else:
															a_       :   Dict			=	self.get_dataloader("""train"""				,    self.hparams.train_batch_size				,    shuffle=a_							)
															a_       :   Tuple			=	len(self.train_dataloader().dataset							)





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_ = False							)		->			List[Any]:
								"""simple docstring"""






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





	def     A				(  self							)		->			Union[str, Any]:
								"""simple docstring"""






								return self.train_loader





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






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





	def     A				(  self							)		->			Union[str, Any]:
								"""simple docstring"""






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





	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
								"""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     A				(  self				,    UpperCamelCase_							)		->			None:
								"""simple docstring"""






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





	@staticmethod
	def     A				(  UpperCamelCase_				,    UpperCamelCase_							)		->			List[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 					snake_case__  ( pl.Callback       ):





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
								"""simple docstring"""






								a_       :   Any			=	trainer.lr_schedulers[0]["""scheduler"""]
								a_       :   List[str]			=	{f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
								pl_module.logger.log_metrics(a_							)





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Tuple:
								"""simple docstring"""






								rank_zero_info("""***** Validation results *****"""							)
								a_       :   Dict			=	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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Any:
								"""simple docstring"""






								rank_zero_info("""***** Test results *****"""							)
								a_       :   int			=	trainer.callback_metrics
								# Log and save results to file
								a_       :   Optional[Any]			=	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 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Dict      ):






							"""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		(							SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: Union[str, Any]	,	SCREAMING_SNAKE_CASE_				: str=None	,	SCREAMING_SNAKE_CASE_				: List[str]=True	,	SCREAMING_SNAKE_CASE_				: str=[]	,	SCREAMING_SNAKE_CASE_				: Dict=None	,	SCREAMING_SNAKE_CASE_				: List[str]=None	,	**SCREAMING_SNAKE_CASE_				: Optional[int]	,	):






							"""simple docstring"""



							pl.seed_everything(args.seed      )

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

							# add custom checkpoints
							if checkpoint_callback is None:
														a_       :   List[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(UpperCamelCase__      )
							if logging_callback is None:
														a_       :   List[str]			=	LoggingCallback()

							a_       :   Union[str, Any]			=	{}

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

							if args.gpus > 1:
														a_       :   Optional[int]			=	"""ddp"""

							a_       :   Any			=	args.accumulate_grad_batches
							a_       :   Any			=	extra_train_kwargs.get("""accelerator"""	,	UpperCamelCase__      )
							a_       :   List[str]			=	extra_train_kwargs.get("""profiler"""	,	UpperCamelCase__      )

							a_       :   Tuple			=	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






def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):






							"""simple docstring"""



							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
							if n in (0, 1):
														return 0
							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()


