
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     _SCREAMING_SNAKE_CASE							(		pl.LightningModule							):





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


								"""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     :  str   =				0
								A     :  Any   =				Path(self.hparams.output_dir							)
								A     :  Union[str, Any]   =				self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
												A     :  Optional[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=lowerCamelCase__					,      **lowerCamelCase__					,      )
								else:
												A     :  Any   =				config

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





				def        _UpperCAmelCase						(		self :		List[Any]					,      *snake_case_ :		Tuple					,      **snake_case_ :		str							):


								"""simple docstring"""

								A     :  Dict   =				self.model_type.from_pretrained(*lowerCamelCase__					,      **lowerCamelCase__							)





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


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		Dict							):


								"""simple docstring"""

								A     :  List[Any]   =				self.model
								A     :  Tuple   =				['''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     :  List[Any]   =				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     :  Optional[Any]   =				optimizer

								A     :  List[Any]   =				self.get_lr_scheduler()

								return [optimizer], [scheduler]





				def        _UpperCAmelCase						(		self :		List[Any]					,      snake_case_ :		Optional[Any]					,      snake_case_ :		Optional[Any]							):


								"""simple docstring"""

								return self.validation_step(lowerCamelCase__					,      lowerCamelCase__							)





				def        _UpperCAmelCase						(		self :		int					,      snake_case_ :		str							):


								"""simple docstring"""

								return self.validation_end(lowerCamelCase__							)





				def        _UpperCAmelCase						(		self :		str							):


								"""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        _UpperCAmelCase						(		self :		int					,      snake_case_ :		int							):


								"""simple docstring"""

								if stage == "test":
												A     :  Optional[int]   =				len(self.test_dataloader().dataset							)
								else:
												A     :  Union[str, Any]   =				self.get_dataloader('''train'''					,      self.hparams.train_batch_size					,      shuffle=lowerCamelCase__							)
												A     :  List[Any]   =				len(self.train_dataloader().dataset							)





				def        _UpperCAmelCase						(		self :		Tuple					,      snake_case_ :		Tuple					,      snake_case_ :		Dict					,      snake_case_ :		Optional[Any] = False							):


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		Optional[Any]							):


								"""simple docstring"""

								return self.train_loader





				def        _UpperCAmelCase						(		self :		Tuple							):


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		str							):


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		List[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        _UpperCAmelCase						(		self :		Union[str, Any]					,      snake_case_ :		Any							):


								"""simple docstring"""

								A     :  List[str]   =				self.output_dir.joinpath('''best_tfmr'''							)
								A     :  int   =				self.step_count
								self.model.save_pretrained(lowerCamelCase__							)
								self.tokenizer.save_pretrained(lowerCamelCase__							)






				@staticmethod
				def        _UpperCAmelCase						(		snake_case_ :		str					,      snake_case_ :		Optional[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     _SCREAMING_SNAKE_CASE							(		pl.Callback							):





				def        _UpperCAmelCase						(		self :		List[str]					,      snake_case_ :		Any					,      snake_case_ :		str							):


								"""simple docstring"""

								if (
								    trainer.is_global_zero and trainer.global_rank == 0
								):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
												pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.



class     _SCREAMING_SNAKE_CASE							(		pl.Callback							):





				def        _UpperCAmelCase						(		self :		int					,      snake_case_ :		int					,      snake_case_ :		List[Any]							):


								"""simple docstring"""

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





				def        _UpperCAmelCase						(		self :		Optional[Any]					,      snake_case_ :		str					,      snake_case_ :		Optional[int]							):


								"""simple docstring"""

								rank_zero_info('''***** Validation results *****'''							)
								A     :  Optional[int]   =				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        _UpperCAmelCase						(		self :		str					,      snake_case_ :		int					,      snake_case_ :		Union[str, Any]							):


								"""simple docstring"""

								rank_zero_info('''***** Test results *****'''							)
								A     :  Optional[Any]   =				trainer.callback_metrics
								# Log and save results to file
								A     :  str   =				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			(					lowerCamelCase_:  Any				,	lowerCamelCase_:  List[str]		):



				'''simple docstring'''







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



				'''simple docstring'''







				pl.seed_everything(args.seed		)

				# init model
				A     :  List[str]   =				Path(model.hparams.output_dir		)
				odir.mkdir(exist_ok=lowercase__		)

				# add custom checkpoints
				if checkpoint_callback is None:
								A     :  Dict   =				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     :  Optional[int]   =				LoggingCallback()

				A     :  List[Any]   =				{}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								A     :  Any   =				16
								# train_params["amp_level"] = args.fp16_opt_level

				if args.gpus > 1:
								A     :  Tuple   =				'''auto'''  # "ddp"
								A     :  int   =				'''ddp'''

				A     :  Dict   =				args.accumulate_grad_batches
				A     :  Optional[int]   =				None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
				A     :  List[Any]   =				'''auto'''

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

				if args.do_train:
								trainer.fit(lowercase__		)

				return trainer

def 							_lowerCamelCase			(					lowerCamelCase_:  int		):



				'''simple docstring'''







				if not isinstance(lowerCamelCase_				,	lowerCamelCase_		):
								raise ValueError('''double_factorial() only accepts integral values'''		)
				if num < 0:
								raise ValueError('''double_factorial() not defined for negative values'''		)
				A     :  int   =				1
				for i in range(lowerCamelCase_				,	0				,	-2		):
								value *= i
				return value


if __name__ == "__main__":
					import doctest

					doctest.testmod()