
"""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


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

require_version('''pytorch_lightning>=1.0.4''')

__lowerCAmelCase							:  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
__lowerCAmelCase							:  int 				=		{
    '''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
}
__lowerCAmelCase							:  Optional[int] 				=		sorted(arg_to_scheduler.keys())
__lowerCAmelCase							:  Optional[int] 				=		'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''
class SCREAMING_SNAKE_CASE       (      pl.LightningModule					):






	'''simple docstring'''







	def __init__(  self :List[Any]       ,    __magic_name__ :argparse.Namespace       ,    __magic_name__ :str=None       ,    __magic_name__ :str="base"       ,    __magic_name__ :Dict=None       ,    __magic_name__ :Dict=None       ,    __magic_name__ :int=None       ,    **__magic_name__ :Dict       ,    )			->   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(__magic_name__			)
			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=__magic_name__       ,    **__magic_name__       ,    )
			else:
					a__   		=     config

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

			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=__magic_name__       ,    )
			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=__magic_name__       ,    )
			else:
					a__   		=     model


	def    _UpperCamelCase							(  self :int       ,    *__magic_name__ :str       ,    **__magic_name__ :Any			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     self.model_type.from_pretrained(*__magic_name__       ,    **__magic_name__			)


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   Optional[int]:

			'''simple docstring'''






			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    _UpperCamelCase							(  self :str			)			->   List[Any]:

			'''simple docstring'''






			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(
					    __magic_name__       ,    lr=self.hparams.learning_rate       ,    scale_parameter=__magic_name__       ,    relative_step=__magic_name__			)

			else:
					a__   		=     AdamW(
					    __magic_name__       ,    lr=self.hparams.learning_rate       ,    eps=self.hparams.adam_epsilon			)
			a__   		=     optimizer

			a__   		=     self.get_lr_scheduler()

			return [optimizer], [scheduler]


	def    _UpperCamelCase							(  self :Tuple       ,    __magic_name__ :Union[str, Any]       ,    __magic_name__ :Union[str, Any]			)			->   int:

			'''simple docstring'''






			return self.validation_step(__magic_name__       ,    __magic_name__			)


	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :Any			)			->   Dict:

			'''simple docstring'''






			return self.validation_end(__magic_name__			)


	def    _UpperCamelCase							(  self :List[str]			)			->   int:

			'''simple docstring'''






			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    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :str			)			->   List[str]:

			'''simple docstring'''






			if stage == "test":
					a__   		=     len(self.test_dataloader().dataset			)
			else:
					a__   		=     self.get_dataloader('''train'''       ,    self.hparams.train_batch_size       ,    shuffle=__magic_name__			)
					a__   		=     len(self.train_dataloader().dataset			)


	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :str       ,    __magic_name__ :int       ,    __magic_name__ :bool = False			)			->   Optional[int]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Any			)			->   Union[str, Any]:

			'''simple docstring'''






			return self.train_loader


	def    _UpperCamelCase							(  self :List[Any]			)			->   Optional[Any]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Tuple			)			->   List[Any]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :Dict       ,    __magic_name__ :str			)			->   str:

			'''simple docstring'''






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


	@pl.utilities.rank_zero_only
	def    _UpperCamelCase							(  self :Tuple       ,    __magic_name__ :Dict[str, Any]			)			->   None:

			'''simple docstring'''






			a__   		=     self.output_dir.joinpath('''best_tfmr'''			)
			a__   		=     self.step_count
			self.model.save_pretrained(__magic_name__			)
			self.tokenizer.save_pretrained(__magic_name__			)






	@staticmethod
	def    _UpperCamelCase							(  __magic_name__ :Optional[int]       ,    __magic_name__ :Optional[int]			)			->   Tuple:

			'''simple docstring'''






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

class SCREAMING_SNAKE_CASE       (      pl.Callback					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :List[Any]       ,    __magic_name__ :Optional[Any]       ,    __magic_name__ :List[Any]			)			->   Tuple:

			'''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					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :Tuple       ,    __magic_name__ :str			)			->   Optional[Any]:

			'''simple docstring'''






			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(__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[int]       ,    __magic_name__ :pl.Trainer       ,    __magic_name__ :pl.LightningModule			)			->   List[str]:

			'''simple docstring'''






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






	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :pl.Trainer       ,    __magic_name__ :pl.LightningModule			)			->   Tuple:

			'''simple docstring'''






			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(__magic_name__       ,    '''w'''			) as writer:
					for key in sorted(__magic_name__			):
							if key not in ["log", "progress_bar"]:
									rank_zero_info('''{} = {}\n'''.format(__magic_name__       ,    str(metrics[key]			)			)			)
									writer.write('''{} = {}\n'''.format(__magic_name__       ,    str(metrics[key]			)			)			)

def __snake_case (      UpperCamelCase						,					UpperCamelCase    )       ->    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 __snake_case (      UpperCamelCase						,					UpperCamelCase						,					UpperCamelCase=None						,					UpperCamelCase=True						,					UpperCamelCase=None						,					UpperCamelCase=[]						,					UpperCamelCase=None						,					UpperCamelCase=None						,					**UpperCamelCase						,					)       ->    Optional[Any]:


		"""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
				# train_params["amp_level"] = args.fp16_opt_level

		if args.gpus > 1:
				a__   		=     '''auto'''  # "ddp"
				a__   		=     '''ddp'''

		a__   		=     args.accumulate_grad_batches
		a__   		=     None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
		a__   		=     '''auto'''

		a__   		=     pl.Trainer.from_argparse_args(
		    UpperCamelCase						,					weights_summary=UpperCamelCase						,					callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]						,					logger=UpperCamelCase						,					**UpperCamelCase						,					)

		if args.do_train:
				trainer.fit(UpperCamelCase    )

		return trainer






"""simple docstring"""




import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__lowerCAmelCase							:  Dict 				=		logging.getLogger()

def __snake_case (      )       ->    Optional[int]:


		"""simple docstring"""





		a__   		=     argparse.ArgumentParser()
		parser.add_argument('''-f'''    )
		a__   		=     parser.parse_args()
		return args.f




class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def    _UpperCamelCase							(  self :Tuple			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     logging.StreamHandler(sys.stdout			)
			logger.addHandler(__magic_name__			)

			a__   		=     self.get_auto_remove_tmp_dir()
			a__   		=     F"\n            run_glue_with_pabee.py\n            --model_type albert\n            --model_name_or_path albert-base-v2\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --task_name mrpc\n            --do_train\n            --do_eval\n            --per_gpu_train_batch_size=2\n            --per_gpu_eval_batch_size=1\n            --learning_rate=2e-5\n            --max_steps=50\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

			with patch.object(__magic_name__       ,    '''argv'''       ,    __magic_name__			):
					a__   		=     run_glue_with_pabee.main()
					for value in result.values():
							self.assertGreaterEqual(__magic_name__       ,    0.75			)




