





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   						=		logging.getLogger(__name__)

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

_SCREAMING_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
_SCREAMING_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
}
_SCREAMING_SNAKE_CASE   						=		sorted(arg_to_scheduler.keys())
_SCREAMING_SNAKE_CASE   						=		"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""
class SCREAMING_SNAKE_CASE_    (       pl.LightningModule       ):






						def __init__(   self   :   Union[str, Any]		, _A   :   argparse.Namespace		, _A   :   Any=None		, _A   :   Any="base"		, _A   :   int=None		, _A   :   str=None		, _A   :   Dict=None		, **_A   :   Optional[Any]		, )				->     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(_A						)
							snake_case_		:      Optional[int]           =						0
							snake_case_		:      Union[str, Any]           =						Path(self.hparams.output_dir						)
							snake_case_		:      str           =						self.hparams.cache_dir if self.hparams.cache_dir else None
							if config is None:
								snake_case_		:      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:
								snake_case_		:      PretrainedConfig           =						config

							snake_case_		:      Dict           =						('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:
								snake_case_		:      Union[str, Any]           =						AutoTokenizer.from_pretrained(
								    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path		, cache_dir=_A		, )
							else:
								snake_case_		:      PreTrainedTokenizer           =						tokenizer
							snake_case_		:      int           =						MODEL_MODES[mode]
							if model is None:
								snake_case_		:      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:
								snake_case_		:      str           =						model






						def UpperCAmelCase_					(   self   :   Any		, *_A   :   Dict		, **_A   :   Optional[Any]						)				->     Optional[Any]:







							"""simple docstring"""


							snake_case_		:      List[str]           =						self.model_type.from_pretrained(*_A		, **_A						)






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







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   Dict						)				->     Optional[int]:







							"""simple docstring"""


							snake_case_		:      Any           =						self.model
							snake_case_		:      List[Any]           =						['bias', 'LayerNorm.weight']
							snake_case_		:      int           =						[
							    {
							        '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_		:      int           =						Adafactor(
								    _A		, lr=self.hparams.learning_rate		, scale_parameter=_A		, relative_step=_A						)

							else:
								snake_case_		:      int           =						AdamW(
								    _A		, lr=self.hparams.learning_rate		, eps=self.hparams.adam_epsilon						)
							snake_case_		:      Union[str, Any]           =						optimizer

							snake_case_		:      Tuple           =						self.get_lr_scheduler()

							return [optimizer], [scheduler]






						def UpperCAmelCase_					(   self   :   str		, _A   :   List[str]		, _A   :   Union[str, Any]						)				->     Tuple:







							"""simple docstring"""


							return self.validation_step(_A		, _A						)






						def UpperCAmelCase_					(   self   :   str		, _A   :   int						)				->     Optional[Any]:







							"""simple docstring"""


							return self.validation_end(_A						)






						def UpperCAmelCase_					(   self   :   List[Any]						)				->     int:







							"""simple docstring"""


							snake_case_		:      Optional[int]           =						max(1		, self.hparams.gpus						)  # TODO: consider num_tpu_cores
							snake_case_		:      Optional[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   :   Optional[Any]		, _A   :   List[Any]						)				->     Tuple:







							"""simple docstring"""


							if mode == "test":
								snake_case_		:      Optional[int]           =						len(self.test_dataloader().dataset						)
							else:
								snake_case_		:      Union[str, Any]           =						self.get_dataloader('train'		, self.hparams.train_batch_size		, shuffle=_A						)
								snake_case_		:      Dict           =						len(self.train_dataloader().dataset						)






						def UpperCAmelCase_					(   self   :   str		, _A   :   str		, _A   :   int		, _A   :   bool = False						)				->     Optional[Any]:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   str						)				->     int:







							"""simple docstring"""


							return self.train_loader






						def UpperCAmelCase_					(   self   :   Tuple						)				->     str:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   int						)				->     Optional[Any]:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   Dict		, _A   :   str						)				->     List[Any]:







							"""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 UpperCAmelCase_					(   self   :   List[Any]		, _A   :   Dict[str, Any]						)				->     None:







							"""simple docstring"""


							snake_case_		:      Any           =						self.output_dir.joinpath('best_tfmr'						)
							snake_case_		:      Any           =						self.step_count
							self.model.save_pretrained(_A						)
							self.tokenizer.save_pretrained(_A						)






						@staticmethod
						def UpperCAmelCase_					(   _A   :   Tuple		, _A   :   Optional[int]						)				->     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 SCREAMING_SNAKE_CASE_    (       pl.Callback       ):






						def UpperCAmelCase_					(   self   :   List[str]		, _A   :   int		, _A   :   int						)				->     Optional[int]:







							"""simple docstring"""


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






						def UpperCAmelCase_					(   self   :   str		, _A   :   pl.Trainer		, _A   :   pl.LightningModule						)				->     Tuple:







							"""simple docstring"""


							rank_zero_info('***** Validation results *****'						)
							snake_case_		:      Any           =						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 UpperCAmelCase_					(   self   :   Union[str, Any]		, _A   :   pl.Trainer		, _A   :   pl.LightningModule						)				->     Optional[Any]:







							"""simple docstring"""


							rank_zero_info('***** Test results *****'						)
							snake_case_		:      Union[str, Any]           =						trainer.callback_metrics
							# Log and save results to file
							snake_case_		:      List[str]           =						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 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	#  To allow all pl args uncomment the following line
	#  parser = pl.Trainer.add_argparse_args(parser)
	parser.add_argument(
	    '--output_dir' ,			default=__a ,			type=__a ,			required=__a ,			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=__a ,			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=__a )
	parser.add_argument('--max_grad_norm' ,			dest='gradient_clip_val' ,			default=1.0 ,			type=__a ,			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=__a ,			default=1 ,			help='Number of updates steps to accumulate before performing a backward/update pass.' ,			)
	parser.add_argument('--seed' ,			type=__a ,			default=42 ,			help='random seed for initialization' )
	parser.add_argument(
	    '--data_dir' ,			default=__a ,			type=__a ,			required=__a ,			help='The input data dir. Should contain the training files for the CoNLL-2003 NER task.' ,			)


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ,			__a=None ,			__a=True ,			__a=[] ,			__a=None ,			__a=None ,			**__a ,			):
	pl.seed_everything(args.seed )

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

	# add custom checkpoints
	if checkpoint_callback is None:
		snake_case_		:      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(__a )
	if logging_callback is None:
		snake_case_		:      Union[str, Any]           =						LoggingCallback()

	snake_case_		:      Tuple           =						{}

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

	if args.gpus > 1:
		snake_case_		:      List[str]           =						'ddp'

	snake_case_		:      List[Any]           =						args.accumulate_grad_batches
	snake_case_		:      Union[str, Any]           =						extra_train_kwargs.get('accelerator' ,			__a )
	snake_case_		:      Optional[Any]           =						extra_train_kwargs.get('profiler' ,			__a )

	snake_case_		:      Tuple           =						pl.Trainer.from_argparse_args(
	    __a ,			weights_summary=__a ,			callbacks=[logging_callback] + extra_callbacks ,			logger=__a ,			checkpoint_callback=__a ,			**__a ,			)

	if args.do_train:
		trainer.fit(__a )

	return trainer








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE   						=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   						=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}
class SCREAMING_SNAKE_CASE_    (       snake_case_       ):
						__magic_name__:      Optional[Any]			   =							"nystromformer"






						def __init__(   self   :   List[str]		, _A   :   Optional[Any]=30000		, _A   :   str=768		, _A   :   Optional[int]=12		, _A   :   Dict=12		, _A   :   Any=3072		, _A   :   str="gelu_new"		, _A   :   Optional[Any]=0.1		, _A   :   int=0.1		, _A   :   Dict=510		, _A   :   List[Any]=2		, _A   :   Optional[Any]=64		, _A   :   Union[str, Any]=64		, _A   :   List[Any]=65		, _A   :   Dict=False		, _A   :   str=0.0_2		, _A   :   Dict=1E-5		, _A   :   List[Any]=1		, _A   :   Optional[int]=0		, _A   :   List[Any]=2		, **_A   :   int		, )				->     int:







							"""simple docstring"""


							snake_case_		:      Union[str, Any]           =						vocab_size
							snake_case_		:      str           =						max_position_embeddings
							snake_case_		:      Tuple           =						hidden_size
							snake_case_		:      Union[str, Any]           =						num_hidden_layers
							snake_case_		:      str           =						num_attention_heads
							snake_case_		:      Optional[Any]           =						intermediate_size
							snake_case_		:      str           =						hidden_act
							snake_case_		:      str           =						hidden_dropout_prob
							snake_case_		:      Optional[Any]           =						attention_probs_dropout_prob
							snake_case_		:      Union[str, Any]           =						initializer_range
							snake_case_		:      Union[str, Any]           =						type_vocab_size
							snake_case_		:      Dict           =						segment_means_seq_len
							snake_case_		:      Tuple           =						num_landmarks
							snake_case_		:      Any           =						conv_kernel_size
							snake_case_		:      List[str]           =						inv_coeff_init_option
							snake_case_		:      str           =						layer_norm_eps
							super().__init__(pad_token_id=_A		, bos_token_id=_A		, eos_token_id=_A		, **_A						)

