
"""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


__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			,  __A			,  __A=None			,  __A="base"			,  __A=None			,  __A=None			,  __A=None			,  **__A			,  )     ->	List[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(__A			)
						lowerCAmelCase_       :List[Any]     	=    0
						lowerCAmelCase_       :str     	=    Path(self.hparams.output_dir			)
						lowerCAmelCase_       :List[str]     	=    self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
									lowerCAmelCase_       :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:
									lowerCAmelCase_       :PretrainedConfig     	=    config

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






			def 					__lowerCAmelCase					(   self			,  *__A			,  **__A			)     ->	List[Any]:
						lowerCAmelCase_       :Any     	=    self.model_type.from_pretrained(*__A			,  **__A			)






			def 					__lowerCAmelCase					(   self			)     ->	List[Any]:
						lowerCAmelCase_       :Optional[Any]     	=    arg_to_scheduler[self.hparams.lr_scheduler]
						lowerCAmelCase_       :Tuple     	=    get_schedule_func(
						    self.opt			,  num_warmup_steps=self.hparams.warmup_steps			,  num_training_steps=self.total_steps()			)
						lowerCAmelCase_       :Dict     	=    {"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
						return scheduler






			def 					__lowerCAmelCase					(   self			)     ->	str:
						lowerCAmelCase_       :Union[str, Any]     	=    self.model
						lowerCAmelCase_       :Optional[int]     	=    ["""bias""", """LayerNorm.weight"""]
						lowerCAmelCase_       :List[str]     	=    [
						    {
						        """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:
									lowerCAmelCase_       :str     	=    Adafactor(
									    __A			,  lr=self.hparams.learning_rate			,  scale_parameter=__A			,  relative_step=__A			)

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

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

						return [optimizer], [scheduler]






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	Any:
						return self.validation_step(__A			,  __A			)






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






			def 					__lowerCAmelCase					(   self			)     ->	int:
						lowerCAmelCase_       :List[str]     	=    max(1			,  self.hparams.gpus			)  # TODO: consider num_tpu_cores
						lowerCAmelCase_       :Optional[int]     	=    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			,  __A			)     ->	Tuple:
						if mode == "test":
									lowerCAmelCase_       :Any     	=    len(self.test_dataloader().dataset			)
						else:
									lowerCAmelCase_       :List[Any]     	=    self.get_dataloader("""train"""			,  self.hparams.train_batch_size			,  shuffle=__A			)
									lowerCAmelCase_       :Union[str, Any]     	=    len(self.train_dataloader().dataset			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			,  __A = False			)     ->	Union[str, Any]:
						raise NotImplementedError("""You must implement this for your task"""			)






			def 					__lowerCAmelCase					(   self			)     ->	Optional[int]:
						return self.train_loader






			def 					__lowerCAmelCase					(   self			)     ->	List[str]:
						return self.get_dataloader("""dev"""			,  self.hparams.eval_batch_size			,  shuffle=__A			)






			def 					__lowerCAmelCase					(   self			)     ->	Any:
						return self.get_dataloader("""test"""			,  self.hparams.eval_batch_size			,  shuffle=__A			)






			def 					__lowerCAmelCase					(   self			,  __A			)     ->	Optional[int]:
						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 					__lowerCAmelCase					(   self			,  __A			)     ->	None:
						lowerCAmelCase_       :Dict     	=    self.output_dir.joinpath("""best_tfmr"""			)
						lowerCAmelCase_       :int     	=    self.step_count
						self.model.save_pretrained(__A			)
						self.tokenizer.save_pretrained(__A			)






			@staticmethod
			def 					__lowerCAmelCase					(   __A			,  __A			)     ->	Dict:
						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 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	Any:
						lowerCAmelCase_       :str     	=    trainer.lr_schedulers[0]["""scheduler"""]
						lowerCAmelCase_       :List[Any]     	=    {f"""lr_group_{i}""": lr for i, lr in enumerate(lr_scheduler.get_lr()			)}
						pl_module.logger.log_metrics(__A			)






			def 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	int:
						rank_zero_info("""***** Validation results *****"""			)
						lowerCAmelCase_       :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 					__lowerCAmelCase					(   self			,  __A			,  __A			)     ->	Tuple:
						rank_zero_info("""***** Test results *****"""			)
						lowerCAmelCase_       :str     	=    trainer.callback_metrics
						# Log and save results to file
						lowerCAmelCase_       :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 		_snake_case			(      lowercase__						:    Union[str, Any] ,	lowercase__						:    Union[str, Any]						)    ->							None:


			'''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=4_2 ,	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 		_snake_case			(      lowercase__						:    BaseTransformer ,	lowercase__						:    argparse.Namespace ,	lowercase__						:    Union[str, Any]=None ,	lowercase__						:    str=True ,	lowercase__						:    str=[] ,	lowercase__						:    Optional[int]=None ,	lowercase__						:    Any=None ,	**lowercase__						:    Optional[Any] ,	)    ->							Tuple:


			'''simple docstring'''





			pl.seed_everything(args.seed						)

			# init model
			lowerCAmelCase_       :Tuple     	=    Path(model.hparams.output_dir						)
			odir.mkdir(exist_ok=lowercase__						)

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

			lowerCAmelCase_       :Optional[Any]     	=    {}

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

			if args.gpus > 1:
						lowerCAmelCase_       :Optional[Any]     	=    """ddp"""

			lowerCAmelCase_       :Optional[Any]     	=    args.accumulate_grad_batches
			lowerCAmelCase_       :str     	=    extra_train_kwargs.get("""accelerator""" ,	lowercase__						)
			lowerCAmelCase_       :str     	=    extra_train_kwargs.get("""profiler""" ,	lowercase__						)

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

			if args.do_train:
						trainer.fit(lowercase__						)

			return trainer








"""simple docstring"""






from __future__ import annotations






def 		_snake_case			(      lowercase__						:    list[int | float]						)    ->							int | float:


			'''simple docstring'''





			if len(lowercase__						) == 0:
						raise ValueError("""find_min() arg is an empty sequence"""						)
			lowerCAmelCase_       :Optional[int]     	=    nums[0]
			for num in nums:
						lowerCAmelCase_       :Optional[Any]     	=    min(lowercase__ ,	lowercase__						)
			return min_num


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)






