


"""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


lowercase  		=			logging.getLogger(__name__)

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

lowercase  		=			{
    '''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
lowercase  		=			{
    '''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
}
lowercase  		=			sorted(arg_to_scheduler.keys())
lowercase  		=			'''{''' + ''', '''.join(arg_to_scheduler_choices) + '''}'''


class 			SCREAMING_SNAKE_CASE_  (   pl.LightningModule):




							'''simple docstring'''





							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__=None	,					lowerCamelCase__="base"	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					**lowerCamelCase__	,					)   ->	Optional[Any]:







								'''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__)
								snake_case__  :						Dict		=      0
								snake_case__  :						Union[str, Any]		=      Path(self.hparams.output_dir)
								snake_case__  :						List[Any]		=      self.hparams.cache_dir if self.hparams.cache_dir else None
								if config is None:
									snake_case__  :						Tuple		=      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:
									snake_case__  :						PretrainedConfig		=      config

								snake_case__  :						Tuple		=      ('''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:
									snake_case__  :						str		=      AutoTokenizer.from_pretrained(
									    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path	,					cache_dir=lowerCamelCase__	,					)
								else:
									snake_case__  :						PreTrainedTokenizer		=      tokenizer
								snake_case__  :						str		=      MODEL_MODES[mode]
								if model is None:
									snake_case__  :						List[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:
									snake_case__  :						str		=      model




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	List[Any]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.model
								snake_case__  :						int		=      ['''bias''', '''LayerNorm.weight''']
								snake_case__  :						Union[str, 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:
									snake_case__  :						List[Any]		=      Adafactor(
									    lowerCamelCase__	,					lr=self.hparams.learning_rate	,					scale_parameter=lowerCamelCase__	,					relative_step=lowerCamelCase__)

								else:
									snake_case__  :						Dict		=      AdamW(
									    lowerCamelCase__	,					lr=self.hparams.learning_rate	,					eps=self.hparams.adam_epsilon)
								snake_case__  :						Optional[Any]		=      optimizer

								snake_case__  :						Tuple		=      self.get_lr_scheduler()

								return [optimizer], [scheduler]




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







								'''simple docstring'''




								return self.validation_step(lowerCamelCase__	,					lowerCamelCase__)




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







								'''simple docstring'''




								return self.validation_end(lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						List[str]		=      max(1	,					self.hparams.gpus)  # TODO: consider num_tpu_cores
								snake_case__  :						List[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	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								if mode == "test":
									snake_case__  :						Dict		=      len(self.test_dataloader().dataset)
								else:
									snake_case__  :						str		=      self.get_dataloader("train"	,					self.hparams.train_batch_size	,					shuffle=lowerCamelCase__)
									snake_case__  :						Optional[Any]		=      len(self.train_dataloader().dataset)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = False)   ->	Union[str, Any]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								return self.train_loader




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	Tuple:







								'''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	,					lowerCamelCase__)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.output_dir.joinpath("best_tfmr")
								snake_case__  :						str		=      self.step_count
								self.model.save_pretrained(lowerCamelCase__)
								self.tokenizer.save_pretrained(lowerCamelCase__)







							@staticmethod
							def        UpperCAmelCase (						lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[Any]:







								'''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):




							'''simple docstring'''





							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Dict:







								'''simple docstring'''




								snake_case__  :						Tuple		=      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(lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	int:







								'''simple docstring'''




								rank_zero_info("***** Validation results *****")
								snake_case__  :						str		=      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	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								rank_zero_info("***** Test results *****")
								snake_case__  :						List[Any]		=      trainer.callback_metrics
								# Log and save results to file
								snake_case__  :						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 			A__		(					_UpperCAmelCase    :      Optional[Any]				,    _UpperCAmelCase    :      Dict	)							->	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 			A__		(					_UpperCAmelCase    :      List[Any]				,    _UpperCAmelCase    :      int				,    _UpperCAmelCase    :      List[str]=None				,    _UpperCAmelCase    :      List[str]=True				,    _UpperCAmelCase    :      List[str]=[]				,    _UpperCAmelCase    :      Union[str, Any]=None				,    _UpperCAmelCase    :      Dict=None				,    **_UpperCAmelCase    :      List[str]				,    )							->	str:

	'''simple docstring'''


	pl.seed_everything(args.seed	)

	# init model
	snake_case__  :						Optional[int]		=      Path(model.hparams.output_dir	)
	odir.mkdir(exist_ok=_UpperCAmelCase	)

	# add custom checkpoints
	if checkpoint_callback is None:
		snake_case__  :						Tuple		=      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:
		snake_case__  :						Tuple		=      LoggingCallback()

	snake_case__  :						Tuple		=      {}

	# TODO: remove with PyTorch 1.6 since pl uses native amp
	if args.fpaa:
		snake_case__  :						str		=      16
		snake_case__  :						Union[str, Any]		=      args.fpaa_opt_level

	if args.gpus > 1:
		snake_case__  :						Optional[Any]		=      '''ddp'''

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

	snake_case__  :						List[str]		=      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











'''simple docstring'''






from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

lowercase						:   Union[str, Any]      						=							TypeVar('''T''', bound=bool)

class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A			):






        '''simple docstring'''




        lowercase	:		T    =   value
        lowercase	:		SkewNode[T] | None    =   None
        lowercase	:		SkewNode[T] | None    =   None



 @property
 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        return self._value





 @staticmethod
 def  SCREAMING_SNAKE_CASE(	A ,		A			):






        '''simple docstring'''




        if not roota:
               return roota

        if not roota:
               return roota

        if roota.value > roota.value:
               lowercase,  lowercase	:		Union[str, Any]    =   roota, roota

        lowercase	:		Optional[int]    =   roota
        lowercase	:		Tuple    =   roota.right
        lowercase	:		Union[str, Any]    =   roota.left
        lowercase	:		Any    =   SkewNode.merge(A ,		A			)

        return result






class 	SCREAMING_SNAKE_CASE_		(							Generic[T]					):



 def __init__(	self ,		A = ()			):






        '''simple docstring'''




        lowercase	:		SkewNode[T] | None    =   None
        if data:
               for item in data:
                      self.insert(A			)



 def __bool__(	self			):






        '''simple docstring'''




        return self._root is not None



 def __iter__(	self			):






        '''simple docstring'''




        lowercase	:		list[Any]    =   []
        while self:
               result.append(self.pop()			)

        # Pushing items back to the heap not to clear it.
        for item in result:
               self.insert(A			)

        return iter(A			)



 def  SCREAMING_SNAKE_CASE(	self ,		A			):






        '''simple docstring'''




        lowercase	:		List[str]    =   SkewNode.merge(self._root ,		SkewNode(A			)			)



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		List[Any]    =   self.top()
        lowercase	:		int    =   (
            SkewNode.merge(self._root.left ,		self._root.right			) if self._root else None
        )

        return result



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        if not self._root:
               raise IndexError('''Can\'t get top element for the empty heap.'''			)
        return self._root.value





 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		Optional[Any]    =   None


if __name__ == "__main__":
      import doctest

      doctest.testmod()




