





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 				_UpperCamelCase       (						pl.LightningModule  ):

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

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

              if tokenizer is None:
                     UpperCamelCase_ 							=	AutoTokenizer.from_pretrained(
                         self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path       , cache_dir=_SCREAMING_SNAKE_CASE       , )
              else:
                     UpperCamelCase_ 							=	tokenizer
              UpperCamelCase_ 							=	MODEL_MODES[mode]
              if model is None:
                     UpperCamelCase_ 							=	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=_SCREAMING_SNAKE_CASE       , )
              else:
                     UpperCamelCase_ 							=	model

       def lowercase			(		self:			Any       , *_SCREAMING_SNAKE_CASE:			Optional[Any]       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			Tuple:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model_type.from_pretrained(*_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_ 							=	arg_to_scheduler[self.hparams.lr_scheduler]
              UpperCamelCase_ 							=	get_schedule_func(
                  self.opt       , num_warmup_steps=self.hparams.warmup_steps       , num_training_steps=self.total_steps()		)
              UpperCamelCase_ 							=	{"scheduler": scheduler, "interval": "step", "frequency": 1}
              return scheduler

       def lowercase			(		self:			List[str]		)							->			Any:




              """simple docstring"""


              UpperCamelCase_ 							=	self.model
              UpperCamelCase_ 							=	["bias", "LayerNorm.weight"]
              UpperCamelCase_ 							=	[
                  {
                      "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:
                     UpperCamelCase_ 							=	Adafactor(
                         _SCREAMING_SNAKE_CASE       , lr=self.hparams.learning_rate       , scale_parameter=_SCREAMING_SNAKE_CASE       , relative_step=_SCREAMING_SNAKE_CASE		)

              else:
                     UpperCamelCase_ 							=	AdamW(
                         _SCREAMING_SNAKE_CASE       , lr=self.hparams.learning_rate       , eps=self.hparams.adam_epsilon		)
              UpperCamelCase_ 							=	optimizer

              UpperCamelCase_ 							=	self.get_lr_scheduler()

              return [optimizer], [scheduler]

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			Union[str, Any]:




              """simple docstring"""


              return self.validation_step(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE		)

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[int]		)							->			List[Any]:




              """simple docstring"""


              return self.validation_end(_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_ 							=	max(1       , self.hparams.gpus		)  # TODO: consider num_tpu_cores
              UpperCamelCase_ 							=	self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
              return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			List[str]:




              """simple docstring"""


              if mode == "test":
                     UpperCamelCase_ 							=	len(self.test_dataloader().dataset		)
              else:
                     UpperCamelCase_ 							=	self.get_dataloader("train"       , self.hparams.train_batch_size       , shuffle=_SCREAMING_SNAKE_CASE		)
                     UpperCamelCase_ 							=	len(self.train_dataloader().dataset		)

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




              """simple docstring"""


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

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




              """simple docstring"""


              return self.train_loader

       def lowercase			(		self:			Tuple		)							->			Optional[Any]:




              """simple docstring"""


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

       def lowercase			(		self:			Dict		)							->			List[Any]:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Dict:




              """simple docstring"""


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

       @pl.utilities.rank_zero_only
       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			Dict[str, Any]		)							->			None:




              """simple docstring"""


              UpperCamelCase_ 							=	self.output_dir.joinpath("best_tfmr"		)
              UpperCamelCase_ 							=	self.step_count
              self.model.save_pretrained(_SCREAMING_SNAKE_CASE		)
              self.tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)



       @staticmethod
       def lowercase			(		_SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			List[Any]		)							->			Tuple:




              """simple docstring"""


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



class 				_UpperCamelCase       (						pl.Callback  ):

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			int		)							->			List[str]:




              """simple docstring"""


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

       def lowercase			(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			pl.Trainer       , _SCREAMING_SNAKE_CASE:			pl.LightningModule		)							->			Tuple:




              """simple docstring"""


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



       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			pl.Trainer       , _SCREAMING_SNAKE_CASE:			pl.LightningModule		)							->			int:




              """simple docstring"""


              rank_zero_info("***** Test results *****"		)
              UpperCamelCase_ 							=	trainer.callback_metrics
              # Log and save results to file
              UpperCamelCase_ 							=	os.path.join(pl_module.hparams.output_dir       , "test_results.txt"		)
              with open(_SCREAMING_SNAKE_CASE       , "w"		) as writer:
                     for key in sorted(_SCREAMING_SNAKE_CASE		):
                            if key not in ["log", "progress_bar"]:
                                   rank_zero_info("{} = {}\n".format(_SCREAMING_SNAKE_CASE       , str(metrics[key]		)		)		)
                                   writer.write("{} = {}\n".format(_SCREAMING_SNAKE_CASE       , str(metrics[key]		)		)		)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		None:
       #  To allow all pl args uncomment the following line
       #  parser = pl.Trainer.add_argparse_args(parser)
       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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=None       ,		UpperCamelCase_=True       ,		UpperCamelCase_=[]       ,		UpperCamelCase_=None       ,		UpperCamelCase_=None       ,		**UpperCamelCase_       ,		)  ->		Tuple:
       pl.seed_everything(args.seed	)

       # init model
       UpperCamelCase_ 							=	Path(model.hparams.output_dir	)
       odir.mkdir(exist_ok=UpperCamelCase_	)

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

       UpperCamelCase_ 							=	{}

       # TODO: remove with PyTorch 1.6 since pl uses native amp
       if args.fpaa:
              UpperCamelCase_ 							=	16
              UpperCamelCase_ 							=	args.fpaa_opt_level

       if args.gpus > 1:
              UpperCamelCase_ 							=	"ddp"

       UpperCamelCase_ 							=	args.accumulate_grad_batches
       UpperCamelCase_ 							=	extra_train_kwargs.get("accelerator"       ,		UpperCamelCase_	)
       UpperCamelCase_ 							=	extra_train_kwargs.get("profiler"       ,		UpperCamelCase_	)

       UpperCamelCase_ 							=	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






class 				lowerCAmelCase__	:







					def __init__(							self   :  Union[str, Any]):
											A__						:				Optional[int]         =			[0]
											A__						:				Any         =			0







					def 			_lowercase      (							self   :  Optional[int]      ,		_A   :  int):
											A__						:				Optional[Any]         =			self.__heap[i]
											while i // 2 > 0:
																	if self.__heap[i] > self.__heap[i // 2]:
																							A__						:				Union[str, Any]         =			self.__heap[i // 2]
																							A__						:				List[str]         =			temporary
																	i //= 2







					def 			_lowercase      (							self   :  Union[str, Any]      ,		_A   :  int):
											self.__heap.append(_A)
											self.__size += 1
											self.__swap_up(self.__size)







					def 			_lowercase      (							self   :  Any      ,		_A   :  int):
											while self.__size >= 2 * i:
																	if 2 * i + 1 > self.__size:
																							A__						:				str         =			2 * i
																	else:
																							if self.__heap[2 * i] > self.__heap[2 * i + 1]:
																													A__						:				Tuple         =			2 * i
																							else:
																													A__						:				Tuple         =			2 * i + 1
																	A__						:				Any         =			self.__heap[i]
																	if self.__heap[i] < self.__heap[bigger_child]:
																							A__						:				List[str]         =			self.__heap[bigger_child]
																							A__						:				Tuple         =			temporary
																	A__						:				Tuple         =			bigger_child







					def 			_lowercase      (							self   :  int):
											A__						:				str         =			self.__heap[1]
											A__						:				Tuple         =			self.__heap[self.__size]
											self.__size -= 1
											self.__heap.pop()
											self.__swap_down(1)
											return max_value







					@property
					def 			_lowercase      (							self   :  Any):
											return self.__heap[1:]







					def __len__(							self   :  Union[str, Any]):
											return self.__size


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		# create an instance of BinaryHeap
		snake_case     :    List[Any]						=    BinaryHeap()
		binary_heap.insert(6)
		binary_heap.insert(1_0)
		binary_heap.insert(1_5)
		binary_heap.insert(1_2)
		# pop root(max-values because it is max heap)
		print(binary_heap.pop())  # 15
		print(binary_heap.pop())  # 12
		# get the list and size after operations
		print(binary_heap.get_list)
		print(len(binary_heap))