

"""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__						=	logging.getLogger(__name__)

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

lowerCAmelCase__						=	{
    "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__						=	{
    "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__						=	sorted(arg_to_scheduler.keys())
lowerCAmelCase__						=	"{" + ", ".join(arg_to_scheduler_choices) + "}"

class    a__  (   pl.LightningModule     ):
			"""simple docstring"""







			def __init__(      self , lowercase , lowercase=None , lowercase="base" , lowercase=None , lowercase=None , lowercase=None , **lowercase , )					->  Dict:
							'''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(UpperCamelCase_    )
							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=UpperCamelCase_ , **UpperCamelCase_ , )
							else:
											A__            =     config

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

							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=UpperCamelCase_ , )
							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=UpperCamelCase_ , )
							else:
											A__            =     model


			def        UpperCamelCase						(      self , *lowercase , **lowercase    )					->  Tuple:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Any:
							'''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    )					->  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(
											    UpperCamelCase_ , lr=self.hparams.learning_rate , scale_parameter=UpperCamelCase_ , relative_step=UpperCamelCase_    )

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

							A__            =     self.get_lr_scheduler()

							return [optimizer], [scheduler]


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  str:
							'''simple docstring'''






							return self.validation_step(UpperCamelCase_ , UpperCamelCase_    )


			def        UpperCamelCase						(      self , lowercase    )					->  Any:
							'''simple docstring'''






							return self.validation_end(UpperCamelCase_    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''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 , lowercase    )					->  int:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase = False    )					->  List[Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							return self.train_loader


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase    )					->  Optional[Any]:
							'''simple docstring'''






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


			@pl.utilities.rank_zero_only
			def        UpperCamelCase						(      self , lowercase    )					->  str:
							'''simple docstring'''






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


			@staticmethod
			def        UpperCamelCase						(      lowercase , lowercase    )					->  Any:
							'''simple docstring'''






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

class    a__  (   pl.Callback     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  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(UpperCamelCase_    )


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  List[Any]:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  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(UpperCamelCase_ , "w"    ) as writer:
											for key in sorted(UpperCamelCase_    ):
															if key not in ["log", "progress_bar"]:
																			rank_zero_info("{} = {}\n".format(UpperCamelCase_ , str(metrics[key]    )    )    )
																			writer.write("{} = {}\n".format(UpperCamelCase_ , str(metrics[key]    )    )    )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							int		,    SCREAMING_SNAKE_CASE_:							Optional[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    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							BaseTransformer		,    SCREAMING_SNAKE_CASE_:							argparse.Namespace		,    SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,    SCREAMING_SNAKE_CASE_:							int=True		,    SCREAMING_SNAKE_CASE_:							Dict=[]		,    SCREAMING_SNAKE_CASE_:							List[Any]=None		,    SCREAMING_SNAKE_CASE_:							Optional[Any]=None		,    **SCREAMING_SNAKE_CASE_:							Optional[Any]		,    )     -> List[str]:




				'''simple docstring'''





				pl.seed_everything(args.seed )

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

				# 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(__lowercase )
				if logging_callback is None:
								A__            =     LoggingCallback()

				A__            =     {}

				# TODO: remove with PyTorch 1.6 since pl uses native amp
				if args.fpaa:
								A__            =     1_6
								A__            =     args.fpaa_opt_level

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

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

				A__            =     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




from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> int:




				'''simple docstring'''





				return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(SCREAMING_SNAKE_CASE_		,    -1 )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> str:




				'''simple docstring'''





				if not balanced_parentheses(SCREAMING_SNAKE_CASE_ ):
								raise ValueError("Mismatched parentheses" )
				A__            =     Stack()
				A__            =     []
				for char in expression_str:
								if char.isalpha() or char.isdigit():
												postfix.append(SCREAMING_SNAKE_CASE_ )
								elif char == "(":
												stack.push(SCREAMING_SNAKE_CASE_ )
								elif char == ")":
												while not stack.is_empty() and stack.peek() != "(":
																postfix.append(stack.pop() )
												stack.pop()
								else:
												while not stack.is_empty() and precedence(SCREAMING_SNAKE_CASE_ ) <= precedence(stack.peek() ):
																postfix.append(stack.pop() )
												stack.push(SCREAMING_SNAKE_CASE_ )
				while not stack.is_empty():
								postfix.append(stack.pop() )
				return " ".join(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	lowerCAmelCase__						=	"""a+b*(c^d-e)^(f+g*h)-i"""

	print("""Infix to Postfix Notation demonstration:\n""")
	print("""Infix notation: """ + expression)
	print("""Postfix notation: """ + infix_to_postfix(expression))

