

"""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


__A		     =    logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

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





class 					snake_case			(       pl.LightningModule						):

				def __init__(			self							:       Dict  ,						UpperCamelCase__							:       argparse.Namespace  ,						UpperCamelCase__							:       List[str]=None  ,						UpperCamelCase__							:       Optional[Any]="base"  ,						UpperCamelCase__							:       int=None  ,						UpperCamelCase__							:       str=None  ,						UpperCamelCase__							:       Optional[int]=None  ,						**UpperCamelCase__							:       Union[str, Any]  ,						)->     int:



						'''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__)
						__lowerCAmelCase: 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: Optional[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=UpperCamelCase__  ,						**UpperCamelCase__  ,						)
						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  ,						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:
								__lowerCAmelCase: Union[str, Any]					   =    AutoTokenizer.from_pretrained(
								    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path  ,						cache_dir=UpperCamelCase__  ,						)
						else:
								__lowerCAmelCase: PreTrainedTokenizer					   =    tokenizer
						__lowerCAmelCase: 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=UpperCamelCase__  ,						)
						else:
								__lowerCAmelCase: Dict					   =    model

				def 							lowercase_  (			self							:       int  ,						*UpperCamelCase__							:       Dict  ,						**UpperCamelCase__							:       Union[str, Any])->     Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase: str					   =    self.model_type.from_pretrained(*UpperCamelCase__  ,						**UpperCamelCase__)

				def 							lowercase_  (			self							:       Dict)->     Dict:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       Optional[int])->     str:



						'''simple docstring'''


						__lowerCAmelCase: Union[str, Any]					   =    self.model
						__lowerCAmelCase: Optional[int]					   =    ["bias", "LayerNorm.weight"]
						__lowerCAmelCase: 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: Union[str, Any]					   =    Adafactor(
								    UpperCamelCase__  ,						lr=self.hparams.learning_rate  ,						scale_parameter=UpperCamelCase__  ,						relative_step=UpperCamelCase__)

						else:
								__lowerCAmelCase: Any					   =    AdamW(
								    UpperCamelCase__  ,						lr=self.hparams.learning_rate  ,						eps=self.hparams.adam_epsilon)
						__lowerCAmelCase: str					   =    optimizer

						__lowerCAmelCase: Union[str, Any]					   =    self.get_lr_scheduler()

						return [optimizer], [scheduler]

				def 							lowercase_  (			self							:       List[Any]  ,						UpperCamelCase__							:       List[Any]  ,						UpperCamelCase__							:       List[Any])->     List[str]:



						'''simple docstring'''


						return self.validation_step(UpperCamelCase__  ,						UpperCamelCase__)

				def 							lowercase_  (			self							:       Tuple  ,						UpperCamelCase__							:       Optional[int])->     Optional[Any]:



						'''simple docstring'''


						return self.validation_end(UpperCamelCase__)

				def 							lowercase_  (			self							:       int)->     int:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    max(1  ,						self.hparams.gpus)  # TODO: consider num_tpu_cores
						__lowerCAmelCase: Dict					   =    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							:       Optional[int]  ,						UpperCamelCase__							:       Union[str, Any])->     str:



						'''simple docstring'''


						if stage == "test":
								__lowerCAmelCase: Optional[Any]					   =    len(self.test_dataloader().dataset)
						else:
								__lowerCAmelCase: Any					   =    self.get_dataloader("train"  ,						self.hparams.train_batch_size  ,						shuffle=UpperCamelCase__)
								__lowerCAmelCase: int					   =    len(self.train_dataloader().dataset)

				def 							lowercase_  (			self							:       int  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       int  ,						UpperCamelCase__							:       bool = False)->     Optional[int]:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       int)->     Any:



						'''simple docstring'''


						return self.train_loader

				def 							lowercase_  (			self							:       Union[str, Any])->     Tuple:



						'''simple docstring'''


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

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



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       Any)->     Union[str, 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 							lowercase_  (			self							:       Optional[Any]  ,						UpperCamelCase__							:       Dict[str, Any])->     None:



						'''simple docstring'''


						__lowerCAmelCase: List[str]					   =    self.output_dir.joinpath("best_tfmr")
						__lowerCAmelCase: Dict					   =    self.step_count
						self.model.save_pretrained(UpperCamelCase__)
						self.tokenizer.save_pretrained(UpperCamelCase__)




				@staticmethod
				def 							lowercase_  (			UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Union[str, Any])->     Optional[int]:



						'''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=3_2  ,						type=UpperCamelCase__)
						parser.add_argument("--eval_batch_size"  ,						default=3_2  ,						type=UpperCamelCase__)
						parser.add_argument("--adafactor"  ,						action="store_true")





class 					snake_case			(       pl.Callback						):

				def 							lowercase_  (			self							:       List[Any]  ,						UpperCamelCase__							:       str  ,						UpperCamelCase__							:       Optional[int])->     Dict:



						'''simple docstring'''


						if (
						    trainer.is_global_zero and trainer.global_rank == 0
						):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
								pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class 					snake_case			(       pl.Callback						):

				def 							lowercase_  (			self							:       List[str]  ,						UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       Optional[Any])->     int:



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    trainer.lr_schedulers[0]["scheduler"]
						__lowerCAmelCase: int					   =    {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
						pl_module.logger.log_metrics(UpperCamelCase__)

				def 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       pl.Trainer  ,						UpperCamelCase__							:       pl.LightningModule)->     Optional[Any]:



						'''simple docstring'''


						rank_zero_info("***** Validation results *****")
						__lowerCAmelCase: Tuple					   =    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 							lowercase_  (			self							:       Dict  ,						UpperCamelCase__							:       pl.Trainer  ,						UpperCamelCase__							:       pl.LightningModule)->     str:



						'''simple docstring'''


						rank_zero_info("***** Test results *****")
						__lowerCAmelCase: Tuple					   =    trainer.callback_metrics
						# Log and save results to file
						__lowerCAmelCase: List[str]					   =    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 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			None:
		#  To allow all pl args uncomment the following line
		#  parser = pl.Trainer.add_argparse_args(parser)
		parser.add_argument(
		    "--output_dir"      ,				default=__SCREAMING_SNAKE_CASE      ,				type=__SCREAMING_SNAKE_CASE      ,				required=__SCREAMING_SNAKE_CASE      ,				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=__SCREAMING_SNAKE_CASE      ,				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=__SCREAMING_SNAKE_CASE				)
		parser.add_argument("--max_grad_norm"      ,				dest="gradient_clip_val"      ,				default=1.0      ,				type=__SCREAMING_SNAKE_CASE      ,				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=__SCREAMING_SNAKE_CASE      ,				default=1      ,				help="Number of updates steps to accumulate before performing a backward/update pass."      ,				)
		parser.add_argument("--seed"      ,				type=__SCREAMING_SNAKE_CASE      ,				default=4_2      ,				help="random seed for initialization"				)
		parser.add_argument(
		    "--data_dir"      ,				default=__SCREAMING_SNAKE_CASE      ,				type=__SCREAMING_SNAKE_CASE      ,				required=__SCREAMING_SNAKE_CASE      ,				help="The input data dir. Should contain the training files for the CoNLL-2003 NER task."      ,				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE=None      ,				__SCREAMING_SNAKE_CASE=True      ,				__SCREAMING_SNAKE_CASE=None      ,				__SCREAMING_SNAKE_CASE=[]      ,				__SCREAMING_SNAKE_CASE=None      ,				__SCREAMING_SNAKE_CASE=None      ,				**__SCREAMING_SNAKE_CASE      ,				)			->			Union[str, Any]:
		pl.seed_everything(args.seed				)

		# init model
		__lowerCAmelCase: Any					   =    Path(model.hparams.output_dir				)
		odir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE				)

		# add custom checkpoints
		if checkpoint_callback is None:
				__lowerCAmelCase: Union[str, Any]					   =    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(__SCREAMING_SNAKE_CASE				)
		if logging_callback is None:
				__lowerCAmelCase: List[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
				# train_params["amp_level"] = args.fp16_opt_level

		if args.gpus > 1:
				__lowerCAmelCase: int					   =    "auto"  # "ddp"
				__lowerCAmelCase: Optional[int]					   =    "ddp"

		__lowerCAmelCase: str					   =    args.accumulate_grad_batches
		__lowerCAmelCase: Optional[Any]					   =    None  # extra_train_kwargs.get("profiler", None) #get unwanted logs
		__lowerCAmelCase: Union[str, Any]					   =    "auto"

		__lowerCAmelCase: Any					   =    pl.Trainer.from_argparse_args(
		    __SCREAMING_SNAKE_CASE      ,				weights_summary=__SCREAMING_SNAKE_CASE      ,				callbacks=[logging_callback] + extra_callbacks + [checkpoint_callback] + [InitCallback()]      ,				logger=__SCREAMING_SNAKE_CASE      ,				**__SCREAMING_SNAKE_CASE      ,				)

		if args.do_train:
				trainer.fit(__SCREAMING_SNAKE_CASE				)

		return trainer





"""simple docstring"""


import math






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			list:
		if n <= 0:
				raise ValueError("Only positive integers have prime factors"				)
		__lowerCAmelCase: Tuple					   =    []
		while n % 2 == 0:
				pf.append(2				)
				__lowerCAmelCase: str					   =    int(n / 2				)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				while n % i == 0:
						pf.append(__SCREAMING_SNAKE_CASE				)
						__lowerCAmelCase: List[Any]					   =    int(n / i				)
		if n > 2:
				pf.append(__SCREAMING_SNAKE_CASE				)
		return pf






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: int					   =    1
		__lowerCAmelCase: str					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Dict					   =    int(n / 2				)
		div *= temp
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: int					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: Tuple					   =    int(n / i				)
				div *= temp
		if n > 1:
				div *= 2
		return div






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		if n <= 0:
				raise ValueError("Only positive numbers are accepted"				)
		__lowerCAmelCase: Dict					   =    1
		__lowerCAmelCase: int					   =    1
		while n % 2 == 0:
				temp += 1
				__lowerCAmelCase: Tuple					   =    int(n / 2				)
		if temp > 1:
				s *= (2**temp - 1) / (2 - 1)
		for i in range(3      ,				int(math.sqrt(__SCREAMING_SNAKE_CASE				)				) + 1      ,				2				):
				__lowerCAmelCase: List[Any]					   =    1
				while n % i == 0:
						temp += 1
						__lowerCAmelCase: List[str]					   =    int(n / i				)
				if temp > 1:
						s *= (i**temp - 1) / (i - 1)
		return int(__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE				)			->			int:
		__lowerCAmelCase: Optional[int]					   =    n
		for x in set(prime_factors(__SCREAMING_SNAKE_CASE				)				):
				s *= (x - 1) / x
		return int(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


