

"""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							:       Optional[int]  ,						UpperCamelCase__							:       argparse.Namespace  ,						UpperCamelCase__							:       List[str]=None  ,						UpperCamelCase__							:       List[str]="base"  ,						UpperCamelCase__							:       List[Any]=None  ,						UpperCamelCase__							:       Optional[Any]=None  ,						UpperCamelCase__							:       Optional[Any]=None  ,						**UpperCamelCase__							:       Any  ,						)->     Optional[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: Tuple					   =    0
						__lowerCAmelCase: Optional[int]					   =    Path(self.hparams.output_dir)
						__lowerCAmelCase: Dict					   =    self.hparams.cache_dir if self.hparams.cache_dir else None
						if config is None:
								__lowerCAmelCase: str					   =    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: Optional[int]					   =    ("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: Dict					   =    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: Any					   =    MODEL_MODES[mode]
						if model is None:
								__lowerCAmelCase: Union[str, 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: Tuple					   =    model

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



						'''simple docstring'''


						__lowerCAmelCase: Optional[Any]					   =    self.model_type.from_pretrained(*UpperCamelCase__  ,						**UpperCamelCase__)

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



						'''simple docstring'''


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

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



						'''simple docstring'''


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

						else:
								__lowerCAmelCase: Tuple					   =    AdamW(
								    UpperCamelCase__  ,						lr=self.hparams.learning_rate  ,						eps=self.hparams.adam_epsilon)
						__lowerCAmelCase: Optional[Any]					   =    optimizer

						__lowerCAmelCase: int					   =    self.get_lr_scheduler()

						return [optimizer], [scheduler]

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



						'''simple docstring'''


						return self.validation_step(UpperCamelCase__  ,						UpperCamelCase__)

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



						'''simple docstring'''


						return self.validation_end(UpperCamelCase__)

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



						'''simple docstring'''


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



						'''simple docstring'''


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

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



						'''simple docstring'''


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

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



						'''simple docstring'''


						return self.train_loader

				def 							lowercase_  (			self							:       List[str])->     Dict:



						'''simple docstring'''


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

				def 							lowercase_  (			self							:       Tuple)->     Tuple:



						'''simple docstring'''


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

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



						'''simple docstring'''


						__lowerCAmelCase: List[Any]					   =    self.output_dir.joinpath("best_tfmr")
						__lowerCAmelCase: Optional[int]					   =    self.step_count
						self.model.save_pretrained(UpperCamelCase__)
						self.tokenizer.save_pretrained(UpperCamelCase__)




				@staticmethod
				def 							lowercase_  (			UpperCamelCase__							:       Dict  ,						UpperCamelCase__							:       List[Any])->     List[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=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							:       Any  ,						UpperCamelCase__							:       List[str]  ,						UpperCamelCase__							:       Tuple)->     Union[str, Any]:



						'''simple docstring'''


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

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



						'''simple docstring'''


						rank_zero_info("***** Validation results *****")
						__lowerCAmelCase: List[str]					   =    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							:       Any  ,						UpperCamelCase__							:       pl.Trainer  ,						UpperCamelCase__							:       pl.LightningModule)->     Optional[int]:



						'''simple docstring'''


						rank_zero_info("***** Test results *****")
						__lowerCAmelCase: Union[str, Any]					   =    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=[]      ,				__SCREAMING_SNAKE_CASE=None      ,				__SCREAMING_SNAKE_CASE=None      ,				**__SCREAMING_SNAKE_CASE      ,				)			->			List[Any]:
		pl.seed_everything(args.seed				)

		# init model
		__lowerCAmelCase: Optional[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: Tuple					   =    LoggingCallback()

		__lowerCAmelCase: List[Any]					   =    {}

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

		if args.gpus > 1:
				__lowerCAmelCase: Any					   =    "ddp"

		__lowerCAmelCase: List[str]					   =    args.accumulate_grad_batches
		__lowerCAmelCase: Tuple					   =    extra_train_kwargs.get("accelerator"      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: List[Any]					   =    extra_train_kwargs.get("profiler"      ,				__SCREAMING_SNAKE_CASE				)

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

		if args.do_train:
				trainer.fit(__SCREAMING_SNAKE_CASE				)

		return trainer





"""simple docstring"""


from __future__ import annotations






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[float, float]:
		assert (
		    c % greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				) == 0
		)  # greatest_common_divisor(a,b) function implemented below
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # extended_gcd(a,b) function implemented below
		__lowerCAmelCase: Optional[int]					   =    c / d
		return (r * x, r * y)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE = 2				)			->			None:
		((__lowerCAmelCase)   ,				(__lowerCAmelCase)): Optional[Any]					   =    diophantine(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)  # Initial value
		__lowerCAmelCase: Optional[Any]					   =    greatest_common_divisor(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)
		__lowerCAmelCase: Any					   =    a // d
		__lowerCAmelCase: str					   =    b // d

		for i in range(__SCREAMING_SNAKE_CASE				):
				__lowerCAmelCase: Dict					   =    xa + i * q
				__lowerCAmelCase: List[str]					   =    ya - i * p
				print(__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			int:
		if a < b:
				__lowerCAmelCase   ,				__lowerCAmelCase: Any					   =    b, a

		while a % b != 0:
				__lowerCAmelCase   ,				__lowerCAmelCase: List[Any]					   =    b, a % b

		return b






def 					a__					(					__SCREAMING_SNAKE_CASE      ,				__SCREAMING_SNAKE_CASE				)			->			tuple[int, int, int]:
		assert a >= 0 and b >= 0

		if b == 0:
				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase: List[str]					   =    a, 1, 0
		else:
				((__lowerCAmelCase)   ,				(__lowerCAmelCase)   ,				(__lowerCAmelCase)): Dict					   =    extended_gcd(__SCREAMING_SNAKE_CASE      ,				a % b				)
				__lowerCAmelCase: Dict					   =    q
				__lowerCAmelCase: int					   =    p - q * (a // b)

		assert a % d == 0 and b % d == 0
		assert d == a * x + b * y

		return (d, x, y)


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

			testmod(name="diophantine", verbose=True)
			testmod(name="diophantine_all_soln", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)


