


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 					lowerCAmelCase__	(    pl.LightningModule):




     '''simple docstring'''


     def __init__(						self			,						__lowerCamelCase			,						__lowerCamelCase=None			,						__lowerCamelCase="base"			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						__lowerCamelCase=None			,						**__lowerCamelCase			,						)      ->    str:
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading

            self.save_hyperparameters(lowercase_)
            _A				:	Tuple       =			0
            _A				:	Tuple       =			Path(self.hparams.output_dir)
            _A				:	str       =			self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _A				:	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=lowercase_			,						**lowercase_			,						)
            else:
                   _A				:	Optional[int]       =			config

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

            if tokenizer is None:
                   _A				:	Optional[int]       =			AutoTokenizer.from_pretrained(
                       self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path			,						cache_dir=lowercase_			,						)
            else:
                   _A				:	List[str]       =			tokenizer
            _A				:	Any       =			MODEL_MODES[mode]
            if model is None:
                   _A				:	int       =			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=lowercase_			,						)
            else:
                   _A				:	int       =			model


     def 			_lowerCamelCase			(						self			,						*__lowerCamelCase			,						**__lowerCamelCase)      ->    Tuple:
            _A				:	Union[str, Any]       =			self.model_type.from_pretrained(*lowercase_			,						**lowercase_)


     def 			_lowerCamelCase			(						self)      ->    Optional[Any]:
            _A				:	Dict       =			arg_to_scheduler[self.hparams.lr_scheduler]
            _A				:	Dict       =			get_schedule_func(
                self.opt			,						num_warmup_steps=self.hparams.warmup_steps			,						num_training_steps=self.total_steps())
            _A				:	Tuple       =			{"scheduler": scheduler, "interval": "step", "frequency": 1}
            return scheduler


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            _A				:	Optional[int]       =			self.model
            _A				:	str       =			["bias", "LayerNorm.weight"]
            _A				:	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:
                   _A				:	List[Any]       =			Adafactor(
                       lowercase_			,						lr=self.hparams.learning_rate			,						scale_parameter=lowercase_			,						relative_step=lowercase_)

            else:
                   _A				:	Tuple       =			AdamW(
                       lowercase_			,						lr=self.hparams.learning_rate			,						eps=self.hparams.adam_epsilon)
            _A				:	Tuple       =			optimizer

            _A				:	Tuple       =			self.get_lr_scheduler()

            return [optimizer], [scheduler]


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    List[Any]:
            return self.validation_step(lowercase_			,						lowercase_)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Optional[Any]:
            return self.validation_end(lowercase_)


     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	str       =			max(1			,						self.hparams.gpus)  # TODO: consider num_tpu_cores
            _A				:	List[str]       =			self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
            return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Optional[int]:
            if mode == "test":
                   _A				:	Dict       =			len(self.test_dataloader().dataset)
            else:
                   _A				:	List[str]       =			self.get_dataloader("train"			,						self.hparams.train_batch_size			,						shuffle=lowercase_)
                   _A				:	str       =			len(self.train_dataloader().dataset)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase			,						__lowerCamelCase = False)      ->    Any:
            raise NotImplementedError("You must implement this for your task")


     def 			_lowerCamelCase			(						self)      ->    Dict:
            return self.train_loader


     def 			_lowerCamelCase			(						self)      ->    List[str]:
            return self.get_dataloader("dev"			,						self.hparams.eval_batch_size			,						shuffle=lowercase_)


     def 			_lowerCamelCase			(						self)      ->    Tuple:
            return self.get_dataloader("test"			,						self.hparams.eval_batch_size			,						shuffle=lowercase_)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    List[str]:
            return os.path.join(
                self.hparams.data_dir			,						"cached_{}_{}_{}".format(
                    lowercase_			,						list(filter(lowercase_			,						self.hparams.model_name_or_path.split("/"))).pop()			,						str(self.hparams.max_seq_length)			,						)			,						)


     @pl.utilities.rank_zero_only
     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    None:
            _A				:	List[Any]       =			self.output_dir.joinpath("best_tfmr")
            _A				:	Tuple       =			self.step_count
            self.model.save_pretrained(lowercase_)
            self.tokenizer.save_pretrained(lowercase_)


     @staticmethod
     def 			_lowerCamelCase			(						__lowerCamelCase			,						__lowerCamelCase)      ->    Dict:
            parser.add_argument(
                "--model_name_or_path"			,						default=lowercase_			,						type=lowercase_			,						required=lowercase_			,						help="Path to pretrained model or model identifier from huggingface.co/models"			,						)
            parser.add_argument(
                "--config_name"			,						default=""			,						type=lowercase_			,						help="Pretrained config name or path if not the same as model_name")
            parser.add_argument(
                "--tokenizer_name"			,						default=lowercase_			,						type=lowercase_			,						help="Pretrained tokenizer name or path if not the same as model_name"			,						)
            parser.add_argument(
                "--cache_dir"			,						default=""			,						type=lowercase_			,						help="Where do you want to store the pre-trained models downloaded from huggingface.co"			,						)
            parser.add_argument(
                "--encoder_layerdrop"			,						type=lowercase_			,						help="Encoder layer dropout probability (Optional). Goes into model.config"			,						)
            parser.add_argument(
                "--decoder_layerdrop"			,						type=lowercase_			,						help="Decoder layer dropout probability (Optional). Goes into model.config"			,						)
            parser.add_argument(
                "--dropout"			,						type=lowercase_			,						help="Dropout probability (Optional). Goes into model.config"			,						)
            parser.add_argument(
                "--attention_dropout"			,						type=lowercase_			,						help="Attention dropout probability (Optional). Goes into model.config"			,						)
            parser.add_argument("--learning_rate"			,						default=5e-5			,						type=lowercase_			,						help="The initial learning rate for Adam.")
            parser.add_argument(
                "--lr_scheduler"			,						default="linear"			,						choices=lowercase_			,						metavar=lowercase_			,						type=lowercase_			,						help="Learning rate scheduler"			,						)
            parser.add_argument("--weight_decay"			,						default=0.0			,						type=lowercase_			,						help="Weight decay if we apply some.")
            parser.add_argument("--adam_epsilon"			,						default=1e-8			,						type=lowercase_			,						help="Epsilon for Adam optimizer.")
            parser.add_argument("--warmup_steps"			,						default=0			,						type=lowercase_			,						help="Linear warmup over warmup_steps.")
            parser.add_argument("--num_workers"			,						default=4			,						type=lowercase_			,						help="kwarg passed to DataLoader")
            parser.add_argument("--num_train_epochs"			,						dest="max_epochs"			,						default=3			,						type=lowercase_)
            parser.add_argument("--train_batch_size"			,						default=3_2			,						type=lowercase_)
            parser.add_argument("--eval_batch_size"			,						default=3_2			,						type=lowercase_)
            parser.add_argument("--adafactor"			,						action="store_true")







class 					lowerCAmelCase__	(    pl.Callback):




     '''simple docstring'''


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    int:
            _A				:	List[str]       =			trainer.lr_schedulers[0]["scheduler"]
            _A				:	Any       =			{F"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
            pl_module.logger.log_metrics(lowercase_)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase			,						__lowerCamelCase)      ->    Any:
            rank_zero_info("***** Validation results *****")
            _A				:	int       =			trainer.callback_metrics
            # Log results
            for key in sorted(lowercase_):
                   if key not in ["log", "progress_bar"]:
                          rank_zero_info("{} = {}\n".format(lowercase_			,						str(metrics[key])))


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




def 							_UpperCAmelCase    (UpperCamelCase__					:  int    ,							UpperCamelCase__					:  Any						):
       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 							_UpperCAmelCase    (UpperCamelCase__					:  BaseTransformer    ,							UpperCamelCase__					:  argparse.Namespace    ,							UpperCamelCase__					:  Optional[int]=None    ,							UpperCamelCase__					:  Optional[int]=True    ,							UpperCamelCase__					:  Union[str, Any]=[]    ,							UpperCamelCase__					:  Any=None    ,							UpperCamelCase__					:  int=None    ,							**UpperCamelCase__					:  Union[str, Any]    ,							):
       pl.seed_everything(args.seed						)

       # init model
       _A				:	Dict       =			Path(model.hparams.output_dir						)
       odir.mkdir(exist_ok=UpperCamelCase__						)

       # add custom checkpoints
       if checkpoint_callback is None:
              _A				:	int       =			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:
              _A				:	List[str]       =			LoggingCallback()

       _A				:	Any       =			{}

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

       if args.gpus > 1:
              _A				:	List[str]       =			"ddp"

       _A				:	str       =			args.accumulate_grad_batches
       _A				:	Tuple       =			extra_train_kwargs.get("accelerator"    ,							UpperCamelCase__						)
       _A				:	Optional[int]       =			extra_train_kwargs.get("profiler"    ,							UpperCamelCase__						)

       _A				:	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




from itertools import permutations
def 							_UpperCAmelCase    ():
       _A				:	List[Any]       =			list(map("".join    ,							permutations("0123456789"						)						)						)
       return result[999999]


if __name__ == "__main__":
    print(solution())
