


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			,						)      ->    Any:
            super().__init__()
            # TODO: move to self.save_hyperparameters()
            # self.save_hyperparameters()
            # can also expand arguments into trainer signature for easier reading

            self.save_hyperparameters(__lowerCamelCase)
            _A				:	List[str]       =			0
            _A				:	List[str]       =			Path(self.hparams.output_dir)
            _A				:	int       =			self.hparams.cache_dir if self.hparams.cache_dir else None
            if config is None:
                   _A				:	Union[str, 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=__lowerCamelCase			,						**__lowerCamelCase			,						)
            else:
                   _A				:	PretrainedConfig       =			config

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

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


     def 			_lowerCamelCase			(						self			,						*__lowerCamelCase			,						**__lowerCamelCase)      ->    Optional[int]:
            _A				:	int       =			self.model_type.from_pretrained(*__lowerCamelCase			,						**__lowerCamelCase)


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


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

            else:
                   _A				:	Optional[int]       =			AdamW(
                       __lowerCamelCase			,						lr=self.hparams.learning_rate			,						eps=self.hparams.adam_epsilon)
            _A				:	Dict       =			optimizer

            _A				:	str       =			self.get_lr_scheduler()

            return [optimizer], [scheduler]


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


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    int:
            return self.validation_end(__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    int:
            _A				:	str       =			max(1			,						self.hparams.gpus)  # TODO: consider num_tpu_cores
            _A				:	Tuple       =			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)      ->    Tuple:
            if mode == "test":
                   _A				:	Dict       =			len(self.test_dataloader().dataset)
            else:
                   _A				:	Tuple       =			self.get_dataloader("train"			,						self.hparams.train_batch_size			,						shuffle=__lowerCamelCase)
                   _A				:	Union[str, Any]       =			len(self.train_dataloader().dataset)


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


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


     def 			_lowerCamelCase			(						self)      ->    Dict:
            return self.get_dataloader("dev"			,						self.hparams.eval_batch_size			,						shuffle=__lowerCamelCase)


     def 			_lowerCamelCase			(						self)      ->    List[Any]:
            return self.get_dataloader("test"			,						self.hparams.eval_batch_size			,						shuffle=__lowerCamelCase)


     def 			_lowerCamelCase			(						self			,						__lowerCamelCase)      ->    Tuple:
            return os.path.join(
                self.hparams.data_dir			,						"cached_{}_{}_{}".format(
                    __lowerCamelCase			,						list(filter(__lowerCamelCase			,						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				:	Optional[Any]       =			self.output_dir.joinpath("best_tfmr")
            _A				:	Any       =			self.step_count
            self.model.save_pretrained(__lowerCamelCase)
            self.tokenizer.save_pretrained(__lowerCamelCase)


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







class 					lowerCAmelCase__    (    pl.Callback):




     '''simple docstring'''


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


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


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




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

       # init model
       _A				:	Optional[Any]       =			Path(model.hparams.output_dir						)
       odir.mkdir(exist_ok=UpperCamelCase__						)

       # add custom checkpoints
       if checkpoint_callback is None:
              _A				:	List[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(UpperCamelCase__						)
       if logging_callback is None:
              _A				:	Optional[Any]       =			LoggingCallback()

       _A				:	Dict       =			{}

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

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

       _A				:	List[Any]       =			args.accumulate_grad_batches
       _A				:	Dict       =			extra_train_kwargs.get("accelerator"    ,							UpperCamelCase__						)
       _A				:	int       =			extra_train_kwargs.get("profiler"    ,							UpperCamelCase__						)

       _A				:	List[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




def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       if right <= left:
              return
       _A				:	str       =			left
       _A				:	List[str]       =			right
       _A				:	Union[str, Any]       =			sorting[left]
       while i <= b:
              if sorting[i] < pivot:
                     _A   ,      _A				:	Union[str, Any]       =			sorting[i], sorting[a]
                     a += 1
                     i += 1
              elif sorting[i] > pivot:
                     _A   ,      _A				:	str       =			sorting[i], sorting[b]
                     b -= 1
              else:
                     i += 1
       quick_sort_apartition(UpperCamelCase__    ,							UpperCamelCase__    ,							a - 1						)
       quick_sort_apartition(UpperCamelCase__    ,							b + 1    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       if left < right:
              _A				:	Tuple       =			lomuto_partition(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__						)
              quick_sort_lomuto_partition(UpperCamelCase__    ,							UpperCamelCase__    ,							pivot_index - 1						)
              quick_sort_lomuto_partition(UpperCamelCase__    ,							pivot_index + 1    ,							UpperCamelCase__						)
def 							_UpperCAmelCase    (UpperCamelCase__					:  list    ,							UpperCamelCase__					:  int    ,							UpperCamelCase__					:  int						):
       _A				:	Tuple       =			sorting[right]
       _A				:	Tuple       =			left
       for i in range(UpperCamelCase__    ,							UpperCamelCase__						):
              if sorting[i] < pivot:
                     _A   ,      _A				:	int       =			sorting[i], sorting[store_index]
                     store_index += 1
       _A   ,      _A				:	Dict       =			sorting[store_index], sorting[right]
       return store_index
def 							_UpperCAmelCase    (UpperCamelCase__					:  list						):
       if len(UpperCamelCase__						) <= 1:
              return sorting
       return (
           three_way_radix_quicksort([i for i in sorting if i < sorting[0]]						)
           + [i for i in sorting if i == sorting[0]]
           + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]						)
       )


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)

    lowerCAmelCase__						=			input('Enter numbers separated by a comma:\n').strip()
    lowerCAmelCase__						=			[int(item) for item in user_input.split(',')]
    quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
    print(unsorted)
