"""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


UpperCamelCase_    :    Optional[int]											=						logging.getLogger(__name__)

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

UpperCamelCase_    :    Any											=						{
    """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
UpperCamelCase_    :    Optional[Any]											=						{
    """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
}
UpperCamelCase_    :    Optional[Any]											=						sorted(arg_to_scheduler.keys())
UpperCamelCase_    :    List[Any]											=						"""{""" + """, """.join(arg_to_scheduler_choices) + """}"""


class      _lowercase						(				pl.LightningModule					):

				def __init__(       self    :      str    ,      _lowercase    :      argparse.Namespace    ,      _lowercase    :      Dict=None    ,      _lowercase    :      Union[str, Any]="base"    ,      _lowercase    :      Dict=None    ,      _lowercase    :      Union[str, Any]=None    ,      _lowercase    :      str=None    ,      **_lowercase    :      Any    ,      ):


										'''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(_lowercase       )
										UpperCAmelCase__   		=						0
										UpperCAmelCase__   		=						Path(self.hparams.output_dir       )
										UpperCAmelCase__   		=						self.hparams.cache_dir if self.hparams.cache_dir else None
										if config is None:
																UpperCAmelCase__   		=						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:
																UpperCAmelCase__   		=						config

										UpperCAmelCase__   		=						("""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:
																UpperCAmelCase__   		=						AutoTokenizer.from_pretrained(
																    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path    ,      cache_dir=_lowercase    ,      )
										else:
																UpperCAmelCase__   		=						tokenizer
										UpperCAmelCase__   		=						MODEL_MODES[mode]
										if model is None:
																UpperCAmelCase__   		=						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:
																UpperCAmelCase__   		=						model

				def     A	(       self    :      Dict    ,      *_lowercase    :      List[str]    ,      **_lowercase    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.model_type.from_pretrained(*_lowercase    ,      **_lowercase       )

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						arg_to_scheduler[self.hparams.lr_scheduler]
										UpperCAmelCase__   		=						get_schedule_func(
										    self.opt    ,      num_warmup_steps=self.hparams.warmup_steps    ,      num_training_steps=self.total_steps()       )
										UpperCAmelCase__   		=						{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
										return scheduler

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.model
										UpperCAmelCase__   		=						["""bias""", """LayerNorm.weight"""]
										UpperCAmelCase__   		=						[
										    {
										        """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:
																UpperCAmelCase__   		=						Adafactor(
																    _lowercase    ,      lr=self.hparams.learning_rate    ,      scale_parameter=_lowercase    ,      relative_step=_lowercase       )

										else:
																UpperCAmelCase__   		=						AdamW(
																    _lowercase    ,      lr=self.hparams.learning_rate    ,      eps=self.hparams.adam_epsilon       )
										UpperCAmelCase__   		=						optimizer

										UpperCAmelCase__   		=						self.get_lr_scheduler()

										return [optimizer], [scheduler]

				def     A	(       self    :      Optional[Any]    ,      _lowercase    :      List[str]    ,      _lowercase    :      Any       ):


										'''simple docstring'''
										return self.validation_step(_lowercase    ,      _lowercase       )

				def     A	(       self    :      Tuple    ,      _lowercase    :      List[Any]       ):


										'''simple docstring'''
										return self.validation_end(_lowercase       )

				def     A	(       self    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						max(1    ,      self.hparams.gpus       )  # TODO: consider num_tpu_cores
										UpperCAmelCase__   		=						self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
										return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs

				def     A	(       self    :      str    ,      _lowercase    :      Optional[Any]       ):


										'''simple docstring'''
										if mode == "test":
																UpperCAmelCase__   		=						len(self.test_dataloader().dataset       )
										else:
																UpperCAmelCase__   		=						self.get_dataloader("""train"""    ,      self.hparams.train_batch_size    ,      shuffle=_lowercase       )
																UpperCAmelCase__   		=						len(self.train_dataloader().dataset       )

				def     A	(       self    :      List[Any]    ,      _lowercase    :      str    ,      _lowercase    :      int    ,      _lowercase    :      bool = False       ):


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

				def     A	(       self    :      str       ):


										'''simple docstring'''
										return self.train_loader

				def     A	(       self    :      List[Any]       ):


										'''simple docstring'''
										return self.get_dataloader("""dev"""    ,      self.hparams.eval_batch_size    ,      shuffle=_lowercase       )

				def     A	(       self    :      Optional[Any]       ):


										'''simple docstring'''
										return self.get_dataloader("""test"""    ,      self.hparams.eval_batch_size    ,      shuffle=_lowercase       )

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      List[str]       ):


										'''simple docstring'''
										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     A	(       self    :      Optional[int]    ,      _lowercase    :      Dict[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.output_dir.joinpath("""best_tfmr"""       )
										UpperCAmelCase__   		=						self.step_count
										self.model.save_pretrained(_lowercase       )
										self.tokenizer.save_pretrained(_lowercase       )

				@staticmethod
				def     A	(       _lowercase    :      str    ,      _lowercase    :      List[str]       ):


										'''simple docstring'''
										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      _lowercase						(				pl.Callback					):

				def     A	(       self    :      str    ,      _lowercase    :      Union[str, Any]    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						trainer.lr_schedulers[0]["""scheduler"""]
										UpperCAmelCase__   		=						{f'lr_group_{i}': lr for i, lr in enumerate(lr_scheduler.get_lr()       )}
										pl_module.logger.log_metrics(_lowercase       )

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      pl.Trainer    ,      _lowercase    :      pl.LightningModule       ):


										'''simple docstring'''
										rank_zero_info("""***** Validation results *****"""       )
										UpperCAmelCase__   		=						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     A	(       self    :      Tuple    ,      _lowercase    :      pl.Trainer    ,      _lowercase    :      pl.LightningModule       ):


										'''simple docstring'''
										rank_zero_info("""***** Test results *****"""       )
										UpperCAmelCase__   		=						trainer.callback_metrics
										# Log and save results to file
										UpperCAmelCase__   		=						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 					a__ (						UpperCAmelCase__    :     Dict						,     UpperCAmelCase__    :     Any     )->		None:
						#  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=4_2						,     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 					a__ (						UpperCAmelCase__    :     BaseTransformer						,     UpperCAmelCase__    :     argparse.Namespace						,     UpperCAmelCase__    :     Optional[Any]=None						,     UpperCAmelCase__    :     Tuple=True						,     UpperCAmelCase__    :     Any=[]						,     UpperCAmelCase__    :     Tuple=None						,     UpperCAmelCase__    :     Tuple=None						,     **UpperCAmelCase__    :     List[Any]						,     )->		Any:
						pl.seed_everything(args.seed     )

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

						# add custom checkpoints
						if checkpoint_callback is None:
												UpperCAmelCase__   		=						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:
												UpperCAmelCase__   		=						LoggingCallback()

						UpperCAmelCase__   		=						{}

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

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

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

						UpperCAmelCase__   		=						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


"""simple docstring"""







def 					a__ (						UpperCAmelCase__    :     List[Any]     )->		Dict:
						UpperCAmelCase__   		=						[7_0_1, 3_0_1, 1_3_2, 5_7, 2_3, 1_0, 4, 1]
						for gap in gaps:
												for i in range(UpperCAmelCase__						,     len(UpperCAmelCase__     )     ):
																		UpperCAmelCase__   		=						collection[i]
																		UpperCAmelCase__   		=						i
																		while j >= gap and collection[j - gap] > insert_value:
																								UpperCAmelCase__   		=						collection[j - gap]
																								j -= gap
																		if j != i:
																								UpperCAmelCase__   		=						insert_value
						return collection


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

						testmod()
						UpperCamelCase_    :    Union[str, Any]											=						input("""Enter numbers separated by a comma:\n""").strip()
						UpperCamelCase_    :    int											=						[int(item) for item in user_input.split(""",""")]
						print(shell_sort(unsorted))

