





from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
				from fairscale.optim import OSS


UpperCAmelCase						:   Optional[int]			  =     logging.get_logger(__name__)

UpperCAmelCase						:   List[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,
    '''constant''': get_constant_schedule,
    '''constant_w_warmup''': get_constant_schedule_with_warmup,
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				def __init__(			self     ,					_A=None     ,					_A=None     ,					*_A     ,					**_A     ):
									super().__init__(*_A     ,					**_A     )

									if config is None:
														assert isinstance(self.model     ,					_A     ), (
														    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
														    F""" {self.model.__class__}"""
														)
														__A	:						Optional[Any]       =				self.model.config
									else:
														__A	:						Tuple       =				config

									__A	:						Union[str, Any]       =				data_args
									__A	:						List[Any]       =				self.config.tgt_vocab_size if isinstance(self.config     ,					_A     ) else self.config.vocab_size

									if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
														assert self.config.pad_token_id is not None, (
														    "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
														    " calculation or doing label smoothing."
														)

									if self.config.pad_token_id is None and self.config.eos_token_id is not None:
														logger.warning(
														    F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
														    ' padding..'     )

									if self.args.label_smoothing == 0:
														__A	:						List[str]       =				torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id     )
									else:
														# dynamically import label_smoothed_nll_loss
														from utils import label_smoothed_nll_loss

														__A	:						Optional[int]       =				label_smoothed_nll_loss



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									if self.optimizer is None:
														__A	:						List[str]       =				['bias', 'LayerNorm.weight']
														__A	:						List[Any]       =				[
														    {
														        'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay     )],
														        'weight_decay': self.args.weight_decay,
														    },
														    {
														        'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay     )],
														        'weight_decay': 0.0,
														    },
														]
														__A	:						Dict       =				Adafactor if self.args.adafactor else AdamW
														if self.args.adafactor:
																			__A	:						Any       =				Adafactor
																			__A	:						List[str]       =				{'scale_parameter': False, 'relative_step': False}
														else:
																			__A	:						Tuple       =				AdamW
																			__A	:						List[Any]       =				{
																			    'betas': (self.args.adam_betaa, self.args.adam_betaa),
																			    'eps': self.args.adam_epsilon,
																			}
														__A	:						Optional[Any]       =				self.args.learning_rate
														if self.sharded_ddp:
																			__A	:						Any       =				OSS(
																			    params=_A     ,					optim=_A     ,					**_A     ,					)
														else:
																			__A	:						List[str]       =				optimizer_cls(_A     ,					**_A     )

									if self.lr_scheduler is None:
														__A	:						Any       =				self._get_lr_scheduler(_A     )
									else:  # ignoring --lr_scheduler
														logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.'     )



				def 					UpperCAmelCase_		(			self     ,					_A     ):
									__A	:						Dict       =				arg_to_scheduler[self.args.lr_scheduler]
									if self.args.lr_scheduler == "constant":
														__A	:						Dict       =				schedule_func(self.optimizer     )
									elif self.args.lr_scheduler == "constant_w_warmup":
														__A	:						str       =				schedule_func(self.optimizer     ,					num_warmup_steps=self.args.warmup_steps     )
									else:
														__A	:						int       =				schedule_func(
														    self.optimizer     ,					num_warmup_steps=self.args.warmup_steps     ,					num_training_steps=_A     )
									return scheduler



				def 					UpperCAmelCase_		(			self     ):
									if isinstance(self.train_dataset     ,					torch.utils.data.IterableDataset     ):
														return None
									elif is_torch_tpu_available():
														return get_tpu_sampler(self.train_dataset     )
									else:
														if self.args.sortish_sampler:
																			self.train_dataset.make_sortish_sampler(
																			    self.args.per_device_train_batch_size     ,					distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)     ,					)

														return (
														    RandomSampler(self.train_dataset     )
														    if self.args.local_rank == -1
														    else DistributedSampler(self.train_dataset     )
														)



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ):
									if self.args.label_smoothing == 0:
														if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
																			# force training to ignore pad token
																			__A	:						List[str]       =				model(**_A     ,					use_cache=_A     )[0]
																			__A	:						List[str]       =				self.loss_fn(logits.view(-1     ,					logits.shape[-1]     )     ,					labels.view(-1     )     )
														else:
																			# compute usual loss via models
																			__A    ,				__A	:						List[str]       =				model(**_A     ,					labels=_A     ,					use_cache=_A     )[:2]
									else:
														# compute label smoothed loss
														__A	:						Tuple       =				model(**_A     ,					use_cache=_A     )[0]
														__A	:						Optional[int]       =				torch.nn.functional.log_softmax(_A     ,					dim=-1     )
														__A    ,				__A	:						Tuple       =				self.loss_fn(_A     ,					_A     ,					self.args.label_smoothing     ,					ignore_index=self.config.pad_token_id     )
									return loss, logits



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									__A	:						int       =				inputs.pop('labels'     )
									__A    ,				__A	:						int       =				self._compute_loss(_A     ,					_A     ,					_A     )
									return loss



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A = None     ,					):
									__A	:						str       =				self._prepare_inputs(_A     )

									__A	:						List[str]       =				{
									    'max_length': self.data_args.val_max_target_length
									    if self.data_args is not None
									    else self.config.max_length,
									    'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
									}

									if self.args.predict_with_generate and not self.args.prediction_loss_only:
														__A	:						Dict       =				self.model.generate(
														    inputs['input_ids']     ,					attention_mask=inputs['attention_mask']     ,					**_A     ,					)
														# in case the batch is shorter than max length, the output should be padded
														if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
																			__A	:						int       =				self._pad_tensors_to_max_len(_A     ,					gen_kwargs['max_length']     )

									__A	:						List[Any]       =				inputs.pop('labels'     )
									with torch.no_grad():
														# compute loss on predict data
														__A    ,				__A	:						int       =				self._compute_loss(_A     ,					_A     ,					_A     )

									__A	:						List[str]       =				loss.mean().detach()
									if self.args.prediction_loss_only:
														return (loss, None, None)

									__A	:						Union[str, Any]       =				generated_tokens if self.args.predict_with_generate else logits

									if labels.shape[-1] < gen_kwargs["max_length"]:
														__A	:						List[Any]       =				self._pad_tensors_to_max_len(_A     ,					gen_kwargs['max_length']     )

									return (loss, logits, labels)





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ):
									# If PAD token is not defined at least EOS token has to be defined
									__A	:						Dict       =				self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

									if pad_token_id is None:
														raise ValueError(
														    'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
														    F""" padded to `max_length`={max_length}"""     )

									__A	:						Union[str, Any]       =				pad_token_id * torch.ones(
									    (tensor.shape[0], max_length)     ,					dtype=tensor.dtype     ,					device=tensor.device     )
									__A	:						str       =				tensor
									return padded_tensor











def  _SCREAMING_SNAKE_CASE				(   a  )      ->      Tuple:
					__A    ,				__A	:						Optional[Any]       =				[], []
					while len(a  ) > 1:
										__A    ,				__A	:						Any       =				min(a  ), max(a  )
										start.append(a  )
										end.append(a  )
										collection.remove(a  )
										collection.remove(a  )
					end.reverse()
					return start + collection + end


if __name__ == "__main__":
				UpperCAmelCase						:   int			  =     input('''Enter numbers separated by a comma:\n''').strip()
				UpperCAmelCase						:   Dict			  =     [int(item) for item in user_input.split(''',''')]
				print(*merge_sort(unsorted), sep=''',''')




