



import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class     __lowerCAmelCase					:



			def __init__(		self     :						int				,	A     :						Optional[Any]=2				,	A     :						List[str]=3				,	A     :						Optional[int]=64				,	A     :						Union[str, Any]=None)     ->					Dict:



						"""simple docstring"""






						_UpperCAmelCase	 =     np.random.default_rng(A)
						_UpperCAmelCase	 =     length
						_UpperCAmelCase	 =     rng.normal(size=(length,)).astype(np.floataa)
						_UpperCAmelCase	 =     a * self.x + b + rng.normal(scale=0.1				,	size=(length,)).astype(np.floataa)



			def __len__(		self     :						Tuple)     ->					List[Any]:



						"""simple docstring"""






						return self.length



			def __getitem__(		self     :						Union[str, Any]				,	A     :						Tuple)     ->					List[Any]:



						"""simple docstring"""






						return {"x": self.x[i], "y": self.y[i]}







class     __lowerCAmelCase					(    torch.nn.Module		):



			def __init__(		self     :						int				,	A     :						List[str]=0				,	A     :						Any=0				,	A     :						Optional[int]=False)     ->					Tuple:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     torch.nn.Parameter(torch.tensor([2, 3]).float())
						_UpperCAmelCase	 =     torch.nn.Parameter(torch.tensor([2, 3]).float())
						_UpperCAmelCase	 =     True



			def 					_lowerCamelCase (		self     :						int				,	A     :						Dict=None)     ->					int:



						"""simple docstring"""






						if self.first_batch:
									print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}")
									_UpperCAmelCase	 =     False
						return x * self.a[0] + self.b[0]







class     __lowerCAmelCase					(    torch.nn.Module		):



			def __init__(		self     :						Any				,	A     :						Optional[Any]=0				,	A     :						Any=0				,	A     :						List[Any]=False)     ->					Dict:



						"""simple docstring"""






						super().__init__()
						_UpperCAmelCase	 =     torch.nn.Parameter(torch.tensor(A).float())
						_UpperCAmelCase	 =     torch.nn.Parameter(torch.tensor(A).float())
						_UpperCAmelCase	 =     True



			def 					_lowerCamelCase (		self     :						Optional[Any]				,	A     :						Optional[int]=None)     ->					str:



						"""simple docstring"""






						if self.first_batch:
									print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}")
									_UpperCAmelCase	 =     False
						return x * self.a + self.b





def 							A			(  _UpperCAmelCase       :     Tuple							,     _UpperCAmelCase       :     int = 16					)       ->				Optional[int]:







			'''simple docstring'''
			from datasets import load_dataset
			from transformers import AutoTokenizer

			_UpperCAmelCase	 =     AutoTokenizer.from_pretrained('bert-base-cased'					)
			_UpperCAmelCase	 =     {'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
			_UpperCAmelCase	 =     load_dataset('csv'							,     data_files=_UpperCAmelCase					)
			_UpperCAmelCase	 =     datasets['train'].unique('label'					)

			_UpperCAmelCase	 =     {v: i for i, v in enumerate(_UpperCAmelCase					)}

			def tokenize_function(_UpperCAmelCase       :     List[str]					):
						# max_length=None => use the model max length (it's actually the default)
						_UpperCAmelCase	 =     tokenizer(
						    examples['sentence1']							,     examples['sentence2']							,     truncation=_UpperCAmelCase							,     max_length=_UpperCAmelCase							,     padding='max_length'					)
						if "label" in examples:
									_UpperCAmelCase	 =     [label_to_id[l] for l in examples['label']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			_UpperCAmelCase	 =     datasets.map(
			    _UpperCAmelCase							,     batched=_UpperCAmelCase							,     remove_columns=['sentence1', 'sentence2', 'label']							,     )

			def collate_fn(_UpperCAmelCase       :     Optional[Any]					):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCAmelCase							,     padding='max_length'							,     max_length=128							,     return_tensors='pt'					)
						return tokenizer.pad(_UpperCAmelCase							,     padding='longest'							,     return_tensors='pt'					)

			# Instantiate dataloaders.
			_UpperCAmelCase	 =     DataLoader(tokenized_datasets['train']							,     shuffle=_UpperCAmelCase							,     collate_fn=_UpperCAmelCase							,     batch_size=2					)
			_UpperCAmelCase	 =     DataLoader(tokenized_datasets['validation']							,     shuffle=_UpperCAmelCase							,     collate_fn=_UpperCAmelCase							,     batch_size=1					)

			return train_dataloader, eval_dataloader





def 							A			(  _UpperCAmelCase       :     list					)       ->				list:







			'''simple docstring'''
			if len(_UpperCAmelCase					) <= 1:
						return lst

			_UpperCAmelCase	 =     1

			while i < len(_UpperCAmelCase					):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									_UpperCAmelCase ,  _UpperCAmelCase	 =     lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												_UpperCAmelCase	 =     1

			return lst


if __name__ == "__main__":
							UpperCAmelCase__     = input("Enter numbers separated by a comma:\n").strip()
							UpperCAmelCase__     = [int(item) for item in user_input.split(",")]
							print(gnome_sort(unsorted))
