




"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType

class __lowercase:




					'''simple docstring'''


					def __init__(    self						,	__a=2						,	__a=3						,	__a=64						,	__a=None     ):
								__lowerCamelCase					:	Optional[Any]	       =				np.random.default_rng(__a     )
								__lowerCamelCase					:	int	       =				length
								__lowerCamelCase					:	Union[str, Any]	       =				rng.normal(size=(length,)     ).astype(np.floataa     )
								__lowerCamelCase					:	Dict	       =				a * self.x + b + rng.normal(scale=0.1						,	size=(length,)     ).astype(np.floataa     )
					def __len__(    self     ):
								return self.length





					def __getitem__(    self						,	__a     ):
								return {"x": self.x[i], "y": self.y[i]}



class __lowercase(      torch.nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a=0						,	__a=0						,	__a=False     ):
								super().__init__()
								__lowerCamelCase					:	List[Any]	       =				torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
								__lowerCamelCase					:	str	       =				torch.nn.Parameter(torch.tensor([2, 3]     ).float()     )
								__lowerCamelCase					:	List[str]	       =				True





					def     snake_case_				(    self						,	__a=None     ):
								if self.first_batch:
											print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''     )
											__lowerCamelCase					:	Union[str, Any]	       =				False
								return x * self.a[0] + self.b[0]



class __lowercase(      torch.nn.Module				):




					'''simple docstring'''


					def __init__(    self						,	__a=0						,	__a=0						,	__a=False     ):
								super().__init__()
								__lowerCamelCase					:	Optional[Any]	       =				torch.nn.Parameter(torch.tensor(__a     ).float()     )
								__lowerCamelCase					:	int	       =				torch.nn.Parameter(torch.tensor(__a     ).float()     )
								__lowerCamelCase					:	str	       =				True





					def     snake_case_				(    self						,	__a=None     ):
								if self.first_batch:
											print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''     )
											__lowerCamelCase					:	Tuple	       =				False
								return x * self.a + self.b





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    int = 16     )  ->     str:
			from datasets import load_dataset
			from transformers import AutoTokenizer

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

			__lowerCamelCase					:	int	       =				{v: i for i, v in enumerate(A__     )}

			def tokenize_function(A__:    Dict     ):
						# max_length=None => use the model max length (it's actually the default)
						__lowerCamelCase					:	Dict	       =				tokenizer(
						    examples['sentence1']							,				examples['sentence2']							,				truncation=A__							,				max_length=A__							,				padding='max_length'     )
						if "label" in examples:
									__lowerCamelCase					:	List[Any]	       =				[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
			__lowerCamelCase					:	Optional[int]	       =				datasets.map(
			    A__							,				batched=A__							,				remove_columns=['sentence1', 'sentence2', 'label']							,				)

			def collate_fn(A__:    Tuple     ):
						# 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(A__							,				padding='max_length'							,				max_length=128							,				return_tensors='pt'     )
						return tokenizer.pad(A__							,				padding='longest'							,				return_tensors='pt'     )

			# Instantiate dataloaders.
			__lowerCamelCase					:	Dict	       =				DataLoader(tokenized_datasets['train']							,				shuffle=A__							,				collate_fn=A__							,				batch_size=2     )
			__lowerCamelCase					:	str	       =				DataLoader(tokenized_datasets['validation']							,				shuffle=A__							,				collate_fn=A__							,				batch_size=1     )

			return train_dataloader, eval_dataloader









"""simple docstring"""
import random





def 						UpperCAmelCase				(			A__:    Union[str, Any]							,				A__:    List[str]							,				A__:    Union[str, Any]     )  ->     int:
			__lowerCamelCase					:	Optional[Any]	       =				a[left_index]
			__lowerCamelCase					:	int	       =				left_index + 1
			for j in range(left_index + 1							,				A__     ):
						if a[j] < pivot:
									__lowerCamelCase       ,      __lowerCamelCase					:	Optional[int]	       =				a[i], a[j]
									i += 1
			__lowerCamelCase       ,      __lowerCamelCase					:	str	       =				a[i - 1], a[left_index]
			return i - 1





def 						UpperCAmelCase				(			A__:    List[Any]							,				A__:    Tuple							,				A__:    Tuple     )  ->     Dict:
			if left < right:
						__lowerCamelCase					:	Optional[int]	       =				random.randint(A__							,				right - 1     )
						__lowerCamelCase       ,      __lowerCamelCase					:	int	       =				(
						    a[left],
						    a[pivot],
						)  # switches the pivot with the left most bound
						__lowerCamelCase					:	Union[str, Any]	       =				partition(A__							,				A__							,				A__     )
						quick_sort_random(
						    A__							,				A__							,				A__     )  # recursive quicksort to the left of the pivot point
						quick_sort_random(
						    A__							,				pivot_index + 1							,				A__     )  # recursive quicksort to the right of the pivot point





def 						UpperCAmelCase				(			)  ->     int:
			__lowerCamelCase					:	Dict	       =				input('Enter numbers separated by a comma:\n'     ).strip()
			__lowerCamelCase					:	int	       =				[int(A__     ) for item in user_input.split(','     )]

			quick_sort_random(A__							,				0							,				len(A__     )     )

			print(A__     )


if __name__ == "__main__":
					main()



