




"""simple docstring"""







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

from accelerate.utils.dataclasses import DistributedType




class       lowerCAmelCase__       :


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	List[str]=2    ,   lowercase__					:	Optional[Any]=3    ,   lowercase__					:	Dict=6_4    ,   lowercase__					:	List[str]=None						):
						__lowercase				:		Union[str, Any]												=				np.random.default_rng(lowercase__						)
						__lowercase				:		Dict												=				length
						__lowercase				:		int												=				rng.normal(size=(length,)						).astype(np.floataa						)
						__lowercase				:		List[str]												=				a * self.x + b + rng.normal(scale=0.1    ,   size=(length,)						).astype(np.floataa						)



				def __len__(     self					:	Optional[int]						):
						return self.length



				def __getitem__(     self					:	Optional[int]    ,   lowercase__					:	List[Any]						):
						return {"x": self.x[i], "y": self.y[i]}





class       lowerCAmelCase__       (			torch.nn.Module    ):


				"""simple docstring"""





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



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	Any=None						):
						if self.first_batch:
								print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
								__lowercase				:		Any												=				False
						return x * self.a[0] + self.b[0]





class       lowerCAmelCase__       (			torch.nn.Module    ):


				"""simple docstring"""





				def __init__(     self					:	int    ,   lowercase__					:	Union[str, Any]=0    ,   lowercase__					:	List[str]=0    ,   lowercase__					:	Tuple=False						):
						super().__init__()
						__lowercase				:		Union[str, Any]												=				torch.nn.Parameter(torch.tensor(lowercase__						).float()						)
						__lowercase				:		Union[str, Any]												=				torch.nn.Parameter(torch.tensor(lowercase__						).float()						)
						__lowercase				:		int												=				True



				def       snake_case    (     self					:	List[str]    ,   lowercase__					:	Optional[int]=None						):
						if self.first_batch:
								print(f'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
								__lowercase				:		List[Any]												=				False
						return x * self.a + self.b





def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase = 16				)			->int:






		"""simple docstring"""


		from datasets import load_dataset
		from transformers import AutoTokenizer

		__lowercase				:		List[Any]												=				AutoTokenizer.from_pretrained("bert-base-cased"				)
		__lowercase				:		int												=				{"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
		__lowercase				:		Tuple												=				load_dataset("csv",  data_files=_lowerCamelCase				)
		__lowercase				:		Optional[int]												=				datasets["train"].unique("label"				)

		__lowercase				:		Optional[Any]												=				{v: i for i, v in enumerate(_lowerCamelCase				)}

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

		def collate_fn(_lowerCamelCase				):
				# 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(_lowerCamelCase,  padding="max_length",  max_length=1_28,  return_tensors="pt"				)
				return tokenizer.pad(_lowerCamelCase,  padding="longest",  return_tensors="pt"				)

		# Instantiate dataloaders.
		__lowercase				:		Optional[int]												=				DataLoader(tokenized_datasets["train"],  shuffle=_lowerCamelCase,  collate_fn=_lowerCamelCase,  batch_size=2				)
		__lowercase				:		Union[str, Any]												=				DataLoader(tokenized_datasets["validation"],  shuffle=_lowerCamelCase,  collate_fn=_lowerCamelCase,  batch_size=1				)

		return train_dataloader, eval_dataloader






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		return abs(_lowerCamelCase				) if a == 0 else greatest_common_divisor(b % a,  _lowerCamelCase				)

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase				)			->int:






		"""simple docstring"""


		while y:  # --> when y=0 then loop will terminate and return x as final GCD.
				__lowercase						,__lowercase				:		Any												=				y, x % y
		return abs(_lowerCamelCase				)

def 						snake_case__       (					)			->Optional[int]:






		"""simple docstring"""


		try:
				__lowercase				:		Optional[int]												=				input("Enter two integers separated by comma (,): "				).split(","				)
				__lowercase				:		Optional[Any]												=				int(nums[0]				)
				__lowercase				:		str												=				int(nums[1]				)
				print(
				    F'greatest_common_divisor({num_a}, {num_a}) = '
				    F'{greatest_common_divisor(_lowerCamelCase,  _lowerCamelCase				)}'				)
				print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowerCamelCase,  _lowerCamelCase				)}'				)
		except (IndexError, UnboundLocalError, ValueError):
				print("Wrong input"				)


if __name__ == "__main__":
						main()
