"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__     )     ->   list:





		"""simple docstring"""




		lowerCAmelCase__        =   [0] * len(lowerCamelCase__     )

		for i in range(1			,  len(lowerCamelCase__     )     ):
				# use last results for better performance - dynamic programming
				lowerCAmelCase__        =   prefix_result[i - 1]
				while j > 0 and input_string[i] != input_string[j]:
						lowerCAmelCase__        =   prefix_result[j - 1]

				if input_string[i] == input_string[j]:
						j += 1
				lowerCAmelCase__        =   j

		return prefix_result







def 							_UpperCAmelCase		(       lowerCamelCase__     )     ->   int:





		"""simple docstring"""




		return max(prefix_function(lowerCamelCase__     )     )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





"""simple docstring"""




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

from accelerate.utils.dataclasses import DistributedType

class       a_      :




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




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




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

class       a_      (     torch.nn.Module			):




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int  ,						snake_case__						:     Any=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a[0] + self.b[0]

class       a_      (     torch.nn.Module			):




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




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Tuple  ,						snake_case__						:     Optional[Any]=None     ):
							if self.first_batch:
									print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""     )
									lowerCAmelCase__        =   False
							return x * self.a + self.b







def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__ = 16     ):





		"""simple docstring"""




		from datasets import load_dataset
		from transformers import AutoTokenizer

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

		lowerCAmelCase__        =   {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)
				lowerCAmelCase__        =   tokenizer(
				    examples["""sentence1"""]			,  examples["""sentence2"""]			,  truncation=lowerCamelCase__			,  max_length=lowerCamelCase__			,  padding="""max_length"""     )
				if "label" in examples:
						lowerCAmelCase__        =   [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__        =   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=128			,  return_tensors="""pt"""     )
				return tokenizer.pad(lowerCamelCase__			,  padding="""longest"""			,  return_tensors="""pt"""     )

		# Instantiate dataloaders.
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""train"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=2     )
		lowerCAmelCase__        =   DataLoader(tokenized_datasets["""validation"""]			,  shuffle=lowerCamelCase__			,  collate_fn=lowerCamelCase__			,  batch_size=1     )

		return train_dataloader, eval_dataloader




