
'''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					,   UpperCAmelCase=2					,   UpperCAmelCase=3					,   UpperCAmelCase=64					,   UpperCAmelCase=None	):
				a_      				=						np.random.default_rng(UpperCAmelCase	)
				a_      				=						length
				a_      				=						rng.normal(size=(length,)	).astype(np.floataa	)
				a_      				=						a * self.x + b + rng.normal(scale=0.1					,   size=(length,)	).astype(np.floataa	)




			def __len__(       self	):
				return self.length






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







class   a_ (      torch.nn.Module					):




			def __init__(       self					,   UpperCAmelCase=0					,   UpperCAmelCase=0					,   UpperCAmelCase=False	):
				super().__init__()
				a_      				=						torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
				a_      				=						torch.nn.Parameter(torch.tensor([2, 3]	).float()	)
				a_      				=						True






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=None	):
				if self.first_batch:
					print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''	)
					a_      				=						False
				return x * self.a[0] + self.b[0]







class   a_ (      torch.nn.Module					):




			def __init__(       self					,   UpperCAmelCase=0					,   UpperCAmelCase=0					,   UpperCAmelCase=False	):
				super().__init__()
				a_      				=						torch.nn.Parameter(torch.tensor(UpperCAmelCase	).float()	)
				a_      				=						torch.nn.Parameter(torch.tensor(UpperCAmelCase	).float()	)
				a_      				=						True






			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=None	):
				if self.first_batch:
					print(f'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''	)
					a_      				=						False
				return x * self.a + self.b







def      UpperCamelCase_				(      A__  ,      A__ = 16						):
	from datasets import load_dataset
	from transformers import AutoTokenizer

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

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

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

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

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

	return train_dataloader, eval_dataloader


'''simple docstring'''
from __future__ import annotations






def      UpperCamelCase_				(      A__						):
	a_      				=						[True] * limit
	a_      				=						False
	a_      				=						False
	a_      				=						True

	for i in range(3  ,      int(limit**0.5 + 1						)  ,      2						):
		a_      				=						i * 2
		while index < limit:
			a_      				=						False
			a_      				=						index + i

	a_      				=						[2]

	for i in range(3  ,      A__  ,      2						):
		if is_prime[i]:
			primes.append(A__						)

	return primes






def      UpperCamelCase_				(      A__ = 1_00_00_00						):
	a_      				=						prime_sieve(A__						)
	a_      				=						0
	a_      				=						0

	for i in range(len(A__						)						):
		for j in range(i + length  ,      len(A__						)						):
			a_      				=						sum(primes[i:j]						)
			if sol >= ceiling:
				break

			if sol in primes:
				a_      				=						j - i
				a_      				=						sol

	return largest


if __name__ == "__main__":
		print(F"""{solution() = }""")
