


'''simple docstring'''






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

from accelerate.utils.dataclasses import DistributedType




class 						a__							:





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=2   ,       lowerCamelCase_=3   ,       lowerCamelCase_=64   ,       lowerCamelCase_=None  )			->				Dict:
														lowerCAmelCase__			  =      np.random.default_rng(lowerCamelCase_  )
														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  )			->				Any:
														return self.length







							def __getitem__(						self   ,       lowerCamelCase_  )			->				List[str]:
														return {"x": self.x[i], "y": self.y[i]}




class 						a__							(				torch.nn.Module    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				List[Any]:
														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   ,       lowerCamelCase_=None  )			->				Optional[Any]:
														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    ):





							'''simple docstring'''






							def __init__(						self   ,       lowerCamelCase_=0   ,       lowerCamelCase_=0   ,       lowerCamelCase_=False  )			->				Any:
														super().__init__()
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      torch.nn.Parameter(torch.tensor(lowerCamelCase_  ).float()  )
														lowerCAmelCase__			  =      True







							def     __SCREAMING_SNAKE_CASE				(						self   ,       lowerCamelCase_=None  )			->				Any:
														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    _snake_case  (	A					,       A = 16			)		->       Any:
							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=A			)
							lowerCAmelCase__			  =      datasets['''train'''].unique('''label'''			)

							lowerCAmelCase__			  =      {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)
														lowerCAmelCase__			  =      tokenizer(
														    examples['''sentence1''']					,       examples['''sentence2''']					,       truncation=A					,       max_length=A					,       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(
							    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=128					,       return_tensors='''pt'''			)
														return tokenizer.pad(A					,       padding='''longest'''					,       return_tensors='''pt'''			)

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

							return train_dataloader, eval_dataloader



'''simple docstring'''





class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			Optional[Any]			,    lowerCAmelCase_							:			Dict	)      ->		List[str]:
						__A=	name
						__A=	val
			def __str__(  self							:			int	)      ->		List[Any]:
						return F"""{self.__class__.__name__}({self.name}, {self.val})"""

			def __lt__(  self							:			Optional[Any]			,    lowerCAmelCase_							:			str	)      ->		List[Any]:
						return self.val < other.val


class 						a__					:





			'''simple docstring'''

			def __init__(  self							:			Tuple			,    lowerCAmelCase_							:			int	)      ->		Any:
						__A=	{}
						__A=	{}
						__A=	self.build_heap(lowerCAmelCase_	)
			def __getitem__(  self							:			int			,    lowerCAmelCase_							:			Dict	)      ->		Tuple:
						return self.get_value(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						return (idx - 1) // 2
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		List[str]:
						return idx * 2 + 1
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Any:
						return idx * 2 + 2
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Dict:
						return self.heap_dict[key]
			def 					lowerCAmelCase     (  self							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Tuple:
						__A=	len(lowerCAmelCase_	) - 1
						__A=	self.get_parent_idx(lowerCAmelCase_	)

						for idx, i in enumerate(lowerCAmelCase_	):
									__A=	idx
									__A=	i.val

						for i in range(lowerCAmelCase_			,    -1			,    -1	):
									self.sift_down(lowerCAmelCase_			,    lowerCAmelCase_	)
						return array
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			Tuple	)      ->		Dict:
						while True:
									__A=	self.get_left_child_idx(lowerCAmelCase_	)  # noqa: E741
									__A=	self.get_right_child_idx(lowerCAmelCase_	)

									__A=	idx
									if l < len(lowerCAmelCase_	) and array[l] < array[idx]:
												__A=	l
									if r < len(lowerCAmelCase_	) and array[r] < array[smallest]:
												__A=	r

									if smallest != idx:
												__A,	__A=	array[smallest], array[idx]
												(
												    (
												    __A
												),	(
												    __A
												),	
												)=	(
												    self.idx_of_element[array[smallest]],
												    self.idx_of_element[array[idx]],
												)
												__A=	smallest
									else:
												break
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			Dict	)      ->		str:
						__A=	self.get_parent_idx(lowerCAmelCase_	)
						while p >= 0 and self.heap[p] > self.heap[idx]:
									__A,	__A=	self.heap[idx], self.heap[p]
									__A,	__A=	(
									    self.idx_of_element[self.heap[idx]],
									    self.idx_of_element[self.heap[p]],
									)
									__A=	p
									__A=	self.get_parent_idx(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return self.heap[0]
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		int:
						__A,	__A=	self.heap[-1], self.heap[0]
						__A,	__A=	(
						    self.idx_of_element[self.heap[-1]],
						    self.idx_of_element[self.heap[0]],
						)

						__A=	self.heap.pop()
						del self.idx_of_element[x]
						self.sift_down(0			,    self.heap	)
						return x
			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			Union[str, Any]	)      ->		Optional[int]:
						self.heap.append(lowerCAmelCase_	)
						__A=	len(self.heap	) - 1
						__A=	node.val
						self.sift_up(len(self.heap	) - 1	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]	)      ->		Tuple:
						return len(self.heap	) == 0

			def 					lowerCAmelCase     (  self							:			Optional[int]			,    lowerCAmelCase_							:			List[Any]			,    lowerCAmelCase_							:			List[Any]	)      ->		Optional[Any]:
						assert (
						    self.heap[self.idx_of_element[node]].val > new_value
						), "newValue must be less that current value"
						__A=	new_value
						__A=	new_value
						self.sift_up(self.idx_of_element[node]	)


UpperCAmelCase__  			=				Node('''R''', -1)
UpperCAmelCase__  			=				Node('''B''', 6)
UpperCAmelCase__  			=				Node('''A''', 3)
UpperCAmelCase__  			=				Node('''X''', 1)
UpperCAmelCase__  			=				Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
UpperCAmelCase__  			=				MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
					print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
					print(i)

if __name__ == "__main__":
					import doctest

					doctest.testmod()



