"""simple docstring"""




from __future__ import annotations

from typing import Any

class       a_      :




					def __init__(	self						:     Union[str, Any]  ,						snake_case__						:     int     ):
							lowerCAmelCase__        =   num_of_nodes
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   {}




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     int  ,						snake_case__						:     int  ,						snake_case__						:     int     ):
							self.m_edges.append([u_node, v_node, weight]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     str  ,						snake_case__						:     int     ):

							if self.m_component[u_node] == u_node:
									return u_node
							return self.find_component(self.m_component[u_node]     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Optional[int]  ,						snake_case__						:     int     ):

							if self.m_component[u_node] != u_node:
									for k in self.m_component:
											lowerCAmelCase__        =   self.find_component(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     Dict  ,						snake_case__						:     list[int]  ,						snake_case__						:     int  ,						snake_case__						:     int     ):

							if component_size[u_node] <= component_size[v_node]:
									lowerCAmelCase__        =   v_node
									component_size[v_node] += component_size[u_node]
									self.set_component(snake_case__     )

							elif component_size[u_node] >= component_size[v_node]:
									lowerCAmelCase__        =   self.find_component(snake_case__     )
									component_size[u_node] += component_size[v_node]
									self.set_component(snake_case__     )




					def 						_SCREAMING_SNAKE_CASE						(	self						:     int     ):
							lowerCAmelCase__        =   []
							lowerCAmelCase__        =   0

							lowerCAmelCase__        =   [-1] * self.m_num_of_nodes

							# A list of components (initialized to all of the nodes)
							for node in range(self.m_num_of_nodes     ):
									self.m_component.update({node: node}     )
									component_size.append(1     )

							lowerCAmelCase__        =   self.m_num_of_nodes

							while num_of_components > 1:
									for edge in self.m_edges:
											lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   edge

											lowerCAmelCase__        =   self.m_component[u]
											lowerCAmelCase__        =   self.m_component[v]

											if u_component != v_component:

													for component in (u_component, v_component):
															if (
															    minimum_weight_edge[component] == -1
															    or minimum_weight_edge[component][2] > w
															):
																	lowerCAmelCase__        =   [u, v, w]

									for edge in minimum_weight_edge:
											if isinstance(snake_case__  ,						snake_case__     ):
													lowerCAmelCase__   ,					lowerCAmelCase__   ,					lowerCAmelCase__        =   edge

													lowerCAmelCase__        =   self.m_component[u]
													lowerCAmelCase__        =   self.m_component[v]

													if u_component != v_component:
															mst_weight += w
															self.union(snake_case__  ,						snake_case__  ,						snake_case__     )
															print(F"""Added edge [{u} - {v}]\nAdded weight: {w}\n"""     )
															num_of_components -= 1

									lowerCAmelCase__        =   [-1] * self.m_num_of_nodes
							print(F"""The total weight of the minimal spanning tree is: {mst_weight}"""     )







def 							_UpperCAmelCase		(       ):





		"""simple docstring"""






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




