
"""simple docstring"""




class 	lowerCAmelCase_						:


				"""simple docstring"""



				def __init__(self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   0
											SCREAMING_SNAKE_CASE__						:       str           =   0
											SCREAMING_SNAKE_CASE__						:       int           =   {}



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											if vertex not in self.adjacency:
																		SCREAMING_SNAKE_CASE__						:       Any           =   {}
																		self.num_vertices += 1



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Dict:



											"""simple docstring"""




											self.add_vertex(SCREAMING_SNAKE_CASE__    )
											self.add_vertex(SCREAMING_SNAKE_CASE__    )

											if head == tail:
																		return

											SCREAMING_SNAKE_CASE__						:       int           =   weight
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   weight



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   self.get_edges()
											for edge in edges:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   edge
																		edges.remove((tail, head, weight)    )
											for i in range(len(SCREAMING_SNAKE_CASE__    )    ):
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   list(edges[i]    )

											edges.sort(key=lambda SCREAMING_SNAKE_CASE__    : e[2]    )
											for i in range(len(SCREAMING_SNAKE_CASE__    ) - 1    ):
																		if edges[i][2] >= edges[i + 1][2]:
																									SCREAMING_SNAKE_CASE__						:       Any           =   edges[i][2] + 1
											for edge in edges:
																		SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   edge
																		SCREAMING_SNAKE_CASE__						:       Any           =   weight
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   weight



				def __str__(self    )					->		Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   """"""
											for tail in self.adjacency:
																		for head in self.adjacency[tail]:
																									SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   self.adjacency[head][tail]
																									string += F'''{head} -> {tail} == {weight}\n'''
											return string.rstrip("""\n"""    )



				def 	__magic_name__    (self    )					->		Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Tuple           =   []
											for tail in self.adjacency:
																		for head in self.adjacency[tail]:
																									output.append((tail, head, self.adjacency[head][tail])    )
											return output



				def 	__magic_name__    (self    )					->		List[str]:



											"""simple docstring"""




											return self.adjacency.keys()



				@staticmethod
				def 	__magic_name__    (SCREAMING_SNAKE_CASE__=None   ,       SCREAMING_SNAKE_CASE__=None    )					->		str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   Graph()
											if vertices is None:
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
											if edges is None:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   []
											for vertex in vertices:
																		g.add_vertex(SCREAMING_SNAKE_CASE__    )
											for edge in edges:
																		g.add_edge(*SCREAMING_SNAKE_CASE__    )
											return g



				class 	lowerCAmelCase_						:


								"""simple docstring"""



								def __init__(self    )					->		List[str]:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__						:       Dict           =   {}
															SCREAMING_SNAKE_CASE__						:       List[Any]           =   {}



								def __len__(self    )					->		Any:



															"""simple docstring"""




															return len(self.parent    )



								def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		Union[str, Any]:



															"""simple docstring"""




															if item in self.parent:
																						return self.find(SCREAMING_SNAKE_CASE__    )

															SCREAMING_SNAKE_CASE__						:       int           =   item
															SCREAMING_SNAKE_CASE__						:       Dict           =   0
															return item



								def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__    )					->		str:



															"""simple docstring"""




															if item not in self.parent:
																						return self.make_set(SCREAMING_SNAKE_CASE__    )
															if item != self.parent[item]:
																						SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.find(self.parent[item]    )
															return self.parent[item]



								def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )					->		Tuple:



															"""simple docstring"""




															SCREAMING_SNAKE_CASE__						:       str           =   self.find(SCREAMING_SNAKE_CASE__    )
															SCREAMING_SNAKE_CASE__						:       List[Any]           =   self.find(SCREAMING_SNAKE_CASE__    )

															if roota == roota:
																						return roota

															if self.rank[roota] > self.rank[roota]:
																						SCREAMING_SNAKE_CASE__						:       str           =   roota
																						return roota

															if self.rank[roota] < self.rank[roota]:
																						SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   roota
																						return roota

															if self.rank[roota] == self.rank[roota]:
																						self.rank[roota] += 1
																						SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   roota
																						return roota
															return None






				@staticmethod
				def 	__magic_name__    (SCREAMING_SNAKE_CASE__    )					->		Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Any           =   graph.num_vertices

											SCREAMING_SNAKE_CASE__						:       List[str]           =   Graph.UnionFind()
											SCREAMING_SNAKE_CASE__						:       Tuple           =   []
											while num_components > 1:
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   {}
																		for vertex in graph.get_vertices():
																									SCREAMING_SNAKE_CASE__						:       List[str]           =   -1

																		SCREAMING_SNAKE_CASE__						:       str           =   graph.get_edges()
																		for edge in edges:
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Dict           =   edge
																									edges.remove((tail, head, weight)    )
																		for edge in edges:
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       List[Any]           =   edge
																									SCREAMING_SNAKE_CASE__						:       int           =   union_find.find(SCREAMING_SNAKE_CASE__    )
																									SCREAMING_SNAKE_CASE__						:       Optional[int]           =   union_find.find(SCREAMING_SNAKE_CASE__    )
																									if seta != seta:
																																if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																							SCREAMING_SNAKE_CASE__						:       Dict           =   [head, tail, weight]

																																if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																							SCREAMING_SNAKE_CASE__						:       List[Any]           =   [head, tail, weight]
																		for vertex in cheap_edge:
																									if cheap_edge[vertex] != -1:
																																SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   cheap_edge[vertex]
																																if union_find.find(SCREAMING_SNAKE_CASE__    ) != union_find.find(SCREAMING_SNAKE_CASE__    ):
																																							union_find.union(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																																							mst_edges.append(cheap_edge[vertex]    )
																																							SCREAMING_SNAKE_CASE__						:       Tuple           =   num_components - 1
											SCREAMING_SNAKE_CASE__						:       int           =   Graph.build(edges=SCREAMING_SNAKE_CASE__    )
											return mst






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

from accelerate.utils.dataclasses import DistributedType





class 	_lowerCAmelCase					:




				def __init__(					self				,				_UpperCamelCase=2				,				_UpperCamelCase=3				,				_UpperCamelCase=64				,				_UpperCamelCase=None     )				->      Optional[Any]:
							lowerCAmelCase_						=	np.random.default_rng(_UpperCamelCase     )
							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     )				->      int:
							return self.length




				def __getitem__(					self				,				_UpperCamelCase     )				->      Dict:
							return {"x": self.x[i], "y": self.y[i]}





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=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      __a						(					self				,				_UpperCamelCase=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[0] + self.b[0]





class 	_lowerCAmelCase					(	torch.nn.Module       ):




				def __init__(					self				,				_UpperCamelCase=0				,				_UpperCamelCase=0				,				_UpperCamelCase=False     )				->      Optional[int]:
							super().__init__()
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	torch.nn.Parameter(torch.tensor(_UpperCamelCase     ).float()     )
							lowerCAmelCase_						=	True




				def      __a						(					self				,				_UpperCamelCase=None     )				->      int:
							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    lowerCamelCase__				(				__lowerCAmelCase  :  List[Any]    ,							__lowerCAmelCase  :  int = 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  :  List[Any]				):
						# 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  :  Optional[int]				):
						# 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



