





"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType






class 					_UpperCAmelCase   :


					def __init__(					self			:Optional[int]     ,       __UpperCamelCase			:Tuple=2     ,       __UpperCamelCase			:List[Any]=3     ,       __UpperCamelCase			:Any=64     ,       __UpperCamelCase			:Tuple=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			:Tuple		):
												return self.length


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







class 					_UpperCAmelCase   (	torch.nn.Module ):


					def __init__(					self			:Tuple     ,       __UpperCamelCase			:List[str]=0     ,       __UpperCamelCase			:Optional[int]=0     ,       __UpperCamelCase			:Optional[int]=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			:Dict     ,       __UpperCamelCase			:Optional[int]=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 					_UpperCAmelCase   (	torch.nn.Module ):


					def __init__(					self			:str     ,       __UpperCamelCase			:List[str]=0     ,       __UpperCamelCase			:List[str]=0     ,       __UpperCamelCase			:str=False		):
												super().__init__()
												A		=  torch.nn.Parameter(torch.tensor(__UpperCamelCase		).float()		)
												A		=  torch.nn.Parameter(torch.tensor(__UpperCamelCase		).float()		)
												A		=  True


					def 	lowerCamelCase       (					self			:int     ,       __UpperCamelCase			:Union[str, Any]=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 					A__				(			UpperCamelCase					,  UpperCamelCase = 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=UpperCamelCase       )
							A		=  datasets["train"].unique("label"       )

							A		=  {v: i for i, v in enumerate(UpperCamelCase       )}

							def tokenize_function(UpperCamelCase       ):
														# max_length=None => use the model max length (it's actually the default)
														A		=  tokenizer(
														    examples["sentence1"]					,  examples["sentence2"]					,  truncation=UpperCamelCase					,  max_length=UpperCamelCase					,  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(
							    UpperCamelCase					,  batched=UpperCamelCase					,  remove_columns=["sentence1", "sentence2", "label"]					,  )

							def collate_fn(UpperCamelCase       ):
														# 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(UpperCamelCase					,  padding="max_length"					,  max_length=128					,  return_tensors="pt"       )
														return tokenizer.pad(UpperCamelCase					,  padding="longest"					,  return_tensors="pt"       )

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

							return train_dataloader, eval_dataloader









"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


