





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

from accelerate.utils.dataclasses import DistributedType

class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase=2					,	__lowerCAmelCase=3					,	__lowerCAmelCase=64					,	__lowerCAmelCase=None					):
											UpperCamelCase__			       =     np.random.default_rng(A__					)
											UpperCamelCase__			       =     length
											UpperCamelCase__			       =     rng.normal(size=(length,)					).astype(np.floataa					)
											UpperCamelCase__			       =     a * self.x + b + rng.normal(scale=0.1					,	size=(length,)					).astype(np.floataa					)






							def __len__(  self					):
											return self.length







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






class     __SCREAMING_SNAKE_CASE   (       torch.nn.Module    ):






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







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase=None					):
											if self.first_batch:
															print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""					)
															UpperCamelCase__			       =     False
											return x * self.a[0] + self.b[0]






class     __SCREAMING_SNAKE_CASE   (       torch.nn.Module    ):






							def __init__(  self					,	__lowerCAmelCase=0					,	__lowerCAmelCase=0					,	__lowerCAmelCase=False					):
											super().__init__()
											UpperCamelCase__			       =     torch.nn.Parameter(torch.tensor(A__					).float()					)
											UpperCamelCase__			       =     torch.nn.Parameter(torch.tensor(A__					).float()					)
											UpperCamelCase__			       =     True







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase=None					):
											if self.first_batch:
															print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""					)
															UpperCamelCase__			       =     False
											return x * self.a + self.b






def       _UpperCamelCase       (a__				:Union[str, Any]				,     a__				:Optional[int] = 16    ):






				"""simple docstring"""





				from datasets import load_dataset
				from transformers import AutoTokenizer

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

				UpperCamelCase__			       =     {v: i for i, v in enumerate(lowerCAmelCase__    )}

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

				def collate_fn(a__				:Any    ):
								# 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.
				UpperCamelCase__			       =     DataLoader(tokenized_datasets["""train"""]				,     shuffle=lowerCAmelCase__				,     collate_fn=lowerCAmelCase__				,     batch_size=2    )
				UpperCamelCase__			       =     DataLoader(tokenized_datasets["""validation"""]				,     shuffle=lowerCAmelCase__				,     collate_fn=lowerCAmelCase__				,     batch_size=1    )

				return train_dataloader, eval_dataloader











def       _UpperCamelCase       (a__				:dict    ):






							"""simple docstring"""





							UpperCamelCase__			       =     set()
							# To detect a back edge, keep track of vertices currently in the recursion stack
							UpperCamelCase__			       =     set()
							return any(
							    node not in visited and depth_first_search(a__				,     a__				,     a__				,     a__    )
							    for node in graph    )





def       _UpperCamelCase       (a__				:dict				,     a__				:int				,     a__				:set				,     a__				:set    ):






							"""simple docstring"""





							visited.add(a__    )
							rec_stk.add(a__    )

							for node in graph[vertex]:
														if node not in visited:
																					if depth_first_search(a__				,     a__				,     a__				,     a__    ):
																												return True
														elif node in rec_stk:
																					return True

    # The node needs to be removed from recursion stack before function ends
							rec_stk.remove(a__    )
							return False


if __name__ == "__main__":
					from doctest import testmod

					testmod()




