


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

from accelerate.utils.dataclasses import DistributedType






class    _UpperCamelCase     :

				"""simple docstring"""







				def __init__(    self					, a__=2					, a__=3					, a__=64					, a__=None )						->					List[str]:
					A             =					np.random.default_rng(a__ )
					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 )						->					Optional[Any]:
					return self.length

				def __getitem__(    self					, a__ )						->					Optional[Any]:
					return {"x": self.x[i], "y": self.y[i]}






class    _UpperCamelCase     (       torch.nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__=0					, a__=0					, a__=False )						->					int:
					super().__init__()
					A             =					torch.nn.Parameter(torch.tensor([2, 3] ).float() )
					A             =					torch.nn.Parameter(torch.tensor([2, 3] ).float() )
					A             =					True

				def   _UpperCAmelCase       (    self					, a__=None )						->					List[Any]:
					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					):

				"""simple docstring"""







				def __init__(    self					, a__=0					, a__=0					, a__=False )						->					Dict:
					super().__init__()
					A             =					torch.nn.Parameter(torch.tensor(a__ ).float() )
					A             =					torch.nn.Parameter(torch.tensor(a__ ).float() )
					A             =					True

				def   _UpperCAmelCase       (    self					, a__=None )						->					Dict:
					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 	_lowerCAmelCase					( UpperCamelCase__:  Optional[Any]				,       UpperCamelCase__:  int = 16    )			-> str:





	"""simple docstring"""







	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__:  Optional[Any]    ):
		# 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__:  List[str]    ):
		# 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=1_28				,       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







def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Any=False    )			-> str:





	"""simple docstring"""







	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ) and isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		A             =					len(set_a.intersection(UpperCamelCase__    )    )

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
		else:
			A             =					len(set_a.union(UpperCamelCase__    )    )

		return intersection / union

	if isinstance(UpperCamelCase__				,       (list, tuple)    ) and isinstance(UpperCamelCase__				,       (list, tuple)    ):
		A             =					[element for element in set_a if element in set_b]

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
			return len(UpperCamelCase__    ) / union
		else:
			A             =					set_a + [element for element in set_b if element not in set_a]
			return len(UpperCamelCase__    ) / len(UpperCamelCase__    )

		return len(UpperCamelCase__    ) / len(UpperCamelCase__    )
	return None


if __name__ == "__main__":
				_lowercase							:   List[Any]						=						{"a", "b", "c", "d", "e"}
				_lowercase							:   List[Any]						=						{"c", "d", "e", "f", "h", "i"}
				print(jaccard_similarity(set_a, set_b))



