'''simple docstring'''


import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
					from scipy.stats import pearsonr, spearmanr
					from sklearn.metrics import fa_score, matthews_corrcoef


__lowerCAmelCase	:       Optional[Any]						       =	(
    "This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate "
    "library. You can have a look at this example script for pointers: "
    "https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"
)





def       lowerCAmelCase					(   UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Optional[Any]       ):


	"""simple docstring"""
	warnings.warn(UpperCamelCase__    ,					UpperCamelCase__       )
	requires_backends(UpperCamelCase__    ,					'''sklearn'''       )
	return (preds == labels).mean()





def       lowerCAmelCase					(   UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	warnings.warn(UpperCamelCase__    ,					UpperCamelCase__       )
	requires_backends(UpperCamelCase__    ,					'''sklearn'''       )
	__UpperCAmelCase							=			simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )
	__UpperCAmelCase							=			fa_score(y_true=UpperCamelCase__    ,					y_pred=UpperCamelCase__       )
	return {
	    "acc": acc,
	    "f1": fa,
	    "acc_and_f1": (acc + fa) / 2,
	}





def       lowerCAmelCase					(   UpperCamelCase__				:   List[Any]    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	warnings.warn(UpperCamelCase__    ,					UpperCamelCase__       )
	requires_backends(UpperCamelCase__    ,					'''sklearn'''       )
	__UpperCAmelCase							=			pearsonr(UpperCamelCase__    ,					UpperCamelCase__       )[0]
	__UpperCAmelCase							=			spearmanr(UpperCamelCase__    ,					UpperCamelCase__       )[0]
	return {
	    "pearson": pearson_corr,
	    "spearmanr": spearman_corr,
	    "corr": (pearson_corr + spearman_corr) / 2,
	}





def       lowerCAmelCase					(   UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Optional[int]    ,					UpperCamelCase__				:   Optional[Any]       ):


	"""simple docstring"""
	warnings.warn(UpperCamelCase__    ,					UpperCamelCase__       )
	requires_backends(UpperCamelCase__    ,					'''sklearn'''       )
	assert len(UpperCamelCase__       ) == len(UpperCamelCase__       ), f"""Predictions and labels have mismatched lengths {len(UpperCamelCase__       )} and {len(UpperCamelCase__       )}"""
	if task_name == "cola":
		return {"mcc": matthews_corrcoef(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "sst-2":
		return {"acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "mrpc":
		return acc_and_fa(UpperCamelCase__    ,					UpperCamelCase__       )
	elif task_name == "sts-b":
		return pearson_and_spearman(UpperCamelCase__    ,					UpperCamelCase__       )
	elif task_name == "qqp":
		return acc_and_fa(UpperCamelCase__    ,					UpperCamelCase__       )
	elif task_name == "mnli":
		return {"mnli/acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "mnli-mm":
		return {"mnli-mm/acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "qnli":
		return {"acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "rte":
		return {"acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "wnli":
		return {"acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	elif task_name == "hans":
		return {"acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	else:
		raise KeyError(UpperCamelCase__       )





def       lowerCAmelCase					(   UpperCamelCase__				:   List[str]    ,					UpperCamelCase__				:   str    ,					UpperCamelCase__				:   Any       ):


	"""simple docstring"""
	warnings.warn(UpperCamelCase__    ,					UpperCamelCase__       )
	requires_backends(UpperCamelCase__    ,					'''sklearn'''       )
	if len(UpperCamelCase__       ) != len(UpperCamelCase__       ):
		raise ValueError(f"""Predictions and labels have mismatched lengths {len(UpperCamelCase__       )} and {len(UpperCamelCase__       )}"""       )
	if task_name == "xnli":
		return {"acc": simple_accuracy(UpperCamelCase__    ,					UpperCamelCase__       )}
	else:
		raise KeyError(UpperCamelCase__       )

'''simple docstring'''


import unittest

import torch
from torch import nn

from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory





def       lowerCAmelCase					(		):


	"""simple docstring"""
	raise RuntimeError('''CUDA out of memory.'''       )






class 						A   (   nn.Module   ):





							def __init__(      self				:							Optional[Any]	)							->	int:
								super().__init__()
								__UpperCAmelCase							=			nn.Linear(3     ,		4	)
								__UpperCAmelCase							=			nn.BatchNormad(4	)
								__UpperCAmelCase							=			nn.Linear(4     ,		5	)






							def       snake_case__						(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Optional[int]:
								return self.lineara(self.batchnorm(self.lineara(__a	)	)	)






class 						A   (   unittest.TestCase   ):





							def       snake_case__						(      self				:							Optional[int]	)							->	Any:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							Union[str, Any]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()

								mock_training_loop_function()
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)





							def       snake_case__						(      self				:							str	)							->	int:
								__UpperCAmelCase							=			[]

								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Optional[int]	):
									nonlocal batch_sizes
									batch_sizes.append(__a	)
									if batch_size != 8:
										raise_fake_out_of_memory()
									return batch_size, arga

								__UpperCAmelCase				,			__UpperCAmelCase							=			mock_training_loop_function('''hello'''	)
								self.assertListEqual(__a     ,		[1_2_8, 6_4, 3_2, 1_6, 8]	)
								self.assertListEqual([bs, arga]     ,		[8, '''hello''']	)





							def       snake_case__						(      self				:							Any	)							->	int:
								@find_executable_batch_size(starting_batch_size=0	)
								def mock_training_loop_function(__a				:							Optional[int]	):
									pass

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''No executable batch size found, reached zero.'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Dict	):
									if batch_size > 0:
										raise_fake_out_of_memory()
									pass

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''No executable batch size found, reached zero.'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								@find_executable_batch_size(starting_batch_size=1_2_8	)
								def mock_training_loop_function(__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							int	):
									if batch_size != 8:
										raise raise_fake_out_of_memory()

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function(1_2_8     ,		'''hello'''     ,		'''world'''	)
									self.assertIn('''Batch size was passed into `f`'''     ,		cm.exception.args[0]	)
									self.assertIn('''`f(arg1=\'hello\', arg2=\'world\')'''     ,		cm.exception.args[0]	)





							def       snake_case__						(      self				:							Tuple	)							->	Optional[Any]:
								@find_executable_batch_size(starting_batch_size=1_6	)
								def mock_training_loop_function(__a				:							Tuple	):
									raise ValueError('''Oops, we had an error!'''	)

								with self.assertRaises(__a	) as cm:
									mock_training_loop_function()
									self.assertIn('''Oops, we had an error!'''     ,		cm.exception.args[0]	)






							@require_cuda
							def       snake_case__						(      self				:							Any	)							->	List[Any]:
								__UpperCAmelCase							=			torch.cuda.memory_allocated()
								__UpperCAmelCase							=			ModelForTest()
								model.cuda()
								self.assertGreater(torch.cuda.memory_allocated()     ,		__a	)
								__UpperCAmelCase							=			release_memory(__a	)
								self.assertEqual(torch.cuda.memory_allocated()     ,		__a	)
