



'''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


UpperCAmelCase     		=   (
    '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 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[Any]							,		_SCREAMING_SNAKE_CASE					:						List[Any]							)      ->						Dict:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[Any]							,		_SCREAMING_SNAKE_CASE					:						Tuple							)      ->						Optional[int]:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[Any]							)      ->						List[str]:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						int							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							)      ->						Optional[int]:







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






def 		_snake_case		( _SCREAMING_SNAKE_CASE					:						List[str]							,		_SCREAMING_SNAKE_CASE					:						Optional[int]							,		_SCREAMING_SNAKE_CASE					:						Tuple							)      ->						List[Any]:







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




'''simple docstring'''

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
				import torch

				from transformers.generation import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)

@require_torch
class __snake_case(				unittest.TestCase ):


						'''simple docstring'''



						def     __snake_case   ( self     ,	A_				)     ->     str:
							lowerCAmelCase   =     3
							lowerCAmelCase   =     250

							lowerCAmelCase   =     ids_tensor((batch_size, length)     ,	A_				)
							lowerCAmelCase   =     torch.ones((batch_size, length)     ,	device=A_     ,	dtype=torch.float				) / length
							return input_ids, scores


						def     __snake_case   ( self				)     ->     Any:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     StoppingCriteriaList(
							    [
							        MaxLengthCriteria(max_length=10				),
							        MaxTimeCriteria(max_time=0.1				),
							    ]				)

							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     Optional[int]:
							lowerCAmelCase   =     MaxLengthCriteria(max_length=10				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)


						def     __snake_case   ( self				)     ->     List[Any]:
							lowerCAmelCase   =     MaxNewTokensCriteria(start_length=5     ,	max_new_tokens=5				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(9				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(10				)
							self.assertTrue(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     StoppingCriteriaList([criteria]				)
							self.assertEqual(criteria_list.max_length     ,	10				)


						def     __snake_case   ( self				)     ->     List[str]:
							lowerCAmelCase,       lowerCAmelCase   =     self._get_tensors(5				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1				)
							self.assertFalse(criteria(A_     ,	A_				)				)

							lowerCAmelCase   =     MaxTimeCriteria(max_time=0.1     ,	initial_timestamp=time.time() - 0.2				)
							self.assertTrue(criteria(A_     ,	A_				)				)




						def     __snake_case   ( self				)     ->     Optional[int]:
							validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	10				)

							with self.assertWarns(A_				):
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)     ,	11				)

							lowerCAmelCase   =     validate_stopping_criteria(StoppingCriteriaList()     ,	11				)

							self.assertEqual(len(A_				)     ,	1				)