


"""simple docstring"""







import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
		from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
		from tensorflow.keras.optimizers import Adam




class  UpperCamelCase_	( tf.keras.optimizers.schedules.LearningRateSchedule):

	"""simple docstring"""





	def __init__( self			:				List[Any]			,			UpperCAmelCase__			:				float			,			UpperCAmelCase__			:				Callable			,			UpperCAmelCase__			:				int			,			UpperCAmelCase__			:				float = 1.0			,			UpperCAmelCase__			:				str = None			,			)       ->	Tuple:
						super().__init__()
						__SCREAMING_SNAKE_CASE						   =				initial_learning_rate
						__SCREAMING_SNAKE_CASE						   =				warmup_steps
						__SCREAMING_SNAKE_CASE						   =				power
						__SCREAMING_SNAKE_CASE						   =				decay_schedule_fn
						__SCREAMING_SNAKE_CASE						   =				name




	def __call__( self			:				List[str]			,			UpperCAmelCase__			:				List[Any]   )       ->	Any:
						with tf.name_scope(self.name or "WarmUp"   ) as name:
											# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
											# learning rate will be `global_step/num_warmup_steps * init_lr`.
											__SCREAMING_SNAKE_CASE						   =				tf.cast(UpperCAmelCase__			,			tf.floataa   )
											__SCREAMING_SNAKE_CASE						   =				tf.cast(self.warmup_steps			,			tf.floataa   )
											__SCREAMING_SNAKE_CASE						   =				global_step_float / warmup_steps_float
											__SCREAMING_SNAKE_CASE						   =				self.initial_learning_rate * tf.math.pow(UpperCAmelCase__			,			self.power   )
											return tf.cond(
											    global_step_float < warmup_steps_float			,			lambda: warmup_learning_rate			,			lambda: self.decay_schedule_fn(step - self.warmup_steps   )			,			name=UpperCAmelCase__			,			)




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[int]:
						return {
						    "initial_learning_rate": self.initial_learning_rate,
						    "decay_schedule_fn": self.decay_schedule_fn,
						    "warmup_steps": self.warmup_steps,
						    "power": self.power,
						    "name": self.name,
						}




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_ = 0.0  ,						lowerCAmelCase_ = 0.9  ,						lowerCAmelCase_ = 0.999  ,						lowerCAmelCase_ = 1E-8  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = None  ,						lowerCAmelCase_ = 0.0  ,						lowerCAmelCase_ = 1.0  ,						lowerCAmelCase_ = None  ,						):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tf.keras.optimizers.schedules.PolynomialDecay(
					    initial_learning_rate=lowerCAmelCase_  ,						decay_steps=num_train_steps - num_warmup_steps  ,						end_learning_rate=init_lr * min_lr_ratio  ,						power=lowerCAmelCase_  ,						)
					if num_warmup_steps:
										__SCREAMING_SNAKE_CASE						   =				WarmUp(
										    initial_learning_rate=lowerCAmelCase_  ,						decay_schedule_fn=lowerCAmelCase_  ,						warmup_steps=lowerCAmelCase_  ,						)
					if weight_decay_rate > 0.0:
										__SCREAMING_SNAKE_CASE						   =				AdamWeightDecay(
										    learning_rate=lowerCAmelCase_  ,						weight_decay_rate=lowerCAmelCase_  ,						beta_a=lowerCAmelCase_  ,						beta_a=lowerCAmelCase_  ,						epsilon=lowerCAmelCase_  ,						clipnorm=lowerCAmelCase_  ,						global_clipnorm=lowerCAmelCase_  ,						exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]  ,						include_in_weight_decay=lowerCAmelCase_  ,						)
					else:
										__SCREAMING_SNAKE_CASE						   =				tf.keras.optimizers.Adam(
										    learning_rate=lowerCAmelCase_  ,						beta_a=lowerCAmelCase_  ,						beta_a=lowerCAmelCase_  ,						epsilon=lowerCAmelCase_  ,						clipnorm=lowerCAmelCase_  ,						global_clipnorm=lowerCAmelCase_  ,						)
					# We return the optimizer and the LR scheduler in order to better track the
					# evolution of the LR independently of the optimizer.
					return optimizer, lr_schedule




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				Tuple			,			UpperCAmelCase__			:				Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001			,			UpperCAmelCase__			:				float = 0.9			,			UpperCAmelCase__			:				float = 0.999			,			UpperCAmelCase__			:				float = 1E-7			,			UpperCAmelCase__			:				bool = False			,			UpperCAmelCase__			:				float = 0.0			,			UpperCAmelCase__			:				Optional[List[str]] = None			,			UpperCAmelCase__			:				Optional[List[str]] = None			,			UpperCAmelCase__			:				str = "AdamWeightDecay"			,			**UpperCAmelCase__			:				Optional[Any]			,			)       ->	Tuple:
						super().__init__(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			**UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				weight_decay_rate
						__SCREAMING_SNAKE_CASE						   =				include_in_weight_decay
						__SCREAMING_SNAKE_CASE						   =				exclude_from_weight_decay




	@classmethod
	def   UpperCAmelCase_      ( cls			:				Optional[int]			,			UpperCAmelCase__			:				Union[str, Any]   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				{"WarmUp": WarmUp}
						return super(UpperCAmelCase__			,			cls   ).from_config(UpperCAmelCase__			,			custom_objects=UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Optional[int]			,			UpperCAmelCase__			:				Union[str, Any]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				List[str]   )       ->	Dict:
						super(UpperCAmelCase__			,			self   )._prepare_local(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				tf.constant(
						    self.weight_decay_rate			,			name="adam_weight_decay_rate"   )




	def   UpperCAmelCase_      ( self			:				Any			,			UpperCAmelCase__			:				Optional[int]			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				Optional[Any]   )       ->	List[Any]:
						__SCREAMING_SNAKE_CASE						   =				self._do_use_weight_decay(var.name   )
						if do_decay:
											return var.assign_sub(
											    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"]			,			use_locking=self._use_locking			,			)
						return tf.no_op()




	def   UpperCAmelCase_      ( self			:				Tuple			,			UpperCAmelCase__			:				Tuple			,			UpperCAmelCase__			:				int=None			,			**UpperCAmelCase__			:				Any   )       ->	Optional[int]:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				list(zip(*UpperCAmelCase__   )   )
						return super(UpperCAmelCase__			,			self   ).apply_gradients(zip(UpperCAmelCase__			,			UpperCAmelCase__   )			,			name=UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Dict			,			UpperCAmelCase__			:				Dict			,			UpperCAmelCase__			:				List[str]			,			UpperCAmelCase__			:				List[Any]   )       ->	Tuple:
						if apply_state is None:
											return self._decayed_lr_t[var_dtype], {}

						__SCREAMING_SNAKE_CASE						   =				apply_state or {}
						__SCREAMING_SNAKE_CASE						   =				apply_state.get((var_device, var_dtype)   )
						if coefficients is None:
											__SCREAMING_SNAKE_CASE						   =				self._fallback_apply_state(UpperCAmelCase__			,			UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				coefficients

						return coefficients["lr_t"], {"apply_state": apply_state}




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Union[str, Any]			,			UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				List[Any]=None   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self._get_lr(var.device			,			var.dtype.base_dtype			,			UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				self._decay_weights_op(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )
						with tf.control_dependencies([decay]   ):
											return super(UpperCAmelCase__			,			self   )._resource_apply_dense(UpperCAmelCase__			,			UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Union[str, Any]			,			UpperCAmelCase__			:				Optional[Any]			,			UpperCAmelCase__			:				List[Any]			,			UpperCAmelCase__			:				str			,			UpperCAmelCase__			:				Optional[int]=None   )       ->	Dict:
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				self._get_lr(var.device			,			var.dtype.base_dtype			,			UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				self._decay_weights_op(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__   )
						with tf.control_dependencies([decay]   ):
											return super(UpperCAmelCase__			,			self   )._resource_apply_sparse(UpperCAmelCase__			,			UpperCAmelCase__			,			UpperCAmelCase__			,			**UpperCAmelCase__   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	Dict:
						__SCREAMING_SNAKE_CASE						   =				super().get_config()
						config.update({"weight_decay_rate": self.weight_decay_rate}   )
						return config




	def   UpperCAmelCase_      ( self			:				Optional[Any]			,			UpperCAmelCase__			:				List[Any]   )       ->	List[Any]:
						if self.weight_decay_rate == 0:
											return False

						if self._include_in_weight_decay:
											for r in self._include_in_weight_decay:
																if re.search(UpperCAmelCase__			,			UpperCAmelCase__   ) is not None:
																					return True

						if self._exclude_from_weight_decay:
											for r in self._exclude_from_weight_decay:
																if re.search(UpperCAmelCase__			,			UpperCAmelCase__   ) is not None:
																					return False
						return True




class  UpperCamelCase_	( UpperCamelCase):

	"""simple docstring"""





	def __init__( self			:				Optional[Any]   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				[]
						__SCREAMING_SNAKE_CASE						   =				None




	@property
	def   UpperCAmelCase_      ( self			:				Optional[Any]   )       ->	Optional[Any]:
						if self._accum_steps is None:
											__SCREAMING_SNAKE_CASE						   =				tf.Variable(
											    tf.constant(0			,			dtype=tf.intaa   )			,			trainable=UpperCAmelCase__			,			synchronization=tf.VariableSynchronization.ON_READ			,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,			)

						return self._accum_steps.value()




	@property
	def   UpperCAmelCase_      ( self			:				Any   )       ->	Any:
						if not self._gradients:
											raise ValueError("The accumulator should be called first to initialize the gradients"   )
						return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]




	def __call__( self			:				Dict			,			UpperCAmelCase__			:				Optional[int]   )       ->	Union[str, Any]:
						if not self._gradients:
											__SCREAMING_SNAKE_CASE						   =				self.step  # Create the step variable.
											self._gradients.extend(
											    [
											        tf.Variable(
											            tf.zeros_like(UpperCAmelCase__   )			,			trainable=UpperCAmelCase__			,			synchronization=tf.VariableSynchronization.ON_READ			,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA			,			)
											        if gradient is not None
											        else gradient
											        for gradient in gradients
											    ]   )
						if len(UpperCAmelCase__   ) != len(self._gradients   ):
											raise ValueError(F"""Expected {len(self._gradients   )} gradients, but got {len(UpperCAmelCase__   )}"""   )

						for accum_gradient, gradient in zip(self._gradients			,			UpperCAmelCase__   ):
											if accum_gradient is not None and gradient is not None:
																accum_gradient.assign_add(UpperCAmelCase__   )

						self._accum_steps.assign_add(1   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Optional[int]:
						if not self._gradients:
											return
						self._accum_steps.assign(0   )
						for gradient in self._gradients:
											if gradient is not None:
																gradient.assign(tf.zeros_like(UpperCAmelCase__   )   )




"""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


a__				:							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    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


					warnings.warn(lowerCAmelCase_  ,						lowerCAmelCase_   )
					requires_backends(lowerCAmelCase_  ,						"sklearn"   )
					return (preds == labels).mean()




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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




def    UpperCAmelCase__       (lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_   ):




					'''simple docstring'''


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