
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     lowercase		( tf.keras.optimizers.schedules.LearningRateSchedule		):




							def __init__(    self       ,A__       ,A__       ,A__       ,A__ = 1.0       ,A__ = None       ,):
												super().__init__()
												lowercase						     =						initial_learning_rate
												lowercase						     =						warmup_steps
												lowercase						     =						power
												lowercase						     =						decay_schedule_fn
												lowercase						     =						name




							def __call__(    self       ,A__):
												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`.
																	lowercase						     =						tf.cast(A__       ,tf.floataa)
																	lowercase						     =						tf.cast(self.warmup_steps       ,tf.floataa)
																	lowercase						     =						global_step_float / warmup_steps_float
																	lowercase						     =						self.initial_learning_rate * tf.math.pow(A__       ,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=A__       ,)





							def 			A__				(    self):
												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__ = 0.9_99			,      lowerCAmelCase__ = 1E-8			,      lowerCAmelCase__ = None			,      lowerCAmelCase__ = None			,      lowerCAmelCase__ = 0.0			,      lowerCAmelCase__ = 1.0			,      lowerCAmelCase__ = None			,      ):
					'''simple docstring'''
					lowercase						     =						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:
										lowercase						     =						WarmUp(
										    initial_learning_rate=lowerCAmelCase__			,      decay_schedule_fn=lowerCAmelCase__			,      warmup_steps=lowerCAmelCase__			,      )
					if weight_decay_rate > 0.0:
										lowercase						     =						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:
										lowercase						     =						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     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self       ,A__ = 0.001       ,A__ = 0.9       ,A__ = 0.999       ,A__ = 1E-7       ,A__ = False       ,A__ = 0.0       ,A__ = None       ,A__ = None       ,A__ = "AdamWeightDecay"       ,**A__       ,):
												super().__init__(A__       ,A__       ,A__       ,A__       ,A__       ,A__       ,**A__)
												lowercase						     =						weight_decay_rate
												lowercase						     =						include_in_weight_decay
												lowercase						     =						exclude_from_weight_decay




							@classmethod
							def 			A__				(    cls       ,A__):
												lowercase						     =						{'''WarmUp''': WarmUp}
												return super(A__       ,cls).from_config(A__       ,custom_objects=A__)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												super(A__       ,self)._prepare_local(A__       ,A__       ,A__)
												lowercase						     =						tf.constant(
												    self.weight_decay_rate       ,name='''adam_weight_decay_rate''')




							def 			A__				(    self       ,A__       ,A__       ,A__):
												lowercase						     =						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 			A__				(    self       ,A__       ,A__=None       ,**A__):
												lowercase     ,						lowercase						     =						list(zip(*A__))
												return super(A__       ,self).apply_gradients(zip(A__       ,A__)       ,name=A__       ,**A__)




							def 			A__				(    self       ,A__       ,A__       ,A__):
												if apply_state is None:
																	return self._decayed_lr_t[var_dtype], {}

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

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




							def 			A__				(    self       ,A__       ,A__       ,A__=None):
												lowercase     ,						lowercase						     =						self._get_lr(var.device       ,var.dtype.base_dtype       ,A__)
												lowercase						     =						self._decay_weights_op(A__       ,A__       ,A__)
												with tf.control_dependencies([decay]):
																	return super(A__       ,self)._resource_apply_dense(A__       ,A__       ,**A__)




							def 			A__				(    self       ,A__       ,A__       ,A__       ,A__=None):
												lowercase     ,						lowercase						     =						self._get_lr(var.device       ,var.dtype.base_dtype       ,A__)
												lowercase						     =						self._decay_weights_op(A__       ,A__       ,A__)
												with tf.control_dependencies([decay]):
																	return super(A__       ,self)._resource_apply_sparse(A__       ,A__       ,A__       ,**A__)




							def 			A__				(    self):
												lowercase						     =						super().get_config()
												config.update({'''weight_decay_rate''': self.weight_decay_rate})
												return config





							def 			A__				(    self       ,A__):
												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(A__       ,A__) is not None:
																											return True

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







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							def __init__(    self):
												lowercase						     =						[]
												lowercase						     =						None




							@property
							def 			A__				(    self):
												if self._accum_steps is None:
																	lowercase						     =						tf.Variable(
																	    tf.constant(0       ,dtype=tf.intaa)       ,trainable=A__       ,synchronization=tf.VariableSynchronization.ON_READ       ,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,)

												return self._accum_steps.value()




							@property
							def 			A__				(    self):
												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       ,A__):
												if not self._gradients:
																	lowercase						     =						self.step  # Create the step variable.
																	self._gradients.extend(
																	    [
																	        tf.Variable(
																	            tf.zeros_like(A__)       ,trainable=A__       ,synchronization=tf.VariableSynchronization.ON_READ       ,aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,)
																	        if gradient is not None
																	        else gradient
																	        for gradient in gradients
																	    ])
												if len(A__) != len(self._gradients):
																	raise ValueError(f'Expected {len(self._gradients)} gradients, but got {len(A__)}')

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

												self._accum_steps.assign_add(1)





							def 			A__				(    self):
												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(A__))






import numpy as np

import datasets


lowercase__		:Dict			  =   "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowercase__		:List[Any]			  =   "\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowercase__		:Dict			  =   "\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION		)
class     lowercase		( datasets.Metric		):




							def 			A__				(    self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
												        {
												            '''X''': datasets.Sequence(datasets.Value('''float'''       ,id='''sequence''')       ,id='''X'''),
												        })       ,)





							def 			A__				(    self       ,A__       ,A__):
												# convert to numpy arrays
												lowercase						     =						np.array(A__)
												lowercase						     =						np.array(A__)

												# Assert that arrays are 2D
												if len(X.shape) != 2:
																	raise ValueError('''Expected `X` to be a 2D vector''')
												if len(reference_distribution.shape) != 2:
																	raise ValueError('''Expected `reference_distribution` to be a 2D vector''')
												if reference_distribution.shape[0] < 2:
																	raise ValueError(
																	    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''')

												# Get mahalanobis distance for each prediction
												lowercase						     =						X - np.mean(A__)
												lowercase						     =						np.cov(reference_distribution.T)
												try:
																	lowercase						     =						np.linalg.inv(A__)
												except np.linalg.LinAlgError:
																	lowercase						     =						np.linalg.pinv(A__)
												lowercase						     =						np.dot(A__       ,A__)
												lowercase						     =						np.dot(A__       ,X_minus_mu.T).diagonal()

												return {"mahalanobis": mahal_dist}




