

'''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        a__  (							tf.keras.optimizers.schedules.LearningRateSchedule  ):


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase = 1.0      ,    _UpperCamelCase = None      ,    ):





      """simple docstring"""





      super().__init__()
      _lowercase      :				Optional[int]      					=       initial_learning_rate
      _lowercase      :				Dict      					=       warmup_steps
      _lowercase      :				str      					=       power
      _lowercase      :				List[str]      					=       decay_schedule_fn
      _lowercase      :				Optional[int]      					=       name


   def __call__(			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      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      :				Any      					=       tf.cast(_UpperCamelCase      ,    tf.floataa  )
         _lowercase      :				Union[str, Any]      					=       tf.cast(self.warmup_steps      ,    tf.floataa  )
         _lowercase      :				Tuple      					=       global_step_float / warmup_steps_float
         _lowercase      :				Dict      					=       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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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  _A		(     snake_case		,							snake_case		,							snake_case		,							snake_case = 0.0		,							snake_case = 0.9		,							snake_case = 0.999		,							snake_case = 1E-8		,							snake_case = None		,							snake_case = None		,							snake_case = 0.0		,							snake_case = 1.0		,							snake_case = None		,							)      ->     Optional[int]:
   _lowercase      :				Optional[int]      					=       tf.keras.optimizers.schedules.PolynomialDecay(
       initial_learning_rate=snake_case		,							decay_steps=num_train_steps - num_warmup_steps		,							end_learning_rate=init_lr * min_lr_ratio		,							power=snake_case		,							)
   if num_warmup_steps:
      _lowercase      :				Union[str, Any]      					=       WarmUp(
          initial_learning_rate=snake_case		,							decay_schedule_fn=snake_case		,							warmup_steps=snake_case		,							)
   if weight_decay_rate > 0.0:
      _lowercase      :				Dict      					=       AdamWeightDecay(
          learning_rate=snake_case		,							weight_decay_rate=snake_case		,							beta_a=snake_case		,							beta_a=snake_case		,							epsilon=snake_case		,							clipnorm=snake_case		,							global_clipnorm=snake_case		,							exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]		,							include_in_weight_decay=snake_case		,							)
   else:
      _lowercase      :				List[str]      					=       tf.keras.optimizers.Adam(
          learning_rate=snake_case		,							beta_a=snake_case		,							beta_a=snake_case		,							epsilon=snake_case		,							clipnorm=snake_case		,							global_clipnorm=snake_case		,							)
   # 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        a__  (							lowerCamelCase_  ):


   def __init__(			self      ,    _UpperCamelCase = 0.0_0_1      ,    _UpperCamelCase = 0.9      ,    _UpperCamelCase = 0.9_9_9      ,    _UpperCamelCase = 1E-7      ,    _UpperCamelCase = False      ,    _UpperCamelCase = 0.0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = "AdamWeightDecay"      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )
      _lowercase      :				Any      					=       weight_decay_rate
      _lowercase      :				Union[str, Any]      					=       include_in_weight_decay
      _lowercase      :				Dict      					=       exclude_from_weight_decay


   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       {"WarmUp": WarmUp}
      return super(_UpperCamelCase      ,    cls  ).from_config(_UpperCamelCase      ,    custom_objects=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super(_UpperCamelCase      ,    self  )._prepare_local(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  )
      _lowercase      :				Optional[Any]      					=       tf.constant(
          self.weight_decay_rate      ,    name="adam_weight_decay_rate"  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				str      					=       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 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       list(zip(*_UpperCamelCase  )  )
      return super(_UpperCamelCase      ,    self  ).apply_gradients(zip(_UpperCamelCase      ,    _UpperCamelCase  )      ,    name=_UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      if apply_state is None:
         return self._decayed_lr_t[var_dtype], {}

      _lowercase      :				List[str]      					=       apply_state or {}
      _lowercase      :				int      					=       apply_state.get((var_device, var_dtype)  )
      if coefficients is None:
         _lowercase      :				Any      					=       self._fallback_apply_state(_UpperCamelCase      ,    _UpperCamelCase  )
         _lowercase      :				List[str]      					=       coefficients

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


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self._get_lr(var.device      ,    var.dtype.base_dtype      ,    _UpperCamelCase  )
      _lowercase      :				str      					=       self._decay_weights_op(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  )
      with tf.control_dependencies([decay]  ):
         return super(_UpperCamelCase      ,    self  )._resource_apply_dense(_UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self._get_lr(var.device      ,    var.dtype.base_dtype      ,    _UpperCamelCase  )
      _lowercase      :				Optional[int]      					=       self._decay_weights_op(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  )
      with tf.control_dependencies([decay]  ):
         return super(_UpperCamelCase      ,    self  )._resource_apply_sparse(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    **_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Tuple      					=       super().get_config()
      config.update({"weight_decay_rate": self.weight_decay_rate}  )
      return config






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase  ):





      """simple docstring"""





      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        a__  (							lowerCamelCase_  ):


   def __init__(			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       []
      _lowercase      :				Dict      					=       None


   @property
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self._accum_steps is None:
         _lowercase      :				str      					=       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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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      ,    _UpperCamelCase  ):





      """simple docstring"""





      if not self._gradients:
         _lowercase      :				Optional[Any]      					=       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 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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'''
from sklearn.metrics import mean_squared_error

import datasets


_snake_case         =  '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

_snake_case         =  '\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


_snake_case         =  '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'


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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      return datasets.MetricInfo(
          description=_DESCRIPTION      ,    citation=_CITATION      ,    inputs_description=_KWARGS_DESCRIPTION      ,    features=datasets.Features(self._get_feature_types()  )      ,    reference_urls=[
              "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html"
          ]      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      if self.config_name == "multilist":
         return {
             "predictions": datasets.Sequence(datasets.Value("float"  )  ),
             "references": datasets.Sequence(datasets.Value("float"  )  ),
         }
      else:
         return {
             "predictions": datasets.Value("float"  ),
             "references": datasets.Value("float"  ),
         }






   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None      ,    _UpperCamelCase="uniform_average"      ,    _UpperCamelCase=True  ):





      """simple docstring"""





      _lowercase      :				str      					=       mean_squared_error(
          _UpperCamelCase      ,    _UpperCamelCase      ,    sample_weight=_UpperCamelCase      ,    multioutput=_UpperCamelCase      ,    squared=_UpperCamelCase  )

      return {"mse": mse}






