import matplotlib.pyplot as plt
import numpy as np







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		Dict			    =    len(__A						)  # Number of training samples
    _lowerCamelCase  :		Dict			    =    np.eye(__A						)  # Initialize weights as identity matrix
    for j in range(__A						):
        _lowerCamelCase  :		Optional[int]			    =    point - x_train[j]
        _lowerCamelCase  :		Optional[int]			    =    np.exp(diff @ diff.T / (-2.0 * tau**2)						)

    return weights







def     A__	(       __A ,			__A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		str			    =    weight_matrix(__A ,			__A ,			__A						)
    _lowerCamelCase  :		List[Any]			    =    np.linalg.inv(x_train.T @ weight_mat @ x_train						) @ (
        x_train.T @ weight_mat @ y_train.T
    )

    return weight







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    np.zeros(len(__A						)						)  # Initialize array of predictions
    for i, item in enumerate(__A						):
        _lowerCamelCase  :		Dict			    =    item @ local_weight(__A ,			__A ,			__A ,			__A						)

    return y_pred







def     A__	(       __A ,			__A ,			__A						):







    '''simple docstring'''
    import seaborn as sns

    _lowerCamelCase  :		Dict			    =    sns.load_dataset(__A						)
    _lowerCamelCase  :		Dict			    =    np.array(data[x_name]						)
    _lowerCamelCase  :		List[Any]			    =    np.array(data[y_name]						)

    _lowerCamelCase  :		str			    =    np.ones(len(__A						)						)

    # pairing elements of one and x_data
    _lowerCamelCase  :		List[str]			    =    np.column_stack((one, x_data)						)

    return x_train, x_data, y_data







def     A__	(       __A ,			__A ,			__A ,			__A ,			__A ,			__A ,			):







    '''simple docstring'''
    _lowerCamelCase  :		Tuple			    =    np.sort(__A ,			axis=0						)
    plt.scatter(__A ,			__A ,			color="""blue"""						)
    plt.plot(
        x_train_sorted[:, 1] ,			preds[x_train[:, 1].argsort(0						)] ,			color="""yellow""" ,			linewidth=5 ,			)
    plt.title("""Local Weighted Regression"""						)
    plt.xlabel(__A						)
    plt.ylabel(__A						)
    plt.show()


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    # Demo with a dataset from the seaborn module
    lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase      :				str     			=load_data("tips", "total_bill", "tip")
    lowerCAmelCase      :				Optional[Any]     			=local_weight_regression(training_data_x, tip, 5)
    plot_preds(training_data_x, predictions, total_bill, tip, "total_bill", "tip")





import torch

from accelerate import Accelerator, DistributedType




class 						__snake_case						:





      '''simple docstring'''






      def __enter__(       self  :					Union[str, Any])					->Dict:


          """simple docstring"""


          if self.enabled:
              _lowerCamelCase  :		str			    =    self.model.no_sync()
              self.model_sync_obj.__enter__()

          return self



      def __exit__(       self  :					List[str]     ,	_UpperCamelCase  :					Any     ,	_UpperCamelCase  :					Union[str, Any]     ,	_UpperCamelCase  :					int)					->Optional[Any]:


          """simple docstring"""


          if self.enabled:
              # Average all models on exit
              self._sync_and_avg_model_params()
              self.model_sync_obj.__exit__(_UpperCamelCase     ,	_UpperCamelCase     ,	_UpperCamelCase)



      def __init__(       self  :					Union[str, Any]     ,	_UpperCamelCase  :					Accelerator     ,	_UpperCamelCase  :					torch.nn.Module     ,	_UpperCamelCase  :					int     ,	_UpperCamelCase  :					bool = True)					->int:


          """simple docstring"""


          if accelerator.distributed_type not in [
              DistributedType.NO,
              DistributedType.MULTI_CPU,
              DistributedType.MULTI_GPU,
          ]:
              raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""")
          _lowerCamelCase  :		str			    =    enabled and accelerator.distributed_type != DistributedType.NO
          _lowerCamelCase  :		Optional[int]			    =    0
          if self.enabled:
              _lowerCamelCase  :		Union[str, Any]			    =    accelerator
              _lowerCamelCase  :		int			    =    model
              _lowerCamelCase  :		int			    =    local_sgd_steps



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Union[str, Any])					->Union[str, Any]:


          """simple docstring"""


          self.num_steps += 1
          if not self.enabled:
              return

          if self.num_steps % self.local_sgd_steps == 0:
              self._sync_and_avg_model_params()




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


          """simple docstring"""


          self.accelerator.wait_for_everyone()
          with self.accelerator.autocast():
              for param in self.model.parameters():
                  _lowerCamelCase  :		Union[str, Any]			    =    self.accelerator.reduce(param.data     ,	reduction="""mean""")




