import matplotlib.pyplot as plt
import numpy as np




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       np.ndarray:







      '''simple docstring'''


      UpperCAmelCase__			=						len(__A							)  # Number of training samples
      UpperCAmelCase__			=						np.eye(__A							)  # Initialize weights as identity matrix
      for j in range(__A							):
            UpperCAmelCase__			=						point - x_train[j]
            UpperCAmelCase__			=						np.exp(diff @ diff.T / (-2.0 * tau**2)							)

      return weights




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A							)  ->       np.ndarray:







      '''simple docstring'''


      UpperCAmelCase__			=						weight_matrix(__A,						__A,						__A							)
      UpperCAmelCase__			=						np.linalg.inv(x_train.T @ weight_mat @ x_train							) @ (
          x_train.T @ weight_mat @ y_train.T
      )

      return weight




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       np.ndarray:







      '''simple docstring'''


      UpperCAmelCase__			=						np.zeros(len(__A							)							)  # Initialize array of predictions
      for i, item in enumerate(__A							):
            UpperCAmelCase__			=						item @ local_weight(__A,						__A,						__A,						__A							)

      return y_pred




def      lowerCAmelCase_				(  __A,						__A,						__A							)  ->       tuple[np.ndarray, np.ndarray, np.ndarray]:







      '''simple docstring'''


      import seaborn as sns

      UpperCAmelCase__			=						sns.load_dataset(__A							)
      UpperCAmelCase__			=						np.array(data[x_name]							)
      UpperCAmelCase__			=						np.array(data[y_name]							)

      UpperCAmelCase__			=						np.ones(len(__A							)							)

      # pairing elements of one and x_data
      UpperCAmelCase__			=						np.column_stack((one, x_data)							)

      return x_train, x_data, y_data




def      lowerCAmelCase_				(  __A,						__A,						__A,						__A,						__A,						__A,						)  ->       None:







      '''simple docstring'''


      UpperCAmelCase__			=						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
      UpperCamelCase__							,							UpperCamelCase__							,							UpperCamelCase__       							=      load_data('tips', 'total_bill', 'tip')
      UpperCamelCase__       							=      local_weight_regression(training_data_x, tip, 5)
      plot_preds(training_data_x, predictions, total_bill, tip, 'total_bill', 'tip')




import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCamelCase__       							=      namedtuple('ExampleNamedTuple', 'a b c')





class   A							(       unittest.TestCase  ):


   def     lowercase_   (self  :		str	)		->       List[Any]:
         """simple docstring"""



         UpperCAmelCase__			=						torch.randn(5 ,						2	)
         UpperCAmelCase__			=						torch.device("cuda"	) if torch.cuda.is_available() else torch.device("cpu"	)

         UpperCAmelCase__			=						send_to_device(__UpperCAmelCase ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta.cpu() ,						__UpperCAmelCase	)	)

         UpperCAmelCase__			=						send_to_device((tensor, [tensor, tensor], 1) ,						__UpperCAmelCase	)
         self.assertIsInstance(__UpperCAmelCase ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta[0].cpu() ,						__UpperCAmelCase	)	)
         self.assertIsInstance(resulta[1] ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta[1][0].cpu() ,						__UpperCAmelCase	)	)
         self.assertTrue(torch.equal(resulta[1][1].cpu() ,						__UpperCAmelCase	)	)
         self.assertEqual(resulta[2] ,						1	)

         UpperCAmelCase__			=						send_to_device({"a": tensor, "b": [tensor, tensor], "c": 1} ,						__UpperCAmelCase	)
         self.assertIsInstance(__UpperCAmelCase ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta["a"].cpu() ,						__UpperCAmelCase	)	)
         self.assertIsInstance(resulta["b"] ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta["b"][0].cpu() ,						__UpperCAmelCase	)	)
         self.assertTrue(torch.equal(resulta["b"][1].cpu() ,						__UpperCAmelCase	)	)
         self.assertEqual(resulta["c"] ,						1	)

         UpperCAmelCase__			=						send_to_device(ExampleNamedTuple(a=__UpperCAmelCase ,						b=[tensor, tensor] ,						c=1	) ,						__UpperCAmelCase	)
         self.assertIsInstance(__UpperCAmelCase ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta.a.cpu() ,						__UpperCAmelCase	)	)
         self.assertIsInstance(resulta.b ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta.b[0].cpu() ,						__UpperCAmelCase	)	)
         self.assertTrue(torch.equal(resulta.b[1].cpu() ,						__UpperCAmelCase	)	)
         self.assertEqual(resulta.c ,						1	)

         UpperCAmelCase__			=						send_to_device(UserDict({"a": tensor, "b": [tensor, tensor], "c": 1}	) ,						__UpperCAmelCase	)
         self.assertIsInstance(__UpperCAmelCase ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta["a"].cpu() ,						__UpperCAmelCase	)	)
         self.assertIsInstance(resulta["b"] ,						__UpperCAmelCase	)
         self.assertTrue(torch.equal(resulta["b"][0].cpu() ,						__UpperCAmelCase	)	)
         self.assertTrue(torch.equal(resulta["b"][1].cpu() ,						__UpperCAmelCase	)	)
         self.assertEqual(resulta["c"] ,						1	)


   def     lowercase_   (self  :		int	)		->       int:
         """simple docstring"""



         with self.assertRaises(__UpperCAmelCase	) as cm:
               UpperCAmelCase__			=						recursively_apply(torch.tensor ,						(torch.tensor(1	), 1) ,						error_on_other_type=__UpperCAmelCase	)
         self.assertEqual(
             str(cm.exception	) ,						"Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed." ,						)


   def     lowercase_   (self  :		List[str]	)		->       Any:
         """simple docstring"""



         UpperCAmelCase__			=						torch.tensor([1, 2, 3, 4, 5]	)
         self.assertEqual(listify(__UpperCAmelCase	) ,						[1, 2, 3, 4, 5]	)

         UpperCAmelCase__			=						torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]	)
         self.assertEqual(listify(__UpperCAmelCase	) ,						[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]]	)

         UpperCAmelCase__			=						torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]	)
         self.assertEqual(
             listify(__UpperCAmelCase	) ,						[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 1_0]], [[1_1, 1_2, 1_3, 1_4, 1_5], [1_6, 1_7, 1_8, 1_9, 2_0]]]	)


   def     lowercase_   (self  :		Union[str, Any]	)		->       Dict:
         """simple docstring"""



         with patch_environment(aa=1 ,						BB=2	):
               self.assertEqual(os.environ.get("AA"	) ,						"1"	)
               self.assertEqual(os.environ.get("BB"	) ,						"2"	)

         self.assertNotIn("AA" ,						os.environ	)
         self.assertNotIn("BB" ,						os.environ	)


   def     lowercase_   (self  :		int	)		->       str:
         """simple docstring"""



         UpperCAmelCase__			=						RegressionModel()
         UpperCAmelCase__			=						model.forward
         UpperCAmelCase__			=						convert_outputs_to_fpaa(model.forward	)
         UpperCAmelCase__			=						extract_model_from_parallel(__UpperCAmelCase ,						keep_fpaa_wrapper=__UpperCAmelCase	)
         UpperCAmelCase__			=						pickle.dumps(__UpperCAmelCase	)


   @require_cuda
   def     lowercase_   (self  :		List[str]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						RegressionModel()
         UpperCAmelCase__			=						model.forward
         UpperCAmelCase__			=						torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
         UpperCAmelCase__			=						convert_outputs_to_fpaa(model.forward	)
         UpperCAmelCase__			=						extract_model_from_parallel(__UpperCAmelCase ,						keep_fpaa_wrapper=__UpperCAmelCase	)
         UpperCAmelCase__			=						pickle.dumps(__UpperCAmelCase	)


   @require_cuda
   @require_torch_min_version(version="2.0"	)
   def     lowercase_   (self  :		Optional[int]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						RegressionModel()
         UpperCAmelCase__			=						model.forward
         UpperCAmelCase__			=						torch.cuda.amp.autocast(dtype=torch.floataa	)(model.forward	)
         UpperCAmelCase__			=						convert_outputs_to_fpaa(model.forward	)
         UpperCAmelCase__			=						torch.compile(model.forward ,						backend="inductor"	)
         UpperCAmelCase__			=						torch.randn(4 ,						1_0	).cuda()
         UpperCAmelCase__			=						model(__UpperCAmelCase	)


   def     lowercase_   (self  :		List[Any]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						RegressionModel()
         # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
         UpperCAmelCase__			=						torch.nn.parallel.DataParallel(__UpperCAmelCase	)
         UpperCAmelCase__			=						extract_model_from_parallel(__UpperCAmelCase	)

         self.assertEqual(__UpperCAmelCase ,						__UpperCAmelCase	)


   @require_torch_min_version(version="2.0"	)
   def     lowercase_   (self  :		Union[str, Any]	)		->       Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__			=						RegressionModel()
         UpperCAmelCase__			=						torch.compile(__UpperCAmelCase	)

         # could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
         UpperCAmelCase__			=						torch.nn.parallel.DataParallel(__UpperCAmelCase	)
         UpperCAmelCase__			=						torch.compile(__UpperCAmelCase	)
         UpperCAmelCase__			=						extract_model_from_parallel(__UpperCAmelCase	)

         self.assertEqual(compiled_model._orig_mod ,						compiled_model_unwrapped._orig_mod	)





   def     lowercase_   (self  :		int	)		->       int:
         """simple docstring"""



         self.assertEqual(find_device([1, "a", torch.tensor([1, 2, 3]	)]	) ,						torch.device("cpu"	)	)
         self.assertEqual(find_device({"a": 1, "b": torch.tensor([1, 2, 3]	)}	) ,						torch.device("cpu"	)	)
         self.assertIsNone(find_device([1, "a"]	)	)



