


import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf
   from tensorflow.python.eager import context
   from tensorflow.python.framework import ops

   from transformers import GradientAccumulator, create_optimizer
@require_tf
class 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:List[Any]     ,		_a:Dict     ,		_a:List[Any]   ):
       self.assertEqual(len(_a   )     ,		len(_a   )   )
       for a, b in zip(_a     ,		_a   ):
         self.assertAlmostEqual(_a     ,		_a     ,		delta=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__						= GradientAccumulator()
       accumulator([tf.constant([1.0, 2.0]   )]   )
       accumulator([tf.constant([-2.0, 1.0]   )]   )
       accumulator([tf.constant([-1.0, 2.0]   )]   )
       with self.assertRaises(_a   ):
         accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
       self.assertEqual(accumulator.step     ,		3   )
       self.assertEqual(len(accumulator.gradients   )     ,		1   )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,		[-2.0, 5.0]     ,		tol=1e-2   )
       accumulator.reset()
       self.assertEqual(accumulator.step     ,		0   )
       self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()     ,		[0.0, 0.0]     ,		tol=1e-2   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= None
       ops.enable_eager_execution_internal()
       snake_case__						= tf.config.list_physical_devices('''CPU'''   )
       if len(_a   ) == 1:
         tf.config.set_logical_device_configuration(
             physical_devices[0]     ,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
       snake_case__						= tf.config.list_logical_devices(device_type='''CPU'''   )
       snake_case__						= tf.distribute.MirroredStrategy(devices=devices[:2]   )

       with strategy.scope():
         snake_case__						= GradientAccumulator()
         snake_case__						= tf.Variable([4.0, 3.0]   )
         snake_case__				,				snake_case__						= create_optimizer(5e-5     ,		10     ,		5   )
         snake_case__						= tf.Variable([0.0, 0.0]     ,		trainable=_a   )

       def accumulate_on_replica(_a:Any   ):
         accumulator([gradient]   )

       def apply_on_replica():
         optimizer.apply_gradients(list(zip(accumulator.gradients     ,		[variable]   )   )   )

       @tf.function
       def accumulate(_a:Optional[int]     ,		_a:List[str]   ):
         with strategy.scope():
           snake_case__						= strategy.experimental_local_results(_a   )
           local_variables[0].assign(_a   )
           local_variables[1].assign(_a   )
           strategy.run(_a     ,		args=(gradient_placeholder,)   )

       @tf.function
       def apply_grad():
         with strategy.scope():
           strategy.run(_a   )

       def _check_local_values(_a:Any     ,		_a:Any   ):
         snake_case__						= strategy.experimental_local_results(accumulator._gradients[0]   )
         self.assertListAlmostEqual(values[0].value()     ,		_a     ,		tol=1e-2   )
         self.assertListAlmostEqual(values[1].value()     ,		_a     ,		tol=1e-2   )

       accumulate([1.0, 2.0]     ,		[-1.0, 1.0]   )
       accumulate([3.0, -1.0]     ,		[-1.0, -1.0]   )
       accumulate([-2.0, 2.0]     ,		[3.0, -2.0]   )
       self.assertEqual(accumulator.step     ,		3   )
       _check_local_values([2.0, 3.0]     ,		[1.0, -2.0]   )
       apply_grad()
       self.assertListAlmostEqual(variable.value()     ,		[4.0, 3.0]     ,		tol=1e-2   )
       accumulator.reset()
       self.assertEqual(accumulator.step     ,		0   )
       _check_local_values([0.0, 0.0]     ,		[0.0, 0.0]   )


'''simple docstring'''


import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def    lowerCAmelCase       (		UpperCamelCase__					: dict							):


	"""simple docstring"""
	return (data["data"], data["target"])





def    lowerCAmelCase       (		UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray     ,       UpperCamelCase__					: np.ndarray							):


	"""simple docstring"""
	__UpperCAmelCase      					=      XGBRegressor(verbosity=0     ,       random_state=4_2							)
	xgb.fit(UpperCamelCase__     ,       UpperCamelCase__							)
	# Predict target for test data
	__UpperCAmelCase      					=      xgb.predict(UpperCamelCase__							)
	__UpperCAmelCase      					=      predictions.reshape(len(UpperCamelCase__							)     ,       1							)
	return predictions





def    lowerCAmelCase       (		):


	"""simple docstring"""
	__UpperCAmelCase      					=      fetch_california_housing()
	__UpperCAmelCase					,      __UpperCAmelCase      					=      data_handling(UpperCamelCase__							)
	__UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase					,      __UpperCAmelCase      					=      train_test_split(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       test_size=0.25     ,       random_state=1							)
	__UpperCAmelCase      					=      xgboost(UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	# Error printing
	print(f"""Mean Absolute Error : {mean_absolute_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)
	print(f"""Mean Square Error  : {mean_squared_error(UpperCamelCase__     ,       UpperCamelCase__							)}"""							)


if __name__ == "__main__":
					import doctest

					doctest.testmod(verbose=True)
					main()
