


'''simple docstring'''



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 		_snake_case					(     unittest.TestCase	):




     def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
        self.assertEqual(len(__lowercase)							,							len(__lowercase))
        for a, b in zip(__lowercase							,							__lowercase):
           self.assertAlmostEqual(__lowercase							,							__lowercase							,							delta=__lowercase)




     def 				snake_case__						(      self):
        UpperCAmelCase__  :					int        =     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(__lowercase):
           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 				snake_case__						(      self):
        UpperCAmelCase__  :					str        =     None
        ops.enable_eager_execution_internal()
        UpperCAmelCase__  :					Tuple        =     tf.config.list_physical_devices("""CPU""")
        if len(__lowercase) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]							,							[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
        UpperCAmelCase__  :					str        =     tf.config.list_logical_devices(device_type="""CPU""")
        UpperCAmelCase__  :					Optional[int]        =     tf.distribute.MirroredStrategy(devices=devices[:2])

        with strategy.scope():
           UpperCAmelCase__  :					Optional[Any]        =     GradientAccumulator()
           UpperCAmelCase__  :					Any        =     tf.Variable([4.0, 3.0])
           UpperCAmelCase__   ,       UpperCAmelCase__  :					str        =     create_optimizer(5e-5							,							10							,							5)
           UpperCAmelCase__  :					str        =     tf.Variable([0.0, 0.0]							,							trainable=__lowercase)

        def accumulate_on_replica(_lowerCamelCase):
           accumulator([gradient])

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

        @tf.function
        def accumulate(_lowerCamelCase							,							_lowerCamelCase):
           with strategy.scope():
              UpperCAmelCase__  :					str        =     strategy.experimental_local_results(__lowercase)
              local_variables[0].assign(__lowercase)
              local_variables[1].assign(__lowercase)
              strategy.run(__lowercase							,							args=(gradient_placeholder,))

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

        def _check_local_values(_lowerCamelCase							,							_lowerCamelCase):
           UpperCAmelCase__  :					Optional[int]        =     strategy.experimental_local_results(accumulator._gradients[0])
           self.assertListAlmostEqual(values[0].value()							,							__lowercase							,							tol=1e-2)
           self.assertListAlmostEqual(values[1].value()							,							__lowercase							,							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'''



from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def     _UpperCamelCase      (		UpperCamelCase__					):

			# A local function to see if a dot lands in the circle.
			def is_in_circle(UpperCamelCase__ ,      UpperCamelCase__					) -> bool:
						UpperCAmelCase__  :					List[str]        =     sqrt((x**2) + (y**2)					)
						# Our circle has a radius of 1, so a distance
						# greater than 1 would land outside the circle.
						return distance_from_centre <= 1

			# The proportion of guesses that landed in the circle
			UpperCAmelCase__  :					Optional[Any]        =     mean(
			    int(is_in_circle(uniform(-1.0 ,      1.0					) ,      uniform(-1.0 ,      1.0					)					)					)
			    for _ in range(UpperCamelCase__					)					)
			# The ratio of the area for circle to square is pi/4.
			UpperCAmelCase__  :					int        =     proportion * 4
			print(f'''The estimated value of pi is {pi_estimate}'''					)
			print(f'''The numpy value of pi is {pi}'''					)
			print(f'''The total error is {abs(pi - pi_estimate					)}'''					)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0 ,      ):
			return mean(
			    function_to_integrate(uniform(UpperCamelCase__ ,      UpperCamelCase__					)					) for _ in range(UpperCamelCase__					)					) * (max_value - min_value)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ = 0.0 ,      UpperCamelCase__ = 1.0					):

			def identity_function(UpperCamelCase__					) -> float:
						return x

			UpperCAmelCase__  :					List[str]        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[int]        =     (max_value * max_value - min_value * min_value) / 2

			print("""******************"""					)
			print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''					)
			print(f'''Estimated value is {estimated_value}'''					)
			print(f'''Expected value is {expected_value}'''					)
			print(f'''Total error is {abs(estimated_value - expected_value					)}'''					)
			print("""******************"""					)







def     _UpperCamelCase      (		UpperCamelCase__					):

			def function_to_integrate(UpperCamelCase__					) -> float:
						return sqrt(4.0 - x * x					)

			UpperCAmelCase__  :					Dict        =     area_under_curve_estimator(
			    UpperCamelCase__ ,      UpperCamelCase__ ,      0.0 ,      2.0					)

			print("""******************"""					)
			print("""Estimating pi using area_under_curve_estimator"""					)
			print(f'''Estimated value is {estimated_value}'''					)
			print(f'''Expected value is {pi}'''					)
			print(f'''Total error is {abs(estimated_value - pi					)}'''					)
			print("""******************"""					)


if __name__ == "__main__":
		import doctest

		doctest.testmod()