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   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Any	,					__snake_case     :    List[str]	,					__snake_case     :    int	,					__snake_case     :    Union[str, Any]		):
										self.assertEqual(len(__snake_case		)	,					len(__snake_case		)		)
										for a, b in zip(__snake_case	,					__snake_case		):
													self.assertAlmostEqual(__snake_case	,					__snake_case	,					delta=__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Dict						  =     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(__snake_case		):
													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     :    Union[str, Any]		):
										lowerCamelCase						:List[Any]						  =     None
										ops.enable_eager_execution_internal()
										lowerCamelCase						:Optional[Any]						  =     tf.config.list_physical_devices('''CPU'''		)
										if len(__snake_case		) == 1:
													tf.config.set_logical_device_configuration(
													    physical_devices[0]	,					[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
										lowerCamelCase						:Union[str, Any]						  =     tf.config.list_logical_devices(device_type='''CPU'''		)
										lowerCamelCase						:Any						  =     tf.distribute.MirroredStrategy(devices=devices[:2]		)

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

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

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

										@tf.function
										def accumulate(__snake_case     :    Union[str, Any]	,					__snake_case     :    Optional[int]		):
													with strategy.scope():
																lowerCamelCase						:Optional[Any]						  =     strategy.experimental_local_results(__snake_case		)
																local_variables[0].assign(__snake_case		)
																local_variables[1].assign(__snake_case		)
																strategy.run(__snake_case	,					args=(gradient_placeholder,)		)

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

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




import math





def  _lowerCamelCase					(   a_				: int):

			if 1 < number < 4:
						# 2 and 3 are primes
						return True
			elif number < 2 or number % 2 == 0 or number % 3 == 0:
						# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
						return False

			# All primes number are in format of 6k +/- 1
			for i in range(5						,			int(math.sqrt(a_) + 1)						,			6):
						if number % i == 0 or number % (i + 2) == 0:
									return False
			return True





def  _lowerCamelCase					(   a_				: float = 0.1):
			lowerCamelCase						:Dict						  =     3
			lowerCamelCase						:List[Any]						  =     3

			while primes / (2 * j - 1) >= ratio:
						for i in range(j * j + j + 1						,			(j + 2) * (j + 2)						,			j + 1):
									primes += is_prime(a_)
						j += 2
			return j


if __name__ == "__main__":
			import doctest

			doctest.testmod()



