


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  _a							(    unittest.TestCase):




	def 							UpperCAmelCase__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		Dict		, _SCREAMING_SNAKE_CASE						:		Optional[int]		, _SCREAMING_SNAKE_CASE						:		Dict		)->	str:
			self.assertEqual(len(_SCREAMING_SNAKE_CASE		)		, len(_SCREAMING_SNAKE_CASE		)		)
			for a, b in zip(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		):
					self.assertAlmostEqual(_SCREAMING_SNAKE_CASE		, _SCREAMING_SNAKE_CASE		, delta=_SCREAMING_SNAKE_CASE		)




	def 							UpperCAmelCase__( self						:		Any		)->	Optional[int]:
			lowerCAmelCase__       :      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(_SCREAMING_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 							UpperCAmelCase__( self						:		Any		)->	Union[str, Any]:
			lowerCAmelCase__       :      Tuple   					= None
			ops.enable_eager_execution_internal()
			lowerCAmelCase__       :      Tuple   					= tf.config.list_physical_devices('''CPU'''		)
			if len(_SCREAMING_SNAKE_CASE		) == 1:
					tf.config.set_logical_device_configuration(
					    physical_devices[0]		, [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]		)
			lowerCAmelCase__       :      Optional[int]   					= tf.config.list_logical_devices(device_type='''CPU'''		)
			lowerCAmelCase__       :      Any   					= tf.distribute.MirroredStrategy(devices=devices[:2]		)

			with strategy.scope():
					lowerCAmelCase__       :      Optional[int]   					= GradientAccumulator()
					lowerCAmelCase__       :      List[str]   					= tf.Variable([4.0, 3.0]		)
					lowerCAmelCase__					,     lowerCAmelCase__       :      Dict   					= create_optimizer(5E-5		, 10		, 5		)
					lowerCAmelCase__       :      int   					= tf.Variable([0.0, 0.0]		, trainable=_SCREAMING_SNAKE_CASE		)

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

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

			@tf.function
			def accumulate(_SCREAMING_SNAKE_CASE						:		Any		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		):
					with strategy.scope():
							lowerCAmelCase__       :      Optional[Any]   					= strategy.experimental_local_results(_SCREAMING_SNAKE_CASE		)
							local_variables[0].assign(_SCREAMING_SNAKE_CASE		)
							local_variables[1].assign(_SCREAMING_SNAKE_CASE		)
							strategy.run(_SCREAMING_SNAKE_CASE		, args=(gradient_placeholder,)		)

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

			def _check_local_values(_SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		List[Any]		):
					lowerCAmelCase__       :      Optional[Any]   					= strategy.experimental_local_results(accumulator._gradients[0]		)
					self.assertListAlmostEqual(values[0].value()		, _SCREAMING_SNAKE_CASE		, tol=1E-2		)
					self.assertListAlmostEqual(values[1].value()		, _SCREAMING_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 random

from .binary_exp_mod import bin_exp_mod






def 					lowerCamelCase_   (	_a     ,       _a=1_000	):




		"""simple docstring"""






		if n < 2:
				return False

		if n % 2 == 0:
				return n == 2

		# this means n is odd
		lowerCAmelCase__       :      int   					= n - 1
		lowerCAmelCase__       :      Any   					= 0
		while d % 2 == 0:
				d /= 2
				exp += 1

		# n - 1=d*(2**exp)
		lowerCAmelCase__       :      Optional[Any]   					= 0
		while count < prec:
				lowerCAmelCase__       :      Optional[Any]   					= random.randint(2     ,       n - 1	)
				lowerCAmelCase__       :      List[Any]   					= bin_exp_mod(_a     ,       _a     ,       _a	)
				if b != 1:
						lowerCAmelCase__       :      Dict   					= True
						for _ in range(_a	):
								if b == n - 1:
										lowerCAmelCase__       :      Union[str, Any]   					= False
										break
								lowerCAmelCase__       :      Tuple   					= b * b
								b %= n
						if flag:
								return False
						count += 1
		return True


if __name__ == "__main__":
		lowerCamelCase						=		abs(int(input('''Enter bound : ''').strip()))
		print('''Here\'s the list of primes:''')
		print(''', '''.join(str(i) for i in range(n + 1) if is_prime_big(i)))




