



"""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       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	Optional[int]:
				self.assertEqual(len(_lowercase		)						,	len(_lowercase		)		)
				for a, b in zip(_lowercase						,	_lowercase		):
					self.assertAlmostEqual(_lowercase						,	_lowercase						,	delta=_lowercase		)


			def   snake_case__	(      self		)      ->	List[str]:
				A__											=					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		)      ->	str:
				A__											=					None
				ops.enable_eager_execution_internal()
				A__											=					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()]		)
				A__											=					tf.config.list_logical_devices(device_type="CPU"		)
				A__											=					tf.distribute.MirroredStrategy(devices=devices[:2]		)

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

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

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

				@tf.function
				def accumulate(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					with strategy.scope():
						A__											=					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(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		):
					A__											=					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'''



import functools


def A (__lowerCamelCase :list[int]					,      __lowerCamelCase :list[int]						):

						# Validation
						if not isinstance(__lowerCamelCase					,      __lowerCamelCase						) or not all(isinstance(__lowerCamelCase					,      __lowerCamelCase						) for day in days						):
												raise ValueError("""The parameter days should be a list of integers"""						)

						if len(__lowerCamelCase						) != 3 or not all(isinstance(__lowerCamelCase					,      __lowerCamelCase						) for cost in costs						):
												raise ValueError("""The parameter costs should be a list of three integers"""						)

						if len(__lowerCamelCase						) == 0:
												return 0

						if min(__lowerCamelCase						) <= 0:
												raise ValueError("""All days elements should be greater than 0"""						)

						if max(__lowerCamelCase						) >= 366:
												raise ValueError("""All days elements should be less than 366"""						)

						_lowerCAmelCase    		=					set(__lowerCamelCase						)

						@functools.cache
						def dynamic_programming(__lowerCamelCase :int						) -> int:
												if index > 365:
																		return 0

												if index not in days_set:
																		return dynamic_programming(index + 1						)

												return min(
												    costs[0] + dynamic_programming(index + 1						)					,      costs[1] + dynamic_programming(index + 7						)					,      costs[2] + dynamic_programming(index + 30						)					,      )

						return dynamic_programming(1						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
