





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						):







	'''simple docstring'''




	def    UpperCAmelCase			(						self							:     Optional[Any]					,      __lowerCamelCase							:     Union[str, Any]					,      __lowerCamelCase							:     Optional[int]					,      __lowerCamelCase							:     Optional[Any]   )       -> int:

			'''simple docstring'''




			self.assertEqual(len(__lowerCamelCase   )					,      len(__lowerCamelCase   )   )
			for a, b in zip(__lowerCamelCase					,      __lowerCamelCase   ):
					self.assertAlmostEqual(__lowerCamelCase					,      __lowerCamelCase					,      delta=__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     int   )       -> str:

			'''simple docstring'''




			__lowercase											=     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(__lowerCamelCase   ):
					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							:     str   )       -> int:

			'''simple docstring'''




			__lowercase											=     None
			ops.enable_eager_execution_internal()
			__lowercase											=     tf.config.list_physical_devices('CPU'   )
			if len(__lowerCamelCase   ) == 1:
					tf.config.set_logical_device_configuration(
					    physical_devices[0]					,      [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
			__lowercase											=     tf.config.list_logical_devices(device_type='CPU'   )
			__lowercase											=     tf.distribute.MirroredStrategy(devices=devices[:2]   )

			with strategy.scope():
					__lowercase											=     GradientAccumulator()
					__lowercase											=     tf.Variable([4.0, 3.0]   )
					__lowercase						,      __lowercase											=     create_optimizer(5E-5					,      10					,      5   )
					__lowercase											=     tf.Variable([0.0, 0.0]					,      trainable=__lowerCamelCase   )

			def accumulate_on_replica(__lowerCamelCase							:     str   ):
					accumulator([gradient]   )

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

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

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

			def _check_local_values(__lowerCamelCase							:     str					,      __lowerCamelCase							:     Union[str, Any]   ):
					__lowercase											=     strategy.experimental_local_results(accumulator._gradients[0]   )
					self.assertListAlmostEqual(values[0].value()					,      __lowerCamelCase					,      tol=1E-2   )
					self.assertListAlmostEqual(values[1].value()					,      __lowerCamelCase					,      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]   )











SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




