

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			):







	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]  , __UpperCamelCase: Dict  , __UpperCamelCase: int  , __UpperCamelCase: List[str]      ):

						'''simple docstring'''





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







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    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(__UpperCamelCase      ):
											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 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





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

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

						def accumulate_on_replica(__UpperCamelCase: Optional[int]      ):
											accumulator([gradient]      )

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

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

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

						def _check_local_values(__UpperCamelCase: str  , __UpperCamelCase: Tuple      ):
											__magic_name__							=    strategy.experimental_local_results(accumulator._gradients[0]      )
											self.assertListAlmostEqual(values[0].value()  , __UpperCamelCase  , tol=1E-2      )
											self.assertListAlmostEqual(values[1].value()  , __UpperCamelCase  , 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 os

A__						=							{"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1000}




def 					_lowercase     (				a_				:					str				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					__magic_name__							=    0
					while index < len(a_				) - 1:
										__magic_name__							=    SYMBOLS[numerals[index]]
										__magic_name__							=    SYMBOLS[numerals[index + 1]]
										if current_value < next_value:
															total_value -= current_value
										else:
															total_value += current_value
										index += 1
					total_value += SYMBOLS[numerals[index]]

					return total_value




def 					_lowercase     (				a_				:					int				)				->	str:




					'''simple docstring'''







					__magic_name__							=    ''

					__magic_name__							=    num // 1_0_0_0
					numerals += m_count * "M"
					num %= 1_0_0_0

					__magic_name__							=    num // 1_0_0
					if c_count == 9:
										numerals += "CM"
										c_count -= 9
					elif c_count == 4:
										numerals += "CD"
										c_count -= 4
					if c_count >= 5:
										numerals += "D"
										c_count -= 5
					numerals += c_count * "C"
					num %= 1_0_0

					__magic_name__							=    num // 1_0
					if x_count == 9:
										numerals += "XC"
										x_count -= 9
					elif x_count == 4:
										numerals += "XL"
										x_count -= 4
					if x_count >= 5:
										numerals += "L"
										x_count -= 5
					numerals += x_count * "X"
					num %= 1_0

					if num == 9:
										numerals += "IX"
										num -= 9
					elif num == 4:
										numerals += "IV"
										num -= 4
					if num >= 5:
										numerals += "V"
										num -= 5
					numerals += num * "I"

					return numerals




def 					_lowercase     (				a_				:					str = "/p089_roman.txt"				)				->	int:




					'''simple docstring'''







					__magic_name__							=    0

					with open(os.path.dirname(a_				) + roman_numerals_filename				) as filea:
										__magic_name__							=    filea.readlines()

					for line in lines:
										__magic_name__							=    line.strip()
										__magic_name__							=    parse_roman_numerals(a_				)
										__magic_name__							=    generate_roman_numerals(a_				)
										savings += len(a_				) - len(a_				)

					return savings


if __name__ == "__main__":
						print(f'''{solution() = }''')




