
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  A_				(			self       ,							a       ,							a       ,							a				)					->      Tuple:


								'''simple docstring'''

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

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	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(a				):
										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  A_				(			self				)					->      Any:


								'''simple docstring'''

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

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

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

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

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

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

								def _check_local_values(a       ,							a				):
										_UpperCamelCase						    =	strategy.experimental_local_results(accumulator._gradients[0]				)
										self.assertListAlmostEqual(values[0].value()       ,							a       ,							tol=1e-2				)
										self.assertListAlmostEqual(values[1].value()       ,							a       ,							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]				)





def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				tuple[float, float]:

		"""simple docstring"""






		if not len(lowerCAmelCase ) == len(lowerCAmelCase ) == 3:
				raise ValueError("""Please enter a valid equation.""" )
		if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
				raise ValueError("""Both a & b of two equations can't be zero.""" )

		# Extract the coefficients
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa
		_UpperCamelCase    ,	_UpperCamelCase    ,	_UpperCamelCase						    =	equationa

		# Calculate the determinants of the matrices
		_UpperCamelCase						    =	aa * ba - aa * ba
		_UpperCamelCase						    =	ca * ba - ca * ba
		_UpperCamelCase						    =	aa * ca - aa * ca

		# Check if the system of linear equations has a solution (using Cramer's rule)
		if determinant == 0:
				if determinant_x == determinant_y == 0:
						raise ValueError("""Infinite solutions. (Consistent system)""" )
				else:
						raise ValueError("""No solution. (Inconsistent system)""" )
		else:
				if determinant_x == determinant_y == 0:
						# Trivial solution (Inconsistent system)
						return (0.0, 0.0)
				else:
						_UpperCamelCase						    =	determinant_x / determinant
						_UpperCamelCase						    =	determinant_y / determinant
						# Non-Trivial Solution (Consistent system)
						return (x, y)



