

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     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			List[Any]   ,SCREAMING_SNAKE_CASE__		:			Tuple):
								self.assertEqual(len(_snake_case)   ,len(_snake_case))
								for a, b in zip(_snake_case   ,_snake_case):
									self.assertAlmostEqual(_snake_case   ,_snake_case   ,delta=_snake_case)
							def     lowerCAmelCase     (		self		:			Union[str, Any]):
								__lowerCamelCase				:				List[Any]					       = 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(_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     lowerCAmelCase     (		self		:			Optional[int]):
								__lowerCamelCase				:				List[str]					       = None
								ops.enable_eager_execution_internal()
								__lowerCamelCase				:				Any					       = tf.config.list_physical_devices('CPU')
								if len(_snake_case) == 1:
									tf.config.set_logical_device_configuration(
									    physical_devices[0]   ,[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()])
								__lowerCamelCase				:				List[str]					       = tf.config.list_logical_devices(device_type='CPU')
								__lowerCamelCase				:				Any					       = tf.distribute.MirroredStrategy(devices=devices[:2])

								with strategy.scope():
									__lowerCamelCase				:				Dict					       = GradientAccumulator()
									__lowerCamelCase				:				Tuple					       = tf.Variable([4.0, 3.0])
									__lowerCamelCase       , __lowerCamelCase				:				Optional[Any]					       = create_optimizer(5E-5   ,1_0   ,5)
									__lowerCamelCase				:				str					       = tf.Variable([0.0, 0.0]   ,trainable=_snake_case)

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

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

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

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

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






from __future__ import annotations

import time
from collections.abc import Sequence
from random import randint

from matplotlib import pyplot as plt


def     __lowerCAmelCase			(			a__						,    a__						,    a__  )       ->  tuple[int | None, int | None, float]:
				if not arr:
								return None, None, 0
				if low == high:
								return low, high, arr[low]

				__a										=    (low + high) // 2
				__a							,     __a							,     __a										=    max_subarray(a__						,    a__						,    a__  )
				__a							,     __a							,     __a										=    max_subarray(a__						,    mid + 1						,    a__  )
				__a							,     __a							,     __a										=    max_cross_sum(a__						,    a__						,    a__						,    a__  )
				if left_sum >= right_sum and left_sum >= cross_sum:
								return left_low, left_high, left_sum
				elif right_sum >= left_sum and right_sum >= cross_sum:
								return right_low, right_high, right_sum
				return cross_left, cross_right, cross_sum


def     __lowerCAmelCase			(			a__						,    a__						,    a__						,    a__  )       ->  tuple[int, int, float]:
				__a							,     __a										=    float('''-inf'''  ), -1
				__a							,     __a										=    float('''-inf'''  ), -1

				__a										=    0
				for i in range(a__						,    low - 1						,    -1  ):
								summ += arr[i]
								if summ > left_sum:
												__a										=    summ
												__a										=    i

				__a										=    0
				for i in range(mid + 1						,    high + 1  ):
								summ += arr[i]
								if summ > right_sum:
												__a										=    summ
												__a										=    i

				return max_left, max_right, (left_sum + right_sum)


def     __lowerCAmelCase			(			a__  )       ->  float:
				__a										=    [randint(1						,    a__  ) for _ in range(a__  )]
				__a										=    time.time()
				max_subarray(a__						,    0						,    input_size - 1  )
				__a										=    time.time()
				return end - start


def     __lowerCAmelCase			(			)       ->  None:
				__a										=    [10, 100, 1000, 1_0000, 5_0000, 10_0000, 20_0000, 30_0000, 40_0000, 50_0000]
				__a										=    [time_max_subarray(a__  ) for input_size in input_sizes]
				print('''No of Inputs\t\tTime Taken'''  )
				for input_size, runtime in zip(a__						,    a__  ):
								print(a__						,    '''\t\t'''						,    a__  )
				plt.plot(a__						,    a__  )
				plt.xlabel('''Number of Inputs'''  )
				plt.ylabel('''Time taken in seconds'''  )
				plt.show()


if __name__ == "__main__":
					from doctest import testmod

					testmod()