


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 	__lowercase					(	unittest.TestCase	):





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       int       ,							__lowerCamelCase    :       Any							)     -> Optional[Any]:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       int							)     -> List[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(__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 							_lowercase					(		self    :       Union[str, Any]							)     -> Tuple:






         """simple docstring"""







         UpperCAmelCase      					=  None
         ops.enable_eager_execution_internal()
         UpperCAmelCase      					=  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()]							)
         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       ,							1_0       ,							5							)
            UpperCAmelCase      					=  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    :       List[str]							):
            with strategy.scope():
               UpperCAmelCase      					=  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    :       List[str]       ,							__lowerCamelCase    :       int							):
            UpperCAmelCase      					=  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]							)







from __future__ import annotations
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   # Checks if the entire collection has been sorted
   if len(lowerCAmelCase_ ) <= 1 or n <= 1:
      return

   insert_next(lowerCAmelCase_	,    n - 1 )
   rec_insertion_sort(lowerCAmelCase_	,    n - 1 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   # Checks order between adjacent elements
   if index >= len(lowerCAmelCase_ ) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   UpperCAmelCase					,	UpperCAmelCase      					=  (
       collection[index],
       collection[index - 1],
   )

   insert_next(lowerCAmelCase_	,    index + 1 )


if __name__ == "__main__":
 __a	 =      input("""Enter integers separated by spaces: """)
 __a	 =      [int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)



