


'''simple docstring'''



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 ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	List[Any]							,				snake_case  :	str							,				snake_case  :	Union[str, Any]    )		->							List[Any]:
          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  :	Optional[int]    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Union[str, 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  :	int    )		->							Union[str, Any]:
          __UpperCAmelCase			:				Dict													=    None
          ops.enable_eager_execution_internal()
          __UpperCAmelCase			:				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()]    )
          __UpperCAmelCase			:				Optional[Any]													=    tf.config.list_logical_devices(device_type='''CPU'''    )
          __UpperCAmelCase			:				Dict													=    tf.distribute.MirroredStrategy(devices=devices[:2]    )

          with strategy.scope():
               __UpperCAmelCase			:				List[str]													=    GradientAccumulator()
               __UpperCAmelCase			:				Optional[Any]													=    tf.Variable([4.0, 3.0]    )
               __UpperCAmelCase							,     __UpperCAmelCase			:				Tuple													=    create_optimizer(5E-5							,				10							,				5    )
               __UpperCAmelCase			:				int													=    tf.Variable([0.0, 0.0]							,				trainable=snake_case    )

          def accumulate_on_replica(snake_case  :	List[str]    ):
               accumulator([gradient]    )

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

          @tf.function
          def accumulate(snake_case  :	Tuple							,				snake_case  :	Optional[Any]    ):
               with strategy.scope():
                    __UpperCAmelCase			:				Optional[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(snake_case  :	List[str]							,				snake_case  :	Tuple    ):
               __UpperCAmelCase			:				List[str]													=    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]    )



'''simple docstring'''



def 			_a			(		_lowercase      :			list[list[int]]    ,      _lowercase      :			int    ,      _lowercase      :			int    ,      _lowercase      :			set						):





     '''simple docstring'''






     __UpperCAmelCase							,     __UpperCAmelCase			:				Any													=    len(_lowercase						), len(grid[0]						)
     if (
         min(_lowercase    ,      _lowercase						) < 0
         or row == row_length
         or col == col_length
         or (row, col) in visit
         or grid[row][col] == 1
     ):
          return 0
     if row == row_length - 1 and col == col_length - 1:
          return 1

     visit.add((row, col)						)

     __UpperCAmelCase			:				Optional[Any]													=    0
     count += depth_first_search(_lowercase    ,      row + 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      row - 1    ,      _lowercase    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col + 1    ,      _lowercase						)
     count += depth_first_search(_lowercase    ,      _lowercase    ,      col - 1    ,      _lowercase						)

     visit.remove((row, col)						)
     return count


if __name__ == "__main__":
      import doctest

      doctest.testmod()