

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			):



       '''simple docstring'''







       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ ,   lowercase__     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    =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(lowercase__     ):
                   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     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

             with strategy.scope():
                   __A    =GradientAccumulator()
                   __A    =tf.Variable([4.0, 3.0]     )
                   __A    ,		__A    =create_optimizer(5E-5 ,   1_0 ,   5     )
                   __A    =tf.Variable([0.0, 0.0] ,   trainable=lowercase__     )

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

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

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

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

             def _check_local_values(lowercase__ ,   lowercase__     ):
                   __A    =strategy.experimental_local_results(accumulator._gradients[0]     )
                   self.assertListAlmostEqual(values[0].value() ,   lowercase__ ,   tol=1E-2     )
                   self.assertListAlmostEqual(values[1].value() ,   lowercase__ ,   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 collections import deque





def      A__					(			__A  :						Optional[Any]			)					->Tuple:
      __A    =len(__A			)
      __A    =deque()
      __A    =[False for _ in range(__A			)]
      __A    =[-1 for _ in range(__A			)]
      __A    =index_of[:]

      def strong_connect(__A  :						Union[str, Any]     ,				__A  :						int     ,				__A  :						Optional[int]			):
            __A    =index  # the number when this node is seen
            __A    =index  # lowest rank node reachable from here
            index += 1
            stack.append(__A			)
            __A    =True

            for w in g[v]:
                  if index_of[w] == -1:
                        __A    =strong_connect(__A     ,				__A     ,				__A			)
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )
                  elif on_stack[w]:
                        __A    =(
                            lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                        )

            if lowlink_of[v] == index_of[v]:
                  __A    =[]
                  __A    =stack.pop()
                  __A    =False
                  component.append(__A			)
                  while w != v:
                        __A    =stack.pop()
                        __A    =False
                        component.append(__A			)
                  components.append(__A			)
            return index

      __A    =[]
      for v in range(__A			):
            if index_of[v] == -1:
                  strong_connect(__A     ,				0     ,				__A			)

      return components





def      A__					(			__A  :						List[Any]     ,				__A  :						Optional[int]			)					->Tuple:
      __A    =[[] for _ in range(__A			)]
      for u, v in edges:
            g[u].append(__A			)
      return g


if __name__ == "__main__":
   # Test
   _lowerCamelCase      :					int					    =      7
   _lowerCamelCase      :					Tuple					    =      [0, 0, 1, 2, 3, 3, 4, 4, 6]
   _lowerCamelCase      :					Dict					    =      [1, 3, 2, 0, 1, 4, 5, 6, 5]
   _lowerCamelCase      :					Optional[Any]					    =      [(u, v) for u, v in zip(source, target)]
   _lowerCamelCase      :					Dict					    =      create_graph(n_vertices, edges)

   assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)


