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 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							len(__SCREAMING_SNAKE_CASE       )       )
   for a, b in zip(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    self.assertAlmostEqual(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							delta=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __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(__SCREAMING_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 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  None
   ops.enable_eager_execution_internal()
   __a       =  tf.config.list_physical_devices("CPU"       )
   if len(__SCREAMING_SNAKE_CASE       ) == 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     ,							10     ,							5       )
    __a       =  tf.Variable([0.0, 0.0]     ,							trainable=__SCREAMING_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 :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[int]       ):
    with strategy.scope():
     __a       =  strategy.experimental_local_results(__SCREAMING_SNAKE_CASE       )
     local_variables[0].assign(__SCREAMING_SNAKE_CASE       )
     local_variables[1].assign(__SCREAMING_SNAKE_CASE       )
     strategy.run(__SCREAMING_SNAKE_CASE     ,							args=(gradient_placeholder,)       )

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

   def _check_local_values(__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]       ):
    __a       =  strategy.experimental_local_results(accumulator._gradients[0]       )
    self.assertListAlmostEqual(values[0].value()     ,							__SCREAMING_SNAKE_CASE     ,							tol=1E-2       )
    self.assertListAlmostEqual(values[1].value()     ,							__SCREAMING_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]       )



# Function to print upper half of diamond (pyramid)



def   __A		(			_A					):



 """simple docstring"""


 for i in range(0							,				_A					):
  for _ in range(0							,				n - i - 1					):  # printing spaces
   print(" "							,				end=""					)
  for _ in range(0							,				i + 1					):  # printing stars
   print("* "							,				end=""					)
  print()



def   __A		(			_A					):



 """simple docstring"""


 for i in range(_A							,				0							,				-1					):
  for _ in range(_A							,				0							,				-1					):  # printing stars
   print("* "							,				end=""					)
  print()
  for _ in range(n - i + 1							,				0							,				-1					):  # printing spaces
   print(" "							,				end=""					)



def   __A		(			_A					):



 """simple docstring"""


 if n <= 0:
  print("       ...       ....        nothing printing :("					)
  return
 floyd(_A					)  # upper half
 reverse_floyd(_A					)  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE :	Tuple							     =		1
  while K:
    SCREAMING_SNAKE_CASE :	Tuple							     =		int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE :	Optional[int]							     =		int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")


