

'''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  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[Any]      )	->							Tuple:
       self.assertEqual(len(__lowerCamelCase      )			,     len(__lowerCamelCase      )      )
       for a, b in zip(__lowerCamelCase			,     __lowerCamelCase      ):
            self.assertAlmostEqual(__lowerCamelCase			,     __lowerCamelCase			,     delta=__lowerCamelCase      )






  def    lowercase_	(							self		:	Tuple      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					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		:	int      )	->							int:
       SCREAMING_SNAKE_CASE__					=					None
       ops.enable_eager_execution_internal()
       SCREAMING_SNAKE_CASE__					=					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()]      )
       SCREAMING_SNAKE_CASE__					=					tf.config.list_logical_devices(device_type='''CPU'''      )
       SCREAMING_SNAKE_CASE__					=					tf.distribute.MirroredStrategy(devices=devices[:2]      )

       with strategy.scope():
            SCREAMING_SNAKE_CASE__					=					GradientAccumulator()
            SCREAMING_SNAKE_CASE__					=					tf.Variable([4.0, 3.0]      )
            SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					create_optimizer(5e-5			,     10			,     5      )
            SCREAMING_SNAKE_CASE__					=					tf.Variable([0.0, 0.0]			,     trainable=__lowerCamelCase      )

       def accumulate_on_replica(__lowerCamelCase		:	Any      ):
            accumulator([gradient]      )

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

       @tf.function
       def accumulate(__lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	List[Any]      ):
            with strategy.scope():
                 SCREAMING_SNAKE_CASE__					=					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		:	Tuple			,     __lowerCamelCase		:	Dict      ):
            SCREAMING_SNAKE_CASE__					=					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]      )





def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for data in source_data:
          for i, el in enumerate(_A       ):
               if len(_A       ) < i + 1:
                    data_lists.append([]       )
               data_lists[i].append(float(_A       )       )
     return data_lists
def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[]
     for dlist, weight in zip(_A   ,	_A       ):
          SCREAMING_SNAKE_CASE__					=					min(_A       )
          SCREAMING_SNAKE_CASE__					=					max(_A       )

          SCREAMING_SNAKE_CASE__					=					[]
          # for weight 0 score is 1 - actual score
          if weight == 0:
               for item in dlist:
                    try:
                         score.append(1 - ((item - mind) / (maxd - mind))       )
                    except ZeroDivisionError:
                         score.append(1       )

          elif weight == 1:
               for item in dlist:
                    try:
                         score.append((item - mind) / (maxd - mind)       )
                    except ZeroDivisionError:
                         score.append(0       )

        # weight not 0 or 1
          else:
               SCREAMING_SNAKE_CASE__					=					F'''Invalid weight of {weight:f} provided'''
               raise ValueError(_A       )

          score_lists.append(_A       )

     return score_lists
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					[0 for i in range(len(score_lists[0]       )       )]

     for slist in score_lists:
          for j, ele in enumerate(_A       ):
               SCREAMING_SNAKE_CASE__					=					final_scores[j] + ele

     return final_scores







def        UpperCAmelCase_     (		_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					get_data(_A       )
     SCREAMING_SNAKE_CASE__					=					calculate_each_score(_A   ,	_A       )
     SCREAMING_SNAKE_CASE__					=					generate_final_scores(_A       )

     # append scores to source data
     for i, ele in enumerate(_A       ):
          source_data[i].append(_A       )

     return source_data


