





"""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 					_lowerCAmelCase   (     unittest.TestCase       ):
       """simple docstring"""




       def      snake_case						(    self    , __UpperCAmelCase    , __UpperCAmelCase    , __UpperCAmelCase      ):



         '''simple docstring'''

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






       def      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:str											=							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(__UpperCAmelCase      ):
           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      snake_case						(    self      ):



         '''simple docstring'''

         lowerCAmelCase__			:List[Any]											=							None
         ops.enable_eager_execution_internal()
         lowerCAmelCase__			:List[str]											=							tf.config.list_physical_devices('CPU'      )
         if len(__UpperCAmelCase      ) == 1:
           tf.config.set_logical_device_configuration(
               physical_devices[0]    , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]      )
         lowerCAmelCase__			:Union[str, Any]											=							tf.config.list_logical_devices(device_type='CPU'      )
         lowerCAmelCase__			:int											=							tf.distribute.MirroredStrategy(devices=devices[:2]      )

         with strategy.scope():
           lowerCAmelCase__			:Any											=							GradientAccumulator()
           lowerCAmelCase__			:Optional[int]											=							tf.Variable([4.0, 3.0]      )
           lowerCAmelCase__			:Optional[int]											=							create_optimizer(5E-5    , 1_0    , 5      )
           lowerCAmelCase__			:Tuple											=							tf.Variable([0.0, 0.0]    , trainable=__UpperCAmelCase      )

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

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

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

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

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

import math

lowerCamelCase__          =		"""2020.9.26"""
lowerCamelCase__          =		"""xcodz-dot, cclaus, dhruvmanila"""
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float]:
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in locals().values()     ):
						lowerCAmelCase__      :      List[str]         =		F'''Input values must either be float or int: {list(locals().values()     )}'''
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      Tuple         =		((x * distance) / (z + distance)) * scale
			lowerCAmelCase__      :      Optional[int]         =		((y * distance) / (z + distance)) * scale
			return projected_x, projected_y
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     )				->		tuple[float, float, float]:
			if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_     ):
						raise TypeError('Axis must be a str'     )
			lowerCAmelCase__      :      Optional[int]         =		locals()
			del input_variables["axis"]
			if not all(isinstance(SCREAMING_SNAKE_CASE_       ,							(float, int)     ) for val in input_variables.values()     ):
						lowerCAmelCase__      :      List[Any]         =		(
						    'Input values except axis must either be float or int: '
						    F'''{list(input_variables.values()     )}'''
						)
						raise TypeError(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      int         =		(angle % 360) / 450 * 180 / math.pi
			if axis == "z":
						lowerCAmelCase__      :      Tuple         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z
			elif axis == "x":
						lowerCAmelCase__      :      Dict         =		y * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[str]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + y * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Tuple         =		x
			elif axis == "y":
						lowerCAmelCase__      :      str         =		x * math.cos(SCREAMING_SNAKE_CASE_     ) - z * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      Optional[int]         =		z * math.cos(SCREAMING_SNAKE_CASE_     ) + x * math.sin(SCREAMING_SNAKE_CASE_     )
						lowerCAmelCase__      :      List[Any]         =		y
			else:
						raise ValueError('not a valid axis, choose one of \'x\', \'y\', \'z\''     )

			return new_x, new_y, new_z


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					print(F"""{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }""")
					print(F"""{rotate(1.0, 2.0, 3.0, "y", 90.0) = }""")