


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]     ,		_a:List[Any]     ,		_a:Dict     ,		_a:List[Any]   ):
       self.assertEqual(len(_a   )     ,		len(_a   )   )
       for a, b in zip(_a     ,		_a   ):
         self.assertAlmostEqual(_a     ,		_a     ,		delta=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       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(_a   ):
         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 	SCREAMING_SNAKE_CASE__						(					self:Union[str, Any]   ):
       snake_case__						= None
       ops.enable_eager_execution_internal()
       snake_case__						= tf.config.list_physical_devices('''CPU'''   )
       if len(_a   ) == 1:
         tf.config.set_logical_device_configuration(
             physical_devices[0]     ,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
       snake_case__						= tf.config.list_logical_devices(device_type='''CPU'''   )
       snake_case__						= tf.distribute.MirroredStrategy(devices=devices[:2]   )

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

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

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

       @tf.function
       def accumulate(_a:Optional[int]     ,		_a:List[str]   ):
         with strategy.scope():
           snake_case__						= strategy.experimental_local_results(_a   )
           local_variables[0].assign(_a   )
           local_variables[1].assign(_a   )
           strategy.run(_a     ,		args=(gradient_placeholder,)   )

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

       def _check_local_values(_a:Any     ,		_a:Any   ):
         snake_case__						= strategy.experimental_local_results(accumulator._gradients[0]   )
         self.assertListAlmostEqual(values[0].value()     ,		_a     ,		tol=1e-2   )
         self.assertListAlmostEqual(values[1].value()     ,		_a     ,		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]   )





import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

       self.assertEqual(output_a.shape     ,		sample.shape   )
       self.assertEqual(output_a.shape     ,		output_a.shape   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 192.7614   ) < 1e-2
       assert abs(result_mean.item() - 0.2510   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 347.6357   ) < 1e-2
       assert abs(result_mean.item() - 0.4527   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

       with self.assertRaises(_a     ,		msg='''`timesteps` must be in descending order.'''   ):
         scheduler.set_timesteps(timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

       with self.assertRaises(_a     ,		msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''   ):
         scheduler.set_timesteps(num_inference_steps=_a     ,		timesteps=_a   )







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

       with self.assertRaises(
           _a     ,		msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''     ,		):
         scheduler.set_timesteps(timesteps=_a   )

