



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				):
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)	->						int:
          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    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Any		      =      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    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    List[Any]		      =      None
          ops.enable_eager_execution_internal()
          UpperCamelCase       :    Any		      =      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()]			)
          UpperCamelCase       :    Union[str, Any]		      =      tf.config.list_logical_devices(device_type='CPU'			)
          UpperCamelCase       :    int		      =      tf.distribute.MirroredStrategy(devices=devices[:2]			)

          with strategy.scope():
             UpperCamelCase       :    Optional[int]		      =      GradientAccumulator()
             UpperCamelCase       :    Tuple		      =      tf.Variable([4.0, 3.0]			)
             UpperCamelCase      ,	UpperCamelCase       :    int		      =      create_optimizer(5e-5,     10,     5			)
             UpperCamelCase       :    List[Any]		      =      tf.Variable([0.0, 0.0],     trainable=SCREAMING_SNAKE_CASE_			)

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

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

          @tf.function
          def accumulate(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             with strategy.scope():
                UpperCamelCase       :    Tuple		      =      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_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Optional[Any]		      =      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]			)







import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			(IPNDMScheduler,)
				lowerCAmelCase					=			(('num_inference_steps', 5_0),)

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[str]:
					A             =					{"""num_train_timesteps""": 1000}
					config.update(**a__ )
					return config

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					Tuple:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config(**a__ )
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )
						# copy over dummy past residuals
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							new_scheduler.set_timesteps(a__ )
							# copy over dummy past residuals
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

				def   _UpperCAmelCase       (    self )						->					Any:
					pass

				def   _UpperCAmelCase       (    self					, a__=0					, **a__ )						->					int:
					A             =					dict(self.forward_default_kwargs )
					A             =					kwargs.pop("""num_inference_steps"""					, a__ )
					A             =					self.dummy_sample
					A             =					0.1 * sample
					A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )
						scheduler.set_timesteps(a__ )

						# copy over dummy past residuals (must be after setting timesteps)
						A             =					dummy_past_residuals[:]

						if time_step is None:
							A             =					scheduler.timesteps[len(scheduler.timesteps ) // 2]

						with tempfile.TemporaryDirectory() as tmpdirname:
							scheduler.save_config(a__ )
							A             =					scheduler_class.from_pretrained(a__ )
							# copy over dummy past residuals
							new_scheduler.set_timesteps(a__ )

							# copy over dummy past residual (must be after setting timesteps)
							A             =					dummy_past_residuals[:]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					new_scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

						assert torch.sum(torch.abs(output - new_output ) ) < 1e-5, "Scheduler outputs are not identical"

				def   _UpperCAmelCase       (    self					, **a__ )						->					List[Any]:
					A             =					self.scheduler_classes[0]
					A             =					self.get_scheduler_config(**a__ )
					A             =					scheduler_class(**a__ )

					A             =					10
					A             =					self.dummy_model()
					A             =					self.dummy_sample_deter
					scheduler.set_timesteps(a__ )

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					for i, t in enumerate(scheduler.timesteps ):
						A             =					model(a__					, a__ )
						A             =					scheduler.step(a__					, a__					, a__ ).prev_sample

					return sample

				def   _UpperCAmelCase       (    self )						->					Any:
					A             =					dict(self.forward_default_kwargs )

					A             =					kwargs.pop("""num_inference_steps"""					, a__ )

					for scheduler_class in self.scheduler_classes:
						A             =					self.get_scheduler_config()
						A             =					scheduler_class(**a__ )

						A             =					self.dummy_sample
						A             =					0.1 * sample

						if num_inference_steps is not None and hasattr(a__					, """set_timesteps""" ):
							scheduler.set_timesteps(a__ )
						elif num_inference_steps is not None and not hasattr(a__					, """set_timesteps""" ):
							A             =					num_inference_steps

						# copy over dummy past residuals (must be done after set_timesteps)
						A             =					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
						A             =					dummy_past_residuals[:]

						A             =					scheduler.timesteps[5]
						A             =					scheduler.timesteps[6]

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample
						A             =					scheduler.step(a__					, a__					, a__					, **a__ ).prev_sample

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

				def   _UpperCAmelCase       (    self )						->					Tuple:
					for timesteps in [100, 1000]:
						self.check_over_configs(num_train_timesteps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					Any:
					for t, num_inference_steps in zip([1, 5, 10]					, [10, 50, 100] ):
						self.check_over_forward(num_inference_steps=a__					, time_step=a__ )

				def   _UpperCAmelCase       (    self )						->					int:
					A             =					self.full_loop()
					A             =					torch.mean(torch.abs(a__ ) )

					assert abs(result_mean.item() - 254_0529 ) < 10



