
'''simple docstring'''


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class     __snake_case			(    _a	):
		a_          =					(EulerAncestralDiscreteScheduler,)
		a_          =					10




		def 	_snake_case	(  self					:Optional[int]			,    **_SCREAMING_SNAKE_CASE					:str)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     {
								    """num_train_timesteps""": 1_1_0_0,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								}

								config.update(**_SCREAMING_SNAKE_CASE)
								return config




		def 	_snake_case	(  self					:List[Any])					->			str:






								'''simple docstring'''






								for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
														self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:int)					->			List[Any]:






								'''simple docstring'''






								for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]			,    [0.0002, 0.002, 0.02]):
														self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE			,    beta_end=_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:int)					->			Any:






								'''simple docstring'''






								for schedule in ["linear", "scaled_linear"]:
														self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Dict)					->			List[Any]:






								'''simple docstring'''






								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int])					->			str:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.scheduler_classes[0]
								UpperCamelCase_     :	Dict					     =     self.get_scheduler_config()
								UpperCamelCase_     :	Optional[Any]					     =     scheduler_class(**_SCREAMING_SNAKE_CASE)

								scheduler.set_timesteps(self.num_inference_steps)

								UpperCamelCase_     :	int					     =     torch.manual_seed(0)

								UpperCamelCase_     :	Union[str, Any]					     =     self.dummy_model()
								UpperCamelCase_     :	Union[str, Any]					     =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								UpperCamelCase_     :	Optional[int]					     =     sample.to(_SCREAMING_SNAKE_CASE)

								for i, t in enumerate(scheduler.timesteps):
														UpperCamelCase_     :	Optional[Any]					     =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     :	Tuple					     =     model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     :	Optional[int]					     =     scheduler.step(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	str					     =     output.prev_sample

								UpperCamelCase_     :	Any					     =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
								UpperCamelCase_     :	int					     =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

								assert abs(result_sum.item() - 152.3192) < 1E-2
								assert abs(result_mean.item() - 0.1983) < 1E-3




		def 	_snake_case	(  self					:int)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Tuple					     =     self.scheduler_classes[0]
								UpperCamelCase_     :	Optional[int]					     =     self.get_scheduler_config(prediction_type="""v_prediction""")
								UpperCamelCase_     :	Dict					     =     scheduler_class(**_SCREAMING_SNAKE_CASE)

								scheduler.set_timesteps(self.num_inference_steps)

								UpperCamelCase_     :	Optional[Any]					     =     torch.manual_seed(0)

								UpperCamelCase_     :	Dict					     =     self.dummy_model()
								UpperCamelCase_     :	Dict					     =     self.dummy_sample_deter * scheduler.init_noise_sigma
								UpperCamelCase_     :	Optional[Any]					     =     sample.to(_SCREAMING_SNAKE_CASE)

								for i, t in enumerate(scheduler.timesteps):
														UpperCamelCase_     :	str					     =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     :	List[Any]					     =     model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     :	List[Any]					     =     scheduler.step(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	int					     =     output.prev_sample

								UpperCamelCase_     :	Any					     =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
								UpperCamelCase_     :	Dict					     =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

								assert abs(result_sum.item() - 108.4439) < 1E-2
								assert abs(result_mean.item() - 0.1412) < 1E-3





		def 	_snake_case	(  self					:List[str])					->			Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self.scheduler_classes[0]
								UpperCamelCase_     :	Optional[Any]					     =     self.get_scheduler_config()
								UpperCamelCase_     :	str					     =     scheduler_class(**_SCREAMING_SNAKE_CASE)

								scheduler.set_timesteps(self.num_inference_steps			,    device=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[Any]					     =     torch.manual_seed(0)

								UpperCamelCase_     :	Any					     =     self.dummy_model()
								UpperCamelCase_     :	List[str]					     =     self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
								UpperCamelCase_     :	str					     =     sample.to(_SCREAMING_SNAKE_CASE)

								for t in scheduler.timesteps:
														UpperCamelCase_     :	Optional[int]					     =     scheduler.scale_model_input(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     :	Optional[Any]					     =     model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)

														UpperCamelCase_     :	Tuple					     =     scheduler.step(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    generator=_SCREAMING_SNAKE_CASE)
														UpperCamelCase_     :	Optional[Any]					     =     output.prev_sample

								UpperCamelCase_     :	List[str]					     =     torch.sum(torch.abs(_SCREAMING_SNAKE_CASE))
								UpperCamelCase_     :	Tuple					     =     torch.mean(torch.abs(_SCREAMING_SNAKE_CASE))

								assert abs(result_sum.item() - 152.3192) < 1E-2
								assert abs(result_mean.item() - 0.1983) < 1E-3






'''simple docstring'''


import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
	import tensorflow as tf

	from transformers.activations_tf import get_tf_activation




@require_tf
class     __snake_case			(    unittest.TestCase	):




		def 	_snake_case	(  self					:Tuple)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[str]					     =     tf.constant([-1_0_0, -1.0, -0.1, 0, 0.1, 1.0, 100.0])
								UpperCamelCase_     :	Any					     =     get_tf_activation("""gelu""")
								UpperCamelCase_     :	List[Any]					     =     get_tf_activation("""gelu_10""")

								UpperCamelCase_     :	Optional[int]					     =     gelu(_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Dict					     =     geluaa(_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[Any]					     =     tf.where(y_gelu_aa < 10.0			,    1.0			,    0.0)

								self.assertEqual(tf.math.reduce_max(_SCREAMING_SNAKE_CASE).numpy().item()			,    10.0)
								self.assertTrue(np.allclose(y_gelu * clipped_mask			,    y_gelu_aa * clipped_mask))





		def 	_snake_case	(  self					:Any)					->			Dict:






								'''simple docstring'''






								get_tf_activation("""gelu""")
								get_tf_activation("""gelu_10""")
								get_tf_activation("""gelu_fast""")
								get_tf_activation("""gelu_new""")
								get_tf_activation("""glu""")
								get_tf_activation("""mish""")
								get_tf_activation("""quick_gelu""")
								get_tf_activation("""relu""")
								get_tf_activation("""sigmoid""")
								get_tf_activation("""silu""")
								get_tf_activation("""swish""")
								get_tf_activation("""tanh""")
								with self.assertRaises(_SCREAMING_SNAKE_CASE):
														get_tf_activation("""bogus""")
								with self.assertRaises(_SCREAMING_SNAKE_CASE):
														get_tf_activation(_SCREAMING_SNAKE_CASE)




