



import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class 						_snake_case (     lowerCamelCase			):





	"""simple docstring"""



	lowerCamelCase_       =     (EulerAncestralDiscreteScheduler,)
	lowerCamelCase_       =     1_0
	def lowercase_						(  self		,      **a   )		->							Tuple:




			"""simple docstring"""







			_A          =      {
			    '''num_train_timesteps''': 1_1_0_0,
			    '''beta_start''': 0.0_001,
			    '''beta_end''': 0.02,
			    '''beta_schedule''': '''linear''',
			}

			config.update(**a   )
			return config
	def lowercase_						(  self   )		->							Dict:




			"""simple docstring"""







			for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
					self.check_over_configs(num_train_timesteps=a   )
	def lowercase_						(  self   )		->							List[str]:




			"""simple docstring"""







			for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]		,      [0.0_002, 0.002, 0.02]   ):
					self.check_over_configs(beta_start=a		,      beta_end=a   )
	def lowercase_						(  self   )		->							Optional[Any]:




			"""simple docstring"""







			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=a   )
	def lowercase_						(  self   )		->							List[Any]:




			"""simple docstring"""







			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=a   )
	def lowercase_						(  self   )		->							Union[str, Any]:




			"""simple docstring"""







			_A          =      self.scheduler_classes[0]
			_A          =      self.get_scheduler_config()
			_A          =      scheduler_class(**a   )

			scheduler.set_timesteps(self.num_inference_steps   )

			_A          =      torch.manual_seed(0   )

			_A          =      self.dummy_model()
			_A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			_A          =      sample.to(a   )

			for i, t in enumerate(scheduler.timesteps   ):
					_A          =      scheduler.scale_model_input(a		,      a   )

					_A          =      model(a		,      a   )

					_A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
					_A          =      output.prev_sample

			_A          =      torch.sum(torch.abs(a   )   )
			_A          =      torch.mean(torch.abs(a   )   )

			assert abs(result_sum.item() - 152.3_192   ) < 1e-2
			assert abs(result_mean.item() - 0.1_983   ) < 1e-3
	def lowercase_						(  self   )		->							int:




			"""simple docstring"""







			_A          =      self.scheduler_classes[0]
			_A          =      self.get_scheduler_config(prediction_type='''v_prediction'''   )
			_A          =      scheduler_class(**a   )

			scheduler.set_timesteps(self.num_inference_steps   )

			_A          =      torch.manual_seed(0   )

			_A          =      self.dummy_model()
			_A          =      self.dummy_sample_deter * scheduler.init_noise_sigma
			_A          =      sample.to(a   )

			for i, t in enumerate(scheduler.timesteps   ):
					_A          =      scheduler.scale_model_input(a		,      a   )

					_A          =      model(a		,      a   )

					_A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
					_A          =      output.prev_sample

			_A          =      torch.sum(torch.abs(a   )   )
			_A          =      torch.mean(torch.abs(a   )   )

			assert abs(result_sum.item() - 108.4_439   ) < 1e-2
			assert abs(result_mean.item() - 0.1_412   ) < 1e-3



	def lowercase_						(  self   )		->							Optional[Any]:




			"""simple docstring"""







			_A          =      self.scheduler_classes[0]
			_A          =      self.get_scheduler_config()
			_A          =      scheduler_class(**a   )

			scheduler.set_timesteps(self.num_inference_steps		,      device=a   )
			_A          =      torch.manual_seed(0   )

			_A          =      self.dummy_model()
			_A          =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			_A          =      sample.to(a   )

			for t in scheduler.timesteps:
					_A          =      scheduler.scale_model_input(a		,      a   )

					_A          =      model(a		,      a   )

					_A          =      scheduler.step(a		,      a		,      a		,      generator=a   )
					_A          =      output.prev_sample

			_A          =      torch.sum(torch.abs(a   )   )
			_A          =      torch.mean(torch.abs(a   )   )

			assert abs(result_sum.item() - 152.3_192   ) < 1e-2
			assert abs(result_mean.item() - 0.1_983   ) < 1e-3




import warnings

from .generation import FlaxGenerationMixin



class 						_snake_case (     lowerCamelCase			):





 """simple docstring"""



 warnings.warn(
     '''Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '''
     '''be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'''   ,lowerCamelCase   ,)