


import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    _a				(							_lowercase):
	_a    :    str        =				(EulerAncestralDiscreteScheduler,)
	_a    :    List[str]        =				10




	def 				UpperCAmelCase__(   self		:						Tuple						,   **_SCREAMING_SNAKE_CASE		:						Tuple						)->			Tuple:
			lowerCAmelCase__  :     Optional[int]       	=   {
			    '''num_train_timesteps''': 1100,
			    '''beta_start''': 0.0001,
			    '''beta_end''': 0.02,
			    '''beta_schedule''': '''linear''',
			}

			config.update(**_SCREAMING_SNAKE_CASE						)
			return config




	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			Any:
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE						)




	def 				UpperCAmelCase__(   self		:						Optional[int]						)->			int:
			for beta_start, beta_end in zip([0.0_0001, 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 				UpperCAmelCase__(   self		:						Dict						)->			str:
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE						)




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




	def 				UpperCAmelCase__(   self		:						Tuple						)->			str:
			lowerCAmelCase__  :     List[str]       	=   self.scheduler_classes[0]
			lowerCAmelCase__  :     str       	=   self.get_scheduler_config()
			lowerCAmelCase__  :     List[str]       	=   scheduler_class(**_SCREAMING_SNAKE_CASE						)

			scheduler.set_timesteps(self.num_inference_steps						)

			lowerCAmelCase__  :     str       	=   torch.manual_seed(0						)

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

			for i, t in enumerate(scheduler.timesteps						):
					lowerCAmelCase__  :     Union[str, Any]       	=   scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

					lowerCAmelCase__  :     int       	=   model(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

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

			lowerCAmelCase__  :     Union[str, Any]       	=   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
			lowerCAmelCase__  :     Union[str, Any]       	=   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 				UpperCAmelCase__(   self		:						Optional[int]						)->			Union[str, Any]:
			lowerCAmelCase__  :     List[Any]       	=   self.scheduler_classes[0]
			lowerCAmelCase__  :     int       	=   self.get_scheduler_config(prediction_type='''v_prediction'''						)
			lowerCAmelCase__  :     str       	=   scheduler_class(**_SCREAMING_SNAKE_CASE						)

			scheduler.set_timesteps(self.num_inference_steps						)

			lowerCAmelCase__  :     Dict       	=   torch.manual_seed(0						)

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

			for i, t in enumerate(scheduler.timesteps						):
					lowerCAmelCase__  :     Union[str, Any]       	=   scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

					lowerCAmelCase__  :     int       	=   model(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

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

			lowerCAmelCase__  :     List[Any]       	=   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
			lowerCAmelCase__  :     int       	=   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 				UpperCAmelCase__(   self		:						List[str]						)->			Any:
			lowerCAmelCase__  :     Dict       	=   self.scheduler_classes[0]
			lowerCAmelCase__  :     Optional[Any]       	=   self.get_scheduler_config()
			lowerCAmelCase__  :     int       	=   scheduler_class(**_SCREAMING_SNAKE_CASE						)

			scheduler.set_timesteps(self.num_inference_steps						,   device=_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     int       	=   torch.manual_seed(0						)

			lowerCAmelCase__  :     Tuple       	=   self.dummy_model()
			lowerCAmelCase__  :     Tuple       	=   self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowerCAmelCase__  :     Optional[int]       	=   sample.to(_SCREAMING_SNAKE_CASE						)

			for t in scheduler.timesteps:
					lowerCAmelCase__  :     Tuple       	=   scheduler.scale_model_input(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

					lowerCAmelCase__  :     List[str]       	=   model(_SCREAMING_SNAKE_CASE						,   _SCREAMING_SNAKE_CASE						)

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

			lowerCAmelCase__  :     Dict       	=   torch.sum(torch.abs(_SCREAMING_SNAKE_CASE						)						)
			lowerCAmelCase__  :     Union[str, Any]       	=   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








import warnings

from ...utils import logging
from .image_processing_maskformer import MaskFormerImageProcessor


lowerCamelCase		=						logging.get_logger(__name__)





class    _a				(							_lowercase):




	def __init__(   self		:						Any						,   *_SCREAMING_SNAKE_CASE		:						int						,   **_SCREAMING_SNAKE_CASE		:						Dict						)->			None:
			warnings.warn(
			    '''The class MaskFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
			    ''' Please use MaskFormerImageProcessor instead.'''						,   _SCREAMING_SNAKE_CASE						,   )
			super().__init__(*_SCREAMING_SNAKE_CASE						,   **_SCREAMING_SNAKE_CASE						)




