



"""simple docstring"""






import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 			__snake_case			(      _a):
  snake_case__							:  Dict         =		(EulerDiscreteScheduler,)
  snake_case__							:  List[Any]         =		1_0




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]		,   **__lowerCAmelCase	:		Any						):






         """simple docstring"""






         _lowerCamelCase		:							Optional[int]            =					{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**__lowerCAmelCase						)
         return config




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Tuple						):






         """simple docstring"""






         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]		,   [0.00_02, 0.0_02, 0.02]						):
                self.check_over_configs(beta_start=__lowerCAmelCase		,   beta_end=__lowerCAmelCase						)




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[int]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Union[str, Any]						):






         """simple docstring"""






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




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							List[str]            =					self.get_scheduler_config()
         _lowerCamelCase		:							Tuple            =					scheduler_class(**__lowerCAmelCase						)

         scheduler.set_timesteps(self.num_inference_steps						)

         _lowerCamelCase		:							Dict            =					torch.manual_seed(0						)

         _lowerCamelCase		:							Tuple            =					self.dummy_model()
         _lowerCamelCase		:							str            =					self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCamelCase		:							Tuple            =					sample.to(__lowerCAmelCase						)

         for i, t in enumerate(scheduler.timesteps						):
                _lowerCamelCase		:							int            =					scheduler.scale_model_input(__lowerCAmelCase		,   __lowerCAmelCase						)

                _lowerCamelCase		:							str            =					model(__lowerCAmelCase		,   __lowerCAmelCase						)

                _lowerCamelCase		:							Tuple            =					scheduler.step(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   generator=__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					output.prev_sample

         _lowerCamelCase		:							Any            =					torch.sum(torch.abs(__lowerCAmelCase						)						)
         _lowerCamelCase		:							Any            =					torch.mean(torch.abs(__lowerCAmelCase						)						)

         assert abs(result_sum.item() - 10.08_07						) < 1E-2
         assert abs(result_mean.item() - 0.01_31						) < 1E-3




  def 		SCREAMING_SNAKE_CASE							(					self	:		Optional[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							List[Any]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							Union[str, Any]            =					self.get_scheduler_config(prediction_type='''v_prediction'''						)
         _lowerCamelCase		:							Tuple            =					scheduler_class(**__lowerCAmelCase						)

         scheduler.set_timesteps(self.num_inference_steps						)

         _lowerCamelCase		:							str            =					torch.manual_seed(0						)

         _lowerCamelCase		:							Dict            =					self.dummy_model()
         _lowerCamelCase		:							List[Any]            =					self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowerCamelCase		:							Any            =					sample.to(__lowerCAmelCase						)

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

                _lowerCamelCase		:							Optional[Any]            =					model(__lowerCAmelCase		,   __lowerCAmelCase						)

                _lowerCamelCase		:							Union[str, Any]            =					scheduler.step(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   generator=__lowerCAmelCase						)
                _lowerCamelCase		:							Optional[Any]            =					output.prev_sample

         _lowerCamelCase		:							List[Any]            =					torch.sum(torch.abs(__lowerCAmelCase						)						)
         _lowerCamelCase		:							Dict            =					torch.mean(torch.abs(__lowerCAmelCase						)						)

         assert abs(result_sum.item() - 0.00_02						) < 1E-2
         assert abs(result_mean.item() - 2.2676E-06						) < 1E-3




  def 		SCREAMING_SNAKE_CASE							(					self	:		List[Any]						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							List[Any]            =					self.get_scheduler_config()
         _lowerCamelCase		:							Union[str, Any]            =					scheduler_class(**__lowerCAmelCase						)

         scheduler.set_timesteps(self.num_inference_steps		,   device=__lowerCAmelCase						)

         _lowerCamelCase		:							Optional[int]            =					torch.manual_seed(0						)

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

         for t in scheduler.timesteps:
                _lowerCamelCase		:							int            =					scheduler.scale_model_input(__lowerCAmelCase		,   __lowerCAmelCase						)

                _lowerCamelCase		:							str            =					model(__lowerCAmelCase		,   __lowerCAmelCase						)

                _lowerCamelCase		:							Dict            =					scheduler.step(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   generator=__lowerCAmelCase						)
                _lowerCamelCase		:							Tuple            =					output.prev_sample

         _lowerCamelCase		:							Dict            =					torch.sum(torch.abs(__lowerCAmelCase						)						)
         _lowerCamelCase		:							int            =					torch.mean(torch.abs(__lowerCAmelCase						)						)

         assert abs(result_sum.item() - 10.08_07						) < 1E-2
         assert abs(result_mean.item() - 0.01_31						) < 1E-3




  def 		SCREAMING_SNAKE_CASE							(					self	:		str						):






         """simple docstring"""






         _lowerCamelCase		:							Union[str, Any]            =					self.scheduler_classes[0]
         _lowerCamelCase		:							Tuple            =					self.get_scheduler_config()
         _lowerCamelCase		:							int            =					scheduler_class(**__lowerCAmelCase		,   use_karras_sigmas=__lowerCAmelCase						)

         scheduler.set_timesteps(self.num_inference_steps		,   device=__lowerCAmelCase						)

         _lowerCamelCase		:							Union[str, Any]            =					torch.manual_seed(0						)

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

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

                _lowerCamelCase		:							int            =					model(__lowerCAmelCase		,   __lowerCAmelCase						)

                _lowerCamelCase		:							Any            =					scheduler.step(__lowerCAmelCase		,   __lowerCAmelCase		,   __lowerCAmelCase		,   generator=__lowerCAmelCase						)
                _lowerCamelCase		:							Optional[int]            =					output.prev_sample

         _lowerCamelCase		:							Any            =					torch.sum(torch.abs(__lowerCAmelCase						)						)
         _lowerCamelCase		:							str            =					torch.mean(torch.abs(__lowerCAmelCase						)						)

         assert abs(result_sum.item() - 1_24.52_29_94_99_51_17_19						) < 1E-2
         assert abs(result_mean.item() - 0.1_62_13_93_26_33_39_99_63						) < 1E-3









import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__           =			logging.get_logger(__name__)

class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[Any]      =							"""encoder-decoder"""
							snake_case  :   Optional[int]      =							True






							def __init__(    self	,			**__lowerCAmelCase	):
														super().__init__(**__lowerCAmelCase	)
														assert (
														    "encoder" in kwargs and "decoder" in kwargs
														), "Config has to be initialized with encoder and decoder config"
														UpperCamelCase__					  =      kwargs.pop("""encoder"""	)
														UpperCamelCase__					  =      encoder_config.pop("""model_type"""	)
														UpperCamelCase__					  =      kwargs.pop("""decoder"""	)
														UpperCamelCase__					  =      decoder_config.pop("""model_type"""	)

														from ..auto.configuration_auto import AutoConfig

														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      AutoConfig.for_model(__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      True






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														logger.info("""Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""	)
														UpperCamelCase__					  =      True
														UpperCamelCase__					  =      True

														return cls(encoder=encoder_config.to_dict()	,			decoder=decoder_config.to_dict()	,			**__lowerCAmelCase	)







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      copy.deepcopy(self.__dict__	)
														UpperCamelCase__					  =      self.encoder.to_dict()
														UpperCamelCase__					  =      self.decoder.to_dict()
														UpperCamelCase__					  =      self.__class__.model_type
														return output




