



'''simple docstring'''

import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest



class 		lowerCamelCase__   (    A  ):




					'''simple docstring'''



					A_         =    (DDIMScheduler,)
					A_         =    (("""eta""", 0.0), ("""num_inference_steps""", 50))




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



						'''simple docstring'''





						_lowercase       :							str  	=		{
						    'num_train_timesteps': 1000,
						    'beta_start': 0.00_01,
						    'beta_end': 0.02,
						    'beta_schedule': 'linear',
						    'clip_sample': True,
						}

						config.update(**UpperCamelCase_       )
						return config




					def     __UpperCAmelCase  (	self					:						Optional[Any]  ,	**UpperCamelCase_					:						Any       )	->							str:



						'''simple docstring'''





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

						_lowercase	,     _lowercase       :							List[str]  	=		10, 0.0

						_lowercase       :							Optional[int]  	=		self.dummy_model()
						_lowercase       :							List[Any]  	=		self.dummy_sample_deter

						scheduler.set_timesteps(UpperCamelCase_       )

						for t in scheduler.timesteps:
							_lowercase       :							Optional[int]  	=		model(UpperCamelCase_  ,	UpperCamelCase_       )
							_lowercase       :							str  	=		scheduler.step(UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_  ,	UpperCamelCase_       ).prev_sample

						return sample




					def     __UpperCAmelCase  (	self					:						int       )	->							Any:



						'''simple docstring'''





						for timesteps in [100, 500, 1000]:
							self.check_over_configs(num_train_timesteps=UpperCamelCase_       )




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



						'''simple docstring'''





						for steps_offset in [0, 1]:
							self.check_over_configs(steps_offset=UpperCamelCase_       )

						_lowercase       :							Union[str, Any]  	=		self.scheduler_classes[0]
						_lowercase       :							Union[str, Any]  	=		self.get_scheduler_config(steps_offset=1       )
						_lowercase       :							Optional[Any]  	=		scheduler_class(**UpperCamelCase_       )
						scheduler.set_timesteps(5       )
						assert torch.equal(scheduler.timesteps  ,	torch.LongTensor([801, 601, 401, 201, 1]       )       )




					def     __UpperCAmelCase  (	self					:						Tuple       )	->							List[str]:



						'''simple docstring'''





						for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]  ,	[0.0_02, 0.02, 0.2, 2]       ):
							self.check_over_configs(beta_start=UpperCamelCase_  ,	beta_end=UpperCamelCase_       )




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



						'''simple docstring'''





						for schedule in ["linear", "squaredcos_cap_v2"]:
							self.check_over_configs(beta_schedule=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Dict       )	->							Tuple:



						'''simple docstring'''





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




					def     __UpperCAmelCase  (	self					:						Dict       )	->							str:



						'''simple docstring'''





						for clip_sample in [True, False]:
							self.check_over_configs(clip_sample=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							Optional[int]:



						'''simple docstring'''





						for timestep_spacing in ["trailing", "leading"]:
							self.check_over_configs(timestep_spacing=UpperCamelCase_       )




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



						'''simple docstring'''





						for rescale_betas_zero_snr in [True, False]:
							self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						str       )	->							int:



						'''simple docstring'''





						self.check_over_configs(thresholding=UpperCamelCase_       )
						for threshold in [0.5, 1.0, 2.0]:
							for prediction_type in ["epsilon", "v_prediction"]:
								self.check_over_configs(
								    thresholding=UpperCamelCase_  ,	prediction_type=UpperCamelCase_  ,	sample_max_value=UpperCamelCase_  ,	)




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



						'''simple docstring'''





						for t in [1, 10, 49]:
							self.check_over_forward(time_step=UpperCamelCase_       )




					def     __UpperCAmelCase  (	self					:						List[str]       )	->							Union[str, Any]:



						'''simple docstring'''





						for t, num_inference_steps in zip([1, 10, 50]  ,	[10, 50, 500]       ):
							self.check_over_forward(time_step=UpperCamelCase_  ,	num_inference_steps=UpperCamelCase_       )




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



						'''simple docstring'''





						for t, eta in zip([1, 10, 49]  ,	[0.0, 0.5, 1.0]       ):
							self.check_over_forward(time_step=UpperCamelCase_  ,	eta=UpperCamelCase_       )




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



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.scheduler_classes[0]
						_lowercase       :							Tuple  	=		self.get_scheduler_config()
						_lowercase       :							Tuple  	=		scheduler_class(**UpperCamelCase_       )

						assert torch.sum(torch.abs(scheduler._get_variance(0  ,	0       ) - 0.0       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(420  ,	400       ) - 0.1_47_71       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(980  ,	960       ) - 0.3_24_60       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(0  ,	0       ) - 0.0       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(487  ,	486       ) - 0.0_09_79       )       ) < 1E-5
						assert torch.sum(torch.abs(scheduler._get_variance(999  ,	998       ) - 0.02       )       ) < 1E-5




					def     __UpperCAmelCase  (	self					:						Dict       )	->							Union[str, Any]:



						'''simple docstring'''





						_lowercase       :							List[str]  	=		self.full_loop()

						_lowercase       :							Optional[Any]  	=		torch.sum(torch.abs(UpperCamelCase_       )       )
						_lowercase       :							str  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_sum.item() - 1_72.00_67       ) < 1E-2
						assert abs(result_mean.item() - 0.22_39_67       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Any       )	->							Any:



						'''simple docstring'''





						_lowercase       :							Dict  	=		self.full_loop(prediction_type='v_prediction'       )

						_lowercase       :							Optional[int]  	=		torch.sum(torch.abs(UpperCamelCase_       )       )
						_lowercase       :							Optional[Any]  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_sum.item() - 52.53_02       ) < 1E-2
						assert abs(result_mean.item() - 0.06_84       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Union[str, Any]       )	->							Any:



						'''simple docstring'''





						_lowercase       :							Tuple  	=		self.full_loop(set_alpha_to_one=UpperCamelCase_  ,	beta_start=0.01       )
						_lowercase       :							Union[str, Any]  	=		torch.sum(torch.abs(UpperCamelCase_       )       )
						_lowercase       :							Dict  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_sum.item() - 1_49.82_95       ) < 1E-2
						assert abs(result_mean.item() - 0.19_51       ) < 1E-3




					def     __UpperCAmelCase  (	self					:						Optional[Any]       )	->							Dict:



						'''simple docstring'''





						_lowercase       :							List[Any]  	=		self.full_loop(set_alpha_to_one=UpperCamelCase_  ,	beta_start=0.01       )
						_lowercase       :							Dict  	=		torch.sum(torch.abs(UpperCamelCase_       )       )
						_lowercase       :							Optional[Any]  	=		torch.mean(torch.abs(UpperCamelCase_       )       )

						assert abs(result_sum.item() - 1_49.07_84       ) < 1E-2
						assert abs(result_mean.item() - 0.19_41       ) < 1E-3





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:     List[Any]      ={'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_A					:     Optional[Any]      =[
		    '''UMT5EncoderModel''',
		    '''UMT5ForConditionalGeneration''',
		    '''UMT5ForQuestionAnswering''',
		    '''UMT5Model''',
		    '''UMT5PreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_umta import UMTaConfig, UMTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_umta import (
				    UMTaEncoderModel,
				    UMTaForConditionalGeneration,
				    UMTaForQuestionAnswering,
				    UMTaModel,
				    UMTaPreTrainedModel,
				)
else:
		import sys

		_A					:     Optional[int]      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
