


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class      UpperCamelCase(	_a ):
					snake_case_		:					List[str]    					=						(EulerDiscreteScheduler,)
					snake_case_		:					Optional[int]    					=						10


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]  ,     **SCREAMING_SNAKE_CASE						:		Any    )      ->						Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		{
											    "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 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						Optional[Any]:




											'''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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, 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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]    )      ->						int:




											'''simple docstring'''

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


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




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE    )

											scheduler.set_timesteps(self.num_inference_steps    )

											__snake_case				=		torch.manual_seed(0    )

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter * scheduler.init_noise_sigma
											__snake_case				=		sample.to(SCREAMING_SNAKE_CASE    )

											for i, t in enumerate(scheduler.timesteps    ):
																	__snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     generator=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		output.prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE    )    )

											assert abs(result_sum.item() - 10.0807    ) < 1e-2
											assert abs(result_mean.item() - 0.0131    ) < 1e-3


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config(prediction_type="v_prediction"    )
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE    )

											scheduler.set_timesteps(self.num_inference_steps    )

											__snake_case				=		torch.manual_seed(0    )

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter * scheduler.init_noise_sigma
											__snake_case				=		sample.to(SCREAMING_SNAKE_CASE    )

											for i, t in enumerate(scheduler.timesteps    ):
																	__snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     generator=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		output.prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE    )    )

											assert abs(result_sum.item() - 0.0002    ) < 1e-2
											assert abs(result_mean.item() - 2.2_6_7_6e-0_6    ) < 1e-3


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




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		torch.manual_seed(0    )

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											__snake_case				=		sample.to(SCREAMING_SNAKE_CASE    )

											for t in scheduler.timesteps:
																	__snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     generator=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		output.prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE    )    )

											assert abs(result_sum.item() - 10.0807    ) < 1e-2
											assert abs(result_mean.item() - 0.0131    ) < 1e-3






					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.scheduler_classes[0]
											__snake_case				=		self.get_scheduler_config()
											__snake_case				=		scheduler_class(**SCREAMING_SNAKE_CASE  ,     use_karras_sigmas=SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		torch.manual_seed(0    )

											__snake_case				=		self.dummy_model()
											__snake_case				=		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
											__snake_case				=		sample.to(SCREAMING_SNAKE_CASE    )

											for t in scheduler.timesteps:
																	__snake_case				=		scheduler.scale_model_input(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		model(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

																	__snake_case				=		scheduler.step(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     generator=SCREAMING_SNAKE_CASE    )
																	__snake_case				=		output.prev_sample

											__snake_case				=		torch.sum(torch.abs(SCREAMING_SNAKE_CASE    )    )
											__snake_case				=		torch.mean(torch.abs(SCREAMING_SNAKE_CASE    )    )

											assert abs(result_sum.item() - 124.52299499511719    ) < 1e-2
											assert abs(result_mean.item() - 0.16213932633399963    ) < 1e-3









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





