



'''simple docstring'''




import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class      _lowerCAmelCase							(      __UpperCAmelCase			):
	__SCREAMING_SNAKE_CASE       :		Any		      = (EulerDiscreteScheduler,)
	__SCREAMING_SNAKE_CASE       :		List[Any]		      = 10


	def    _a     (self	,				**lowercase							):
				A_     :						Union[str, Any]     =					{
				    """num_train_timesteps""": 1100,
				    """beta_start""": 0.00_01,
				    """beta_end""": 0.02,
				    """beta_schedule""": """linear""",
				}

				config.update(**lowercase							)
				return config


	def    _a     (self							):
				for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=lowercase							)


	def    _a     (self							):
				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=lowercase	,				beta_end=lowercase							)


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


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


	def    _a     (self							):
				A_     :						Tuple     =					self.scheduler_classes[0]
				A_     :						List[Any]     =					self.get_scheduler_config()
				A_     :						int     =					scheduler_class(**lowercase							)

				scheduler.set_timesteps(self.num_inference_steps							)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)

				A_     :						Optional[int]     =					self.dummy_model()
				A_     :						Optional[int]     =					self.dummy_sample_deter * scheduler.init_noise_sigma
				A_     :						List[Any]     =					sample.to(lowercase							)

				for i, t in enumerate(scheduler.timesteps							):
							A_     :						Tuple     =					scheduler.scale_model_input(lowercase	,				lowercase							)

							A_     :						Tuple     =					model(lowercase	,				lowercase							)

							A_     :						Any     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				generator=lowercase							)
							A_     :						List[Any]     =					output.prev_sample

				A_     :						Optional[Any]     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Any     =					torch.mean(torch.abs(lowercase							)							)

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


	def    _a     (self							):
				A_     :						Any     =					self.scheduler_classes[0]
				A_     :						Optional[int]     =					self.get_scheduler_config(prediction_type="""v_prediction"""							)
				A_     :						Optional[int]     =					scheduler_class(**lowercase							)

				scheduler.set_timesteps(self.num_inference_steps							)

				A_     :						Optional[Any]     =					torch.manual_seed(0							)

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

				for i, t in enumerate(scheduler.timesteps							):
							A_     :						List[Any]     =					scheduler.scale_model_input(lowercase	,				lowercase							)

							A_     :						str     =					model(lowercase	,				lowercase							)

							A_     :						List[str]     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				generator=lowercase							)
							A_     :						Optional[Any]     =					output.prev_sample

				A_     :						Tuple     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Optional[Any]     =					torch.mean(torch.abs(lowercase							)							)

				assert abs(result_sum.item() - 0.00_02							) < 1E-2
				assert abs(result_mean.item() - 2.2_676E-06							) < 1E-3


	def    _a     (self							):
				A_     :						Optional[int]     =					self.scheduler_classes[0]
				A_     :						Tuple     =					self.get_scheduler_config()
				A_     :						Optional[Any]     =					scheduler_class(**lowercase							)

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

				A_     :						int     =					torch.manual_seed(0							)

				A_     :						Any     =					self.dummy_model()
				A_     :						str     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				A_     :						Optional[int]     =					sample.to(lowercase							)

				for t in scheduler.timesteps:
							A_     :						Union[str, Any]     =					scheduler.scale_model_input(lowercase	,				lowercase							)

							A_     :						int     =					model(lowercase	,				lowercase							)

							A_     :						Optional[Any]     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				generator=lowercase							)
							A_     :						Optional[Any]     =					output.prev_sample

				A_     :						str     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						Dict     =					torch.mean(torch.abs(lowercase							)							)

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


	def    _a     (self							):
				A_     :						Optional[int]     =					self.scheduler_classes[0]
				A_     :						Any     =					self.get_scheduler_config()
				A_     :						Optional[int]     =					scheduler_class(**lowercase	,				use_karras_sigmas=lowercase							)

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

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

				A_     :						int     =					self.dummy_model()
				A_     :						Tuple     =					self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				A_     :						Union[str, Any]     =					sample.to(lowercase							)

				for t in scheduler.timesteps:
							A_     :						List[str]     =					scheduler.scale_model_input(lowercase	,				lowercase							)

							A_     :						Any     =					model(lowercase	,				lowercase							)

							A_     :						List[str]     =					scheduler.step(lowercase	,				lowercase	,				lowercase	,				generator=lowercase							)
							A_     :						Optional[int]     =					output.prev_sample

				A_     :						List[Any]     =					torch.sum(torch.abs(lowercase							)							)
				A_     :						str     =					torch.mean(torch.abs(lowercase							)							)

				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




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)