






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 					_snake_case						(			_A ):
	_A          =   (DDPMScheduler,)






	def    lowerCAmelCase_							(		self					,**UpperCamelCase       )						->    List[Any]:
					snake_case__      :Any         =   {
					    "num_train_timesteps": 1_000,
					    "beta_start": 0.0001,
					    "beta_end": 0.02,
					    "beta_schedule": "linear",
					    "variance_type": "fixed_small",
					    "clip_sample": True,
					}

					config.update(**UpperCamelCase       )
					return config






	def    lowerCAmelCase_							(		self       )						->    Dict:
					for timesteps in [1, 5, 100, 1_000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]					,[0.002, 0.02, 0.2, 2]       ):
									self.check_over_configs(beta_start=UpperCamelCase					,beta_end=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					for schedule in ["linear", "squaredcos_cap_v2"]:
									self.check_over_configs(beta_schedule=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					for variance in ["fixed_small", "fixed_large", "other"]:
									self.check_over_configs(variance_type=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					for clip_sample in [True, False]:
									self.check_over_configs(clip_sample=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					self.check_over_configs(thresholding=UpperCamelCase       )
					for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample", "v_prediction"]:
													self.check_over_configs(
													    thresholding=UpperCamelCase					,prediction_type=UpperCamelCase					,sample_max_value=UpperCamelCase					,)






	def    lowerCAmelCase_							(		self       )						->    Optional[Any]:
					for prediction_type in ["epsilon", "sample", "v_prediction"]:
									self.check_over_configs(prediction_type=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					for t in [0, 500, 999]:
									self.check_over_forward(time_step=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :Optional[int]         =   self.scheduler_classes[0]
					snake_case__      :Tuple         =   self.get_scheduler_config()
					snake_case__      :Union[str, Any]         =   scheduler_class(**UpperCamelCase       )

					assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(487       ) - 0.00979       )       ) < 1E-5
					assert torch.sum(torch.abs(scheduler._get_variance(999       ) - 0.02       )       ) < 1E-5






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :str         =   self.scheduler_classes[0]
					snake_case__      :Optional[Any]         =   self.get_scheduler_config()
					snake_case__      :str         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Any         =   len(UpperCamelCase       )

					snake_case__      :Union[str, Any]         =   self.dummy_model()
					snake_case__      :Optional[Any]         =   self.dummy_sample_deter
					snake_case__      :int         =   torch.manual_seed(0       )

					for t in reversed(range(UpperCamelCase       )       ):
									# 1. predict noise residual
									snake_case__      :Optional[int]         =   model(UpperCamelCase					,UpperCamelCase       )

									# 2. predict previous mean of sample x_t-1
									snake_case__      :Union[str, Any]         =   scheduler.step(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,generator=UpperCamelCase       ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									snake_case__      :Union[str, Any]         =   pred_prev_sample

					snake_case__      :int         =   torch.sum(torch.abs(UpperCamelCase       )       )
					snake_case__      :List[Any]         =   torch.mean(torch.abs(UpperCamelCase       )       )

					assert abs(result_sum.item() - 258.9606       ) < 1E-2
					assert abs(result_mean.item() - 0.3372       ) < 1E-3






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :List[Any]         =   self.scheduler_classes[0]
					snake_case__      :Tuple         =   self.get_scheduler_config(prediction_type="v_prediction"       )
					snake_case__      :int         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :List[Any]         =   len(UpperCamelCase       )

					snake_case__      :int         =   self.dummy_model()
					snake_case__      :Any         =   self.dummy_sample_deter
					snake_case__      :List[str]         =   torch.manual_seed(0       )

					for t in reversed(range(UpperCamelCase       )       ):
									# 1. predict noise residual
									snake_case__      :Optional[Any]         =   model(UpperCamelCase					,UpperCamelCase       )

									# 2. predict previous mean of sample x_t-1
									snake_case__      :str         =   scheduler.step(UpperCamelCase					,UpperCamelCase					,UpperCamelCase					,generator=UpperCamelCase       ).prev_sample

									# if t > 0:
									#     noise = self.dummy_sample_deter
									#     variance = scheduler.get_variance(t) ** (0.5) * noise
									#
									# sample = pred_prev_sample + variance
									snake_case__      :List[Any]         =   pred_prev_sample

					snake_case__      :str         =   torch.sum(torch.abs(UpperCamelCase       )       )
					snake_case__      :int         =   torch.mean(torch.abs(UpperCamelCase       )       )

					assert abs(result_sum.item() - 202.0296       ) < 1E-2
					assert abs(result_mean.item() - 0.2631       ) < 1E-3






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :str         =   self.scheduler_classes[0]
					snake_case__      :Dict         =   self.get_scheduler_config()
					snake_case__      :Any         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Tuple         =   [100, 87, 50, 1, 0]

					scheduler.set_timesteps(timesteps=UpperCamelCase       )

					snake_case__      :List[Any]         =   scheduler.timesteps

					for i, timestep in enumerate(UpperCamelCase       ):
									if i == len(UpperCamelCase       ) - 1:
													snake_case__      :Any         =   -1
									else:
													snake_case__      :Optional[Any]         =   timesteps[i + 1]

									snake_case__      :str         =   scheduler.previous_timestep(UpperCamelCase       )
									snake_case__      :Any         =   prev_t.item()

									self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Optional[int]:
					snake_case__      :Dict         =   self.scheduler_classes[0]
					snake_case__      :List[str]         =   self.get_scheduler_config()
					snake_case__      :int         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Optional[int]         =   [100, 87, 50, 51, 0]

					with self.assertRaises(UpperCamelCase					,msg="`custom_timesteps` must be in descending order."       ):
									scheduler.set_timesteps(timesteps=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Optional[Any]         =   self.scheduler_classes[0]
					snake_case__      :str         =   self.get_scheduler_config()
					snake_case__      :List[Any]         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :Tuple         =   [100, 87, 50, 1, 0]
					snake_case__      :Optional[Any]         =   len(UpperCamelCase       )

					with self.assertRaises(UpperCamelCase					,msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."       ):
									scheduler.set_timesteps(num_inference_steps=UpperCamelCase					,timesteps=UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Union[str, Any]         =   self.scheduler_classes[0]
					snake_case__      :Any         =   self.get_scheduler_config()
					snake_case__      :List[str]         =   scheduler_class(**UpperCamelCase       )

					snake_case__      :List[str]         =   [scheduler.config.num_train_timesteps]

					with self.assertRaises(
					    UpperCamelCase					,msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"					,):
									scheduler.set_timesteps(timesteps=UpperCamelCase       )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCAmelCase							:   Tuple							=  {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCAmelCase							:   List[Any]							=  ["BartphoTokenizer"]

if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_bartpho import BartphoTokenizer

else:
	import sys

	__UpperCAmelCase							:   Tuple							=  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)