
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					(DDPMScheduler,)






	def     UpperCamelCase_  (	self :			Any							,**A :			List[str]			):
								__A             =			{
								    "num_train_timesteps": 10_00,
								    "beta_start": 0.00_01,
								    "beta_end": 0.02,
								    "beta_schedule": "linear",
								    "variance_type": "fixed_small",
								    "clip_sample": True,
								}

								config.update(**A			)
								return config






	def     UpperCamelCase_  (	self :			Optional[Any]			):
								for timesteps in [1, 5, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=A			)






	def     UpperCamelCase_  (	self :			Optional[int]			):
								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=A							,beta_end=A			)






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






	def     UpperCamelCase_  (	self :			int			):
								for variance in ["fixed_small", "fixed_large", "other"]:
															self.check_over_configs(variance_type=A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=A			)






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






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






	def     UpperCamelCase_  (	self :			List[str]			):
								for t in [0, 5_00, 9_99]:
															self.check_over_forward(time_step=A			)






	def     UpperCamelCase_  (	self :			int			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config()
								__A             =			scheduler_class(**A			)

								assert torch.sum(torch.abs(scheduler._get_variance(0			) - 0.0			)			) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(4_87			) - 0.0_09_79			)			) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99			) - 0.02			)			) < 1E-5






	def     UpperCamelCase_  (	self :			Any			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config()
								__A             =			scheduler_class(**A			)

								__A             =			len(A			)

								__A             =			self.dummy_model()
								__A             =			self.dummy_sample_deter
								__A             =			torch.manual_seed(0			)

								for t in reversed(range(A			)			):
															# 1. predict noise residual
															__A             =			model(A							,A			)

															# 2. predict previous mean of sample x_t-1
															__A             =			scheduler.step(A							,A							,A							,generator=A			).prev_sample

															# if t > 0:
															#     noise = self.dummy_sample_deter
															#     variance = scheduler.get_variance(t) ** (0.5) * noise
															#
															# sample = pred_prev_sample + variance
															__A             =			pred_prev_sample

								__A             =			torch.sum(torch.abs(A			)			)
								__A             =			torch.mean(torch.abs(A			)			)

								assert abs(result_sum.item() - 2_58.96_06			) < 1E-2
								assert abs(result_mean.item() - 0.33_72			) < 1E-3






	def     UpperCamelCase_  (	self :			Optional[int]			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config(prediction_type="v_prediction"			)
								__A             =			scheduler_class(**A			)

								__A             =			len(A			)

								__A             =			self.dummy_model()
								__A             =			self.dummy_sample_deter
								__A             =			torch.manual_seed(0			)

								for t in reversed(range(A			)			):
															# 1. predict noise residual
															__A             =			model(A							,A			)

															# 2. predict previous mean of sample x_t-1
															__A             =			scheduler.step(A							,A							,A							,generator=A			).prev_sample

															# if t > 0:
															#     noise = self.dummy_sample_deter
															#     variance = scheduler.get_variance(t) ** (0.5) * noise
															#
															# sample = pred_prev_sample + variance
															__A             =			pred_prev_sample

								__A             =			torch.sum(torch.abs(A			)			)
								__A             =			torch.mean(torch.abs(A			)			)

								assert abs(result_sum.item() - 2_02.02_96			) < 1E-2
								assert abs(result_mean.item() - 0.26_31			) < 1E-3






	def     UpperCamelCase_  (	self :			Optional[Any]			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config()
								__A             =			scheduler_class(**A			)

								__A             =			[1_00, 87, 50, 1, 0]

								scheduler.set_timesteps(timesteps=A			)

								__A             =			scheduler.timesteps

								for i, timestep in enumerate(A			):
															if i == len(A			) - 1:
																						__A             =			-1
															else:
																						__A             =			timesteps[i + 1]

															__A             =			scheduler.previous_timestep(A			)
															__A             =			prev_t.item()

															self.assertEqual(A							,A			)






	def     UpperCamelCase_  (	self :			Optional[int]			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config()
								__A             =			scheduler_class(**A			)

								__A             =			[1_00, 87, 50, 51, 0]

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






	def     UpperCamelCase_  (	self :			Optional[int]			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config()
								__A             =			scheduler_class(**A			)

								__A             =			[1_00, 87, 50, 1, 0]
								__A             =			len(A			)

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







	def     UpperCamelCase_  (	self :			List[str]			):
								__A             =			self.scheduler_classes[0]
								__A             =			self.get_scheduler_config()
								__A             =			scheduler_class(**A			)

								__A             =			[scheduler.config.num_train_timesteps]

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							:Any										=    {
    'configuration_rag': ['RagConfig'],
    'retrieval_rag': ['RagRetriever'],
    'tokenization_rag': ['RagTokenizer'],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]										=    [
					    'RagModel',
					    'RagPreTrainedModel',
					    'RagSequenceForGeneration',
					    'RagTokenForGeneration',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Any										=    [
					    'TFRagModel',
					    'TFRagPreTrainedModel',
					    'TFRagSequenceForGeneration',
					    'TFRagTokenForGeneration',
					]


if TYPE_CHECKING:
					from .configuration_rag import RagConfig
					from .retrieval_rag import RagRetriever
					from .tokenization_rag import RagTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_rag import (
										    TFRagModel,
										    TFRagPreTrainedModel,
										    TFRagSequenceForGeneration,
										    TFRagTokenForGeneration,
										)

else:
					import sys

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