

'''simple docstring'''



import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class       SCREAMING_SNAKE_CASE       (				_SCREAMING_SNAKE_CASE       ):






      '''simple docstring'''






      SCREAMING_SNAKE_CASE__			:					Tuple     =					(DDPMScheduler,)




      def 						__UpperCAmelCase      (       self			:		str  ,       **snake_case			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			str				       =		{
              """num_train_timesteps""": 1000,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """variance_type""": """fixed_small""",
              """clip_sample""": True,
          }

          config.update(**lowerCAmelCase_ )
          return config




      def 						__UpperCAmelCase      (       self			:		str ):



          """simple docstring"""





          for timesteps in [1, 5, 100, 1000]:
              self.check_over_configs(num_train_timesteps=lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          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=lowerCAmelCase_  ,       beta_end=lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		Optional[int] ):



          """simple docstring"""





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




      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          for variance in ["fixed_small", "fixed_large", "other"]:
              self.check_over_configs(variance_type=lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		List[str] ):



          """simple docstring"""





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




      def 						__UpperCAmelCase      (       self			:		Dict ):



          """simple docstring"""





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




      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





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




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



          """simple docstring"""





          for t in [0, 500, 999]:
              self.check_over_forward(time_step=lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		str ):



          """simple docstring"""





          _snake_case			:			Optional[int]				       =		self.scheduler_classes[0]
          _snake_case			:			List[Any]				       =		self.get_scheduler_config()
          _snake_case			:			Dict				       =		scheduler_class(**lowerCAmelCase_ )

          assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.0_0979 ) ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1e-5




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



          """simple docstring"""





          _snake_case			:			Any				       =		self.scheduler_classes[0]
          _snake_case			:			List[str]				       =		self.get_scheduler_config()
          _snake_case			:			List[str]				       =		scheduler_class(**lowerCAmelCase_ )

          _snake_case			:			Optional[int]				       =		len(lowerCAmelCase_ )

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

          for t in reversed(range(lowerCAmelCase_ ) ):
              # 1. predict noise residual
              _snake_case			:			Union[str, Any]				       =		model(lowerCAmelCase_  ,       lowerCAmelCase_ )

              # 2. predict previous mean of sample x_t-1
              _snake_case			:			Optional[Any]				       =		scheduler.step(lowerCAmelCase_  ,       lowerCAmelCase_  ,       lowerCAmelCase_  ,       generator=lowerCAmelCase_ ).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			:			str				       =		torch.sum(torch.abs(lowerCAmelCase_ ) )
          _snake_case			:			Tuple				       =		torch.mean(torch.abs(lowerCAmelCase_ ) )

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




      def 						__UpperCAmelCase      (       self			:		Optional[Any] ):



          """simple docstring"""





          _snake_case			:			int				       =		self.scheduler_classes[0]
          _snake_case			:			int				       =		self.get_scheduler_config(prediction_type='v_prediction' )
          _snake_case			:			Any				       =		scheduler_class(**lowerCAmelCase_ )

          _snake_case			:			List[str]				       =		len(lowerCAmelCase_ )

          _snake_case			:			Dict				       =		self.dummy_model()
          _snake_case			:			Any				       =		self.dummy_sample_deter
          _snake_case			:			Tuple				       =		torch.manual_seed(0 )

          for t in reversed(range(lowerCAmelCase_ ) ):
              # 1. predict noise residual
              _snake_case			:			Dict				       =		model(lowerCAmelCase_  ,       lowerCAmelCase_ )

              # 2. predict previous mean of sample x_t-1
              _snake_case			:			str				       =		scheduler.step(lowerCAmelCase_  ,       lowerCAmelCase_  ,       lowerCAmelCase_  ,       generator=lowerCAmelCase_ ).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			:			int				       =		pred_prev_sample

          _snake_case			:			List[Any]				       =		torch.sum(torch.abs(lowerCAmelCase_ ) )
          _snake_case			:			Dict				       =		torch.mean(torch.abs(lowerCAmelCase_ ) )

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




      def 						__UpperCAmelCase      (       self			:		Dict ):



          """simple docstring"""





          _snake_case			:			Optional[int]				       =		self.scheduler_classes[0]
          _snake_case			:			Any				       =		self.get_scheduler_config()
          _snake_case			:			Any				       =		scheduler_class(**lowerCAmelCase_ )

          _snake_case			:			str				       =		[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=lowerCAmelCase_ )

          _snake_case			:			List[str]				       =		scheduler.timesteps

          for i, timestep in enumerate(lowerCAmelCase_ ):
              if i == len(lowerCAmelCase_ ) - 1:
                  _snake_case			:			Dict				       =		-1
              else:
                  _snake_case			:			Optional[int]				       =		timesteps[i + 1]

              _snake_case			:			Optional[Any]				       =		scheduler.previous_timestep(lowerCAmelCase_ )
              _snake_case			:			List[Any]				       =		prev_t.item()

              self.assertEqual(lowerCAmelCase_  ,       lowerCAmelCase_ )




      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			Union[str, Any]				       =		self.scheduler_classes[0]
          _snake_case			:			Optional[int]				       =		self.get_scheduler_config()
          _snake_case			:			Any				       =		scheduler_class(**lowerCAmelCase_ )

          _snake_case			:			Tuple				       =		[100, 87, 50, 51, 0]

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




      def 						__UpperCAmelCase      (       self			:		List[Any] ):



          """simple docstring"""





          _snake_case			:			List[str]				       =		self.scheduler_classes[0]
          _snake_case			:			List[Any]				       =		self.get_scheduler_config()
          _snake_case			:			Tuple				       =		scheduler_class(**lowerCAmelCase_ )

          _snake_case			:			List[Any]				       =		[100, 87, 50, 1, 0]
          _snake_case			:			str				       =		len(lowerCAmelCase_ )

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




      def 						__UpperCAmelCase      (       self			:		Tuple ):



          """simple docstring"""





          _snake_case			:			str				       =		self.scheduler_classes[0]
          _snake_case			:			List[str]				       =		self.get_scheduler_config()
          _snake_case			:			str				       =		scheduler_class(**lowerCAmelCase_ )

          _snake_case			:			Any				       =		[scheduler.config.num_train_timesteps]

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











"""simple docstring"""
class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_=None  ,  lowerCAmelCase_=None				):
		A_		:    Dict						     = data
		A_		:    Dict						     = previous
		A_		:    int						     = next_node





	def __str__(self				):
		return f"""{self.data}"""





	def  lowerCamelCase(self				):
		return self.data





	def  lowerCamelCase(self				):
		return self.next





	def  lowerCamelCase(self				):
		return self.previous






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self  ,  lowerCAmelCase_				):
		A_		:    Optional[Any]						     = head





	def __iter__(self				):
		return self





	def  lowerCamelCase(self				):
		if not self.current:
			raise StopIteration
		else:
			A_		:    str						     = self.current.get_data()
			A_		:    List[str]						     = self.current.get_next()
			return value






class   SCREAMING_SNAKE_CASE	:





	"""simple docstring"""




	def __init__(self				):
		A_		:    str						     = None  # First node in list
		A_		:    Tuple						     = None  # Last node in list





	def __str__(self				):
		A_		:    int						     = self.head
		A_		:    str						     = []
		while current is not None:
			nodes.append(current.get_data()				)
			A_		:    Any						     = current.get_next()
		return " ".join(str(lowerCAmelCase_				) for node in nodes				)





	def __contains__(self  ,  lowerCAmelCase_				):
		A_		:    Union[str, Any]						     = self.head
		while current:
			if current.get_data() == value:
				return True
			A_		:    Union[str, Any]						     = current.get_next()
		return False





	def __iter__(self				):
		return LinkedListIterator(self.head				)





	def  lowerCamelCase(self				):
		if self.head:
			return self.head.get_data()
		return None





	def  lowerCamelCase(self				):
		if self.tail:
			return self.tail.get_data()
		return None





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			A_		:    str						     = node
			A_		:    Optional[int]						     = node
		else:
			self.insert_before_node(self.head  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    List[Any]						     = Node(lowerCAmelCase_				)
		if self.head is None:
			self.set_head(lowerCAmelCase_				)
		else:
			self.set_tail(lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = node
		A_		:    Union[str, Any]						     = node.previous

		if node.get_previous() is None:
			A_		:    Dict						     = node_to_insert
		else:
			A_		:    Optional[Any]						     = node_to_insert

		A_		:    Any						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    Dict						     = node
		A_		:    Optional[Any]						     = node.next

		if node.get_next() is None:
			A_		:    Optional[int]						     = node_to_insert
		else:
			A_		:    List[Any]						     = node_to_insert

		A_		:    Optional[Any]						     = node_to_insert





	def  lowerCamelCase(self  ,  lowerCAmelCase_  ,  lowerCAmelCase_				):
		A_		:    str						     = 1
		A_		:    Tuple						     = Node(lowerCAmelCase_				)
		A_		:    Dict						     = self.head
		while node:
			if current_position == position:
				self.insert_before_node(lowerCAmelCase_  ,  lowerCAmelCase_				)
				return
			current_position += 1
			A_		:    List[str]						     = node.next
		self.insert_after_node(self.tail  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		A_		:    Dict						     = self.head
		while node:
			if node.get_data() == item:
				return node
			A_		:    Optional[int]						     = node.get_next()
		raise Exception("""Node not found"""				)





	def  lowerCamelCase(self  ,  lowerCAmelCase_				):
		if (node := self.get_node(lowerCAmelCase_				)) is not None:
			if node == self.head:
				A_		:    Tuple						     = self.head.get_next()

			if node == self.tail:
				A_		:    Dict						     = self.tail.get_previous()

			self.remove_node_pointers(lowerCAmelCase_				)





	@staticmethod
	def  lowerCamelCase(lowerCAmelCase_				):
		if node.get_next():
			A_		:    Any						     = node.previous

		if node.get_previous():
			A_		:    int						     = node.next

		A_		:    str						     = None
		A_		:    int						     = None





	def  lowerCamelCase(self				):
		return self.head is None






def       __UpperCamelCase						(		):
	pass


if __name__ == "__main__":
							import doctest

							doctest.testmod()

