






import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					(DDPMScheduler,)




	def 				_UpperCamelCase     (			self			,     **a   )			->       Tuple:
								snake_case_           =				{
								    'num_train_timesteps': 10_00,
								    'beta_start': 0.0_001,
								    'beta_end': 0.02,
								    'beta_schedule': 'linear',
								    'variance_type': 'fixed_small',
								    'clip_sample': True,
								}

								config.update(**a   )
								return config




	def 				_UpperCamelCase     (			self   )			->       str:
								for timesteps in [1, 5, 1_00, 10_00]:
															self.check_over_configs(num_train_timesteps=a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]			,     [0.002, 0.02, 0.2, 2]   ):
															self.check_over_configs(beta_start=a			,     beta_end=a   )




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								for schedule in ["linear", "squaredcos_cap_v2"]:
															self.check_over_configs(beta_schedule=a   )




	def 				_UpperCamelCase     (			self   )			->       str:
								for variance in ["fixed_small", "fixed_large", "other"]:
															self.check_over_configs(variance_type=a   )




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								for clip_sample in [True, False]:
															self.check_over_configs(clip_sample=a   )




	def 				_UpperCamelCase     (			self   )			->       int:
								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   )			->       Tuple:
								for prediction_type in ["epsilon", "sample", "v_prediction"]:
															self.check_over_configs(prediction_type=a   )




	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								for t in [0, 5_00, 9_99]:
															self.check_over_forward(time_step=a   )




	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config()
								snake_case_           =				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   ) - 0.00_979   )   ) < 1E-5
								assert torch.sum(torch.abs(scheduler._get_variance(9_99   ) - 0.02   )   ) < 1E-5




	def 				_UpperCamelCase     (			self   )			->       Tuple:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config()
								snake_case_           =				scheduler_class(**a   )

								snake_case_           =				len(a   )

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

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

															# 2. predict previous mean of sample x_t-1
															snake_case_           =				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
															snake_case_           =				pred_prev_sample

								snake_case_           =				torch.sum(torch.abs(a   )   )
								snake_case_           =				torch.mean(torch.abs(a   )   )

								assert abs(result_sum.item() - 258.9_606   ) < 1E-2
								assert abs(result_mean.item() - 0.3_372   ) < 1E-3




	def 				_UpperCamelCase     (			self   )			->       Any:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config(prediction_type='v_prediction'   )
								snake_case_           =				scheduler_class(**a   )

								snake_case_           =				len(a   )

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

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

															# 2. predict previous mean of sample x_t-1
															snake_case_           =				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
															snake_case_           =				pred_prev_sample

								snake_case_           =				torch.sum(torch.abs(a   )   )
								snake_case_           =				torch.mean(torch.abs(a   )   )

								assert abs(result_sum.item() - 202.0_296   ) < 1E-2
								assert abs(result_mean.item() - 0.2_631   ) < 1E-3




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config()
								snake_case_           =				scheduler_class(**a   )

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

								scheduler.set_timesteps(timesteps=a   )

								snake_case_           =				scheduler.timesteps

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

															snake_case_           =				scheduler.previous_timestep(a   )
															snake_case_           =				prev_t.item()

															self.assertEqual(a			,     a   )




	def 				_UpperCamelCase     (			self   )			->       int:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config()
								snake_case_           =				scheduler_class(**a   )

								snake_case_           =				[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   )			->       int:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config()
								snake_case_           =				scheduler_class(**a   )

								snake_case_           =				[1_00, 87, 50, 1, 0]
								snake_case_           =				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]:
								snake_case_           =				self.scheduler_classes[0]
								snake_case_           =				self.get_scheduler_config()
								snake_case_           =				scheduler_class(**a   )

								snake_case_           =				[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 __future__ import annotations

from collections.abc import Callable
from typing import Generic, TypeVar

lowercase    				=   TypeVar("T")
lowercase    				=   TypeVar("U")




class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       List[Any]:
								snake_case_           =				key
								snake_case_           =				val
								snake_case_           =				None
								snake_case_           =				None






	def __repr__(			self   )			->       str:
								return (
								    F'''Node: key: {self.key}, val: {self.val}, '''
								    F'''has next: {bool(self.next   )}, has prev: {bool(self.prev   )}'''
								)






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	def __init__(			self   )			->       None:
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_           =				DoubleLinkedListNode(a			,     a   )
								snake_case_     ,						snake_case_           =				self.rear, self.head




	def __repr__(			self   )			->       str:
								snake_case_           =				['DoubleLinkedList']
								snake_case_           =				self.head
								while node.next is not None:
															rep.append(str(a   )   )
															snake_case_           =				node.next
								rep.append(str(self.rear   )   )
								return ",\n    ".join(a   )




	def 				_UpperCamelCase     (			self			,     a   )			->       None:
								snake_case_           =				self.rear.prev

								# All nodes other than self.head are guaranteed to have non-None previous
								assert previous is not None

								snake_case_           =				node
								snake_case_           =				previous
								snake_case_           =				node
								snake_case_           =				self.rear






	def 				_UpperCamelCase     (			self			,     a   )			->       DoubleLinkedListNode[T, U] | None:

								if node.prev is None or node.next is None:
															return None

								snake_case_           =				node.next
								snake_case_           =				node.prev
								snake_case_           =				None
								snake_case_           =				None
								return node






class 					UpperCamelCase_ (     Generic[T, U]							):



	'''simple docstring'''




	lowerCAmelCase						=					{}




	def __init__(			self			,     a   )			->       str:
								snake_case_           =				DoubleLinkedList()
								snake_case_           =				capacity
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				0
								snake_case_           =				{}




	def __repr__(			self   )			->       str:
								return (
								    F'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
								    F'''capacity={self.capacity}, current size={self.num_keys})'''
								)




	def __contains__(			self			,     a   )			->       bool:
								return key in self.cache




	def 				_UpperCamelCase     (			self			,     a   )			->       U | None:
								# Note: pythonic interface would throw KeyError rather than return None

								if key in self.cache:
															self.hits += 1
															snake_case_           =				self.cache[key]
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node == value_node

															# node is guaranteed not None because it is in self.cache
															assert node is not None
															self.list.add(a   )
															return node.val
								self.miss += 1
								return None




	def 				_UpperCamelCase     (			self			,     a			,     a   )			->       None:

								if key not in self.cache:
															if self.num_keys >= self.capacity:
																						# delete first node (oldest) when over capacity
																						snake_case_           =				self.list.head.next

																						# guaranteed to have a non-None first node when num_keys > 0
																						# explain to type checker via assertions
																						assert first_node is not None
																						assert first_node.key is not None
																						assert (
																						    self.list.remove(a   ) is not None
																						)  # node guaranteed to be in list assert node.key is not None

																						del self.cache[first_node.key]
																						self.num_keys -= 1
															snake_case_           =				DoubleLinkedListNode(a			,     a   )
															self.list.add(self.cache[key]   )
															self.num_keys += 1

								else:
															# bump node to the end of the list, update value
															snake_case_           =				self.list.remove(self.cache[key]   )
															assert node is not None  # node guaranteed to be in list
															snake_case_           =				value
															self.list.add(a   )






	@classmethod
	def 				_UpperCamelCase     (			cls			,     a = 1_28   )			->       Callable[[Callable[[T], U]], Callable[..., U]]:

								def cache_decorator_inner(a   ) -> Callable[..., U]:
															def cache_decorator_wrapper(*a   ) -> U:
																						if func not in cls.decorator_function_to_instance_map:
																													snake_case_           =				LRUCache(a   )

																						snake_case_           =				cls.decorator_function_to_instance_map[func].get(args[0]   )
																						if result is None:
																													snake_case_           =				func(*a   )
																													cls.decorator_function_to_instance_map[func].put(args[0]			,     a   )
																						return result

															def cache_info() -> LRUCache[T, U]:
																						return cls.decorator_function_to_instance_map[func]

															setattr(a			,     'cache_info'			,     a   )  # noqa: B010

															return cache_decorator_wrapper

								return cache_decorator_inner


if __name__ == "__main__":
	import doctest

	doctest.testmod()





