


'''simple docstring'''







import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      (DDIMScheduler,)
    __UpperCAmelCase       =      (('eta', 0.0), ('num_inference_steps', 50))


    def   __a				(	self    ,   **a       ):
          UpperCamelCase__      =							{
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "clip_sample": True,
          }

          config.update(**a       )
          return config


    def   __a				(	self    ,   **a       ):
          UpperCamelCase__      =							self.scheduler_classes[0]
          UpperCamelCase__      =							self.get_scheduler_config(**a       )
          UpperCamelCase__      =							scheduler_class(**a       )

          UpperCamelCase__   ,      UpperCamelCase__      =							10, 0.0

          UpperCamelCase__      =							self.dummy_model()
          UpperCamelCase__      =							self.dummy_sample_deter

          scheduler.set_timesteps(a       )

          for t in scheduler.timesteps:
                UpperCamelCase__      =							model(a    ,   a       )
                UpperCamelCase__      =							scheduler.step(a    ,   a    ,   a    ,   a       ).prev_sample

          return sample


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


    def   __a				(	self       ):
          for steps_offset in [0, 1]:
                self.check_over_configs(steps_offset=a       )

          UpperCamelCase__      =							self.scheduler_classes[0]
          UpperCamelCase__      =							self.get_scheduler_config(steps_offset=1       )
          UpperCamelCase__      =							scheduler_class(**a       )
          scheduler.set_timesteps(5       )
          assert torch.equal(scheduler.timesteps    ,   torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]       )       )


    def   __a				(	self       ):
          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=a    ,   beta_end=a       )


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


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


    def   __a				(	self       ):
          for clip_sample in [True, False]:
                self.check_over_configs(clip_sample=a       )


    def   __a				(	self       ):
          for timestep_spacing in ["trailing", "leading"]:
                self.check_over_configs(timestep_spacing=a       )


    def   __a				(	self       ):
          for rescale_betas_zero_snr in [True, False]:
                self.check_over_configs(rescale_betas_zero_snr=a       )


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


    def   __a				(	self       ):
          for t in [1, 10, 49]:
                self.check_over_forward(time_step=a       )


    def   __a				(	self       ):
          for t, num_inference_steps in zip([1, 10, 50]    ,   [10, 50, 5_00]       ):
                self.check_over_forward(time_step=a    ,   num_inference_steps=a       )


    def   __a				(	self       ):
          for t, eta in zip([1, 10, 49]    ,   [0.0, 0.5, 1.0]       ):
                self.check_over_forward(time_step=a    ,   eta=a       )


    def   __a				(	self       ):
          UpperCamelCase__      =							self.scheduler_classes[0]
          UpperCamelCase__      =							self.get_scheduler_config()
          UpperCamelCase__      =							scheduler_class(**a       )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,   0       ) - 0.0       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,   4_00       ) - 0.1_4771       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,   9_60       ) - 0.3_2460       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(0    ,   0       ) - 0.0       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_87    ,   4_86       ) - 0.0_0979       )       ) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,   9_98       ) - 0.02       )       ) < 1e-5


    def   __a				(	self       ):
          UpperCamelCase__      =							self.full_loop()

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

          assert abs(result_sum.item() - 172.0067       ) < 1e-2
          assert abs(result_mean.item() - 0.22_3967       ) < 1e-3


    def   __a				(	self       ):
          UpperCamelCase__      =							self.full_loop(prediction_type="v_prediction"       )

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

          assert abs(result_sum.item() - 52.5302       ) < 1e-2
          assert abs(result_mean.item() - 0.0684       ) < 1e-3


    def   __a				(	self       ):
          # We specify different beta, so that the first alpha is 0.99
          UpperCamelCase__      =							self.full_loop(set_alpha_to_one=a    ,   beta_start=0.01       )
          UpperCamelCase__      =							torch.sum(torch.abs(a       )       )
          UpperCamelCase__      =							torch.mean(torch.abs(a       )       )

          assert abs(result_sum.item() - 149.8295       ) < 1e-2
          assert abs(result_mean.item() - 0.1951       ) < 1e-3


    def   __a				(	self       ):
          # We specify different beta, so that the first alpha is 0.99
          UpperCamelCase__      =							self.full_loop(set_alpha_to_one=a    ,   beta_start=0.01       )
          UpperCamelCase__      =							torch.sum(torch.abs(a       )       )
          UpperCamelCase__      =							torch.mean(torch.abs(a       )       )

          assert abs(result_sum.item() - 149.0784       ) < 1e-2
          assert abs(result_mean.item() - 0.1941       ) < 1e-3









'''simple docstring'''







from __future__ import annotations

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

a__    :   Dict								=			TypeVar('T')
a__    :   Optional[Any]								=			TypeVar('U')
class    lowercase_	(       Generic[T, U]  ):


    def __init__(	self    ,   a    ,   a       ):
          UpperCamelCase__      =							key
          UpperCamelCase__      =							val
          UpperCamelCase__      =							0
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None


    def __repr__(	self       ):
          return "Node: key: {}, val: {}, freq: {}, has next: {}, has prev: {}".format(
              self.key    ,   self.val    ,   self.freq    ,   self.next is not None    ,   self.prev is not None       )

class    lowercase_	(       Generic[T, U]  ):


    def __init__(	self       ):
          UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
          UpperCamelCase__      =							DoubleLinkedListNode(a    ,   a       )
          UpperCamelCase__   ,      UpperCamelCase__      =							self.rear, self.head


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


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							self.rear.prev

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

          UpperCamelCase__      =							node
          UpperCamelCase__      =							previous
          UpperCamelCase__      =							node
          UpperCamelCase__      =							self.rear
          node.freq += 1
          self._position_node(a       )


    def   __a				(	self    ,   a       ):

          while node.prev is not None and node.prev.freq > node.freq:
                # swap node with previous node
                UpperCamelCase__      =							node.prev

                UpperCamelCase__      =							previous_node.prev
                UpperCamelCase__      =							node.prev
                UpperCamelCase__      =							previous_node
                UpperCamelCase__      =							node


    def   __a				(	self    ,   a       ):

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

          UpperCamelCase__      =							node.next
          UpperCamelCase__      =							node.prev
          UpperCamelCase__      =							None
          UpperCamelCase__      =							None
          return node

class    lowercase_	(       Generic[T, U]  ):
    __UpperCAmelCase       =      {}


    def __init__(	self    ,   a       ):
          UpperCamelCase__      =							DoubleLinkedList()
          UpperCamelCase__      =							capacity
          UpperCamelCase__      =							0
          UpperCamelCase__      =							0
          UpperCamelCase__      =							0
          UpperCamelCase__      =							{}


    def __repr__(	self       ):
          return (
              f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
              f'''capacity={self.capacity}, current_size={self.num_keys})'''
          )


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


    def   __a				(	self    ,   a       ):

          if key in self.cache:
                self.hits += 1
                UpperCamelCase__      =							self.cache[key]
                UpperCamelCase__      =							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   __a				(	self    ,   a    ,   a       ):

          if key not in self.cache:
                if self.num_keys >= self.capacity:
                      # delete first node when over capacity
                      UpperCamelCase__      =							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
                      # first_node guaranteed to be in list

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

          else:
                UpperCamelCase__      =							self.list.remove(self.cache[key]       )
                assert node is not None  # node guaranteed to be in list
                UpperCamelCase__      =							value
                self.list.add(a       )


    @classmethod
    def   __a				(	cls    ,   a = 1_28       ):

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

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

                def cache_info() -> LFUCache[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()





