




import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     (UnCLIPScheduler,)







     def       lowerCAmelCase_    (     self  ,		**A			)  ->   str:



          '''simple docstring'''



          a     =				{
              "num_train_timesteps": 1000,
              "variance_type": "fixed_small_log",
              "clip_sample": True,
              "clip_sample_range": 1.0,
              "prediction_type": "epsilon",
          }

          config.update(**__UpperCAmelCase			)
          return config







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          for variance in ["fixed_small_log", "learned_range"]:
               self.check_over_configs(variance_type=__UpperCAmelCase			)







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          for clip_sample_range in [1, 5, 10, 20]:
               self.check_over_configs(clip_sample_range=__UpperCAmelCase			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          for time_step in [0, 500, 999]:
               for prev_timestep in [None, 5, 100, 250, 500, 750]:
                    if prev_timestep is not None and prev_timestep >= time_step:
                         continue

                    self.check_over_forward(time_step=__UpperCAmelCase  ,		prev_timestep=__UpperCAmelCase			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(variance_type="fixed_small_log"			)
          a     =				scheduler_class(**__UpperCAmelCase			)

          assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_000e-10			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0_5_4_9_6_2_5			)			) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9_9_9_4_9_8_7			)			) < 1e-5







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config(variance_type="learned_range"			)
          a     =				scheduler_class(**__UpperCAmelCase			)

          a     =				0.5

          assert scheduler._get_variance(1  ,		predicted_variance=__UpperCAmelCase			) - -1_0.1_7_1_2_7_9_0 < 1e-5
          assert scheduler._get_variance(487  ,		predicted_variance=__UpperCAmelCase			) - -5.7_9_9_8_0_5_2 < 1e-5
          assert scheduler._get_variance(999  ,		predicted_variance=__UpperCAmelCase			) - -0.0_0_1_0_0_1_1 < 1e-5







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config()
          a     =				scheduler_class(**__UpperCAmelCase			)

          a     =				scheduler.timesteps

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

          for i, t in enumerate(__UpperCAmelCase			):
               # 1. predict noise residual
               a     =				model(__UpperCAmelCase  ,		__UpperCAmelCase			)

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

               a     =				pred_prev_sample

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

          assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5			) < 1e-2
          assert abs(result_mean.item() - 0.3_2_8_4_7_4_3			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				self.scheduler_classes[0]
          a     =				self.get_scheduler_config()
          a     =				scheduler_class(**__UpperCAmelCase			)

          scheduler.set_timesteps(25			)

          a     =				scheduler.timesteps

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

          for i, t in enumerate(__UpperCAmelCase			):
               # 1. predict noise residual
               a     =				model(__UpperCAmelCase  ,		__UpperCAmelCase			)

               if i + 1 == timesteps.shape[0]:
                    a     =				None
               else:
                    a     =				timesteps[i + 1]

               # 2. predict previous mean of sample x_t-1
               a     =				scheduler.step(
                   __UpperCAmelCase  ,		__UpperCAmelCase  ,		__UpperCAmelCase  ,		prev_timestep=__UpperCAmelCase  ,		generator=__UpperCAmelCase			).prev_sample

               a     =				pred_prev_sample

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

          assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3			) < 1e-2
          assert abs(result_mean.item() - 0.3_3_6_2_0_3_8			) < 1e-3







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          pass







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          pass







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowercase__				:      str	   = TypeVar("T")




class       a__	( Generic[T]				):







     def __init__(     self  ,		A = True			)  ->   None:



          '''simple docstring'''



          a     =				{}  # dictionary of lists
          a     =				directed







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   GraphAdjacencyList[T]:



          '''simple docstring'''



          if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    self.adj_list[destination_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(A			)
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                    a     =				[destination_vertex]
                    a     =				[source_vertex]
          else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[]
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                    a     =				[destination_vertex]
                    a     =				[]

          return self







     def __repr__(     self			)  ->   str:



          '''simple docstring'''



          return pformat(self.adj_list			)

