
'''simple docstring'''
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							(DDPMScheduler,)







				def 						UpperCAmelCase_    (       self						,				**A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     {
											    'num_train_timesteps': 1000,
											    '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     )->		Optional[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






											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     )->		Union[str, Any]:







											'''simple docstring'''






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







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







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											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     )->		Union[str, Any]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Optional[int]:







											'''simple docstring'''






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







				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											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     )     ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487     ) - 0.00_979     )     ) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999     ) - 0.02     )     ) < 1e-5







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







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     len(A_     )

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

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

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

											UpperCamelCase   			=     torch.sum(torch.abs(A_     )     )
											UpperCamelCase   			=     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     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config(prediction_type='v_prediction'     )
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     len(A_     )

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

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

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

											UpperCamelCase   			=     torch.sum(torch.abs(A_     )     )
											UpperCamelCase   			=     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[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [100, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=A_     )

											UpperCamelCase   			=     scheduler.timesteps

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

																		UpperCamelCase   			=     scheduler.previous_timestep(A_     )
																		UpperCamelCase   			=     prev_t.item()

																		self.assertEqual(A_						,				A_     )







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [100, 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:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [100, 87, 50, 1, 0]
											UpperCamelCase   			=     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     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.scheduler_classes[0]
											UpperCamelCase   			=     self.get_scheduler_config()
											UpperCamelCase   			=     scheduler_class(**A_     )

											UpperCamelCase   			=     [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_     )











# 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

__lowerCamelCase			:	str					    =	TypeVar("""T""")



class    A__  (			Generic[T]     ):





      def __init__(					self			,				A_ = True  ):
       '''simple docstring'''




       UpperCamelCase      :   dict[T, list[T]]			      =  {}  # dictionary of lists
       UpperCamelCase      :   Union[str, Any]			      =  directed





      def 			__UpperCamelCase(					self			,				A_			,				A_  ):
       '''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_  )
         UpperCamelCase      :   Any			      =  [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_  )
         UpperCamelCase      :   Any			      =  [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:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   List[str]			      =  [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_  )
         UpperCamelCase      :   List[str]			      =  []
        # 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:
         UpperCamelCase      :   Union[str, Any]			      =  [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:
         UpperCamelCase      :   Union[str, Any]			      =  [destination_vertex]
         UpperCamelCase      :   Dict			      =  []

       return self





      def __repr__(					self  ):
       '''simple docstring'''




       return pformat(self.adj_list  )
