




'''simple docstring'''


import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class      A       (						lowerCAmelCase__       ):
							lowercase_							     =							(KDPMaDiscreteScheduler,)
							lowercase_							     =							10







							def   __lowerCAmelCase     (    self			:    str				,     **lowerCAmelCase_			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							{
														    '''num_train_timesteps''': 11_00,
														    '''beta_start''': 0.0_0_0_1,
														    '''beta_end''': 0.0_2,
														    '''beta_schedule''': '''linear''',
														}

														config.update(**__a   )
														return config







							def   __lowerCAmelCase     (    self			:    str   )     ->				Optional[int]:


														"""simple docstring"""


														for timesteps in [10, 50, 1_00, 10_00]:
																					self.check_over_configs(num_train_timesteps=__a   )







							def   __lowerCAmelCase     (    self			:    str   )     ->				Dict:


														"""simple docstring"""


														for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]				,     [0.0_0_0_2, 0.0_0_2, 0.0_2]   ):
																					self.check_over_configs(beta_start=__a				,     beta_end=__a   )







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


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[str]   )     ->				Tuple:


														"""simple docstring"""


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







							def   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[int]:


														"""simple docstring"""


														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config(prediction_type='''v_prediction'''   )
														_a      	=							scheduler_class(**__a   )

														scheduler.set_timesteps(self.num_inference_steps   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter * scheduler.init_noise_sigma
														_a      	=							sample.to(__a   )

														for i, t in enumerate(scheduler.timesteps   ):
																					_a      	=							scheduler.scale_model_input(__a				,     __a   )

																					_a      	=							model(__a				,     __a   )

																					_a      	=							scheduler.step(__a				,     __a				,     __a   )
																					_a      	=							output.prev_sample

														_a      	=							torch.sum(torch.abs(__a   )   )
														_a      	=							torch.mean(torch.abs(__a   )   )

														if torch_device in ["cpu", "mps"]:
																					assert abs(result_sum.item() - 4.6934e-07   ) < 1e-2
																					assert abs(result_mean.item() - 6.1112e-10   ) < 1e-3
														else:
																					# CUDA
																					assert abs(result_sum.item() - 4.693428650170972e-07   ) < 1e-2
																					assert abs(result_mean.item() - 0.0_0_0_2   ) < 1e-3







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


														if torch_device == "mps":
																					return
														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**__a   )

														scheduler.set_timesteps(self.num_inference_steps   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter * scheduler.init_noise_sigma
														_a      	=							sample.to(__a   )

														for i, t in enumerate(scheduler.timesteps   ):
																					_a      	=							scheduler.scale_model_input(__a				,     __a   )

																					_a      	=							model(__a				,     __a   )

																					_a      	=							scheduler.step(__a				,     __a				,     __a   )
																					_a      	=							output.prev_sample

														_a      	=							torch.sum(torch.abs(__a   )   )
														_a      	=							torch.mean(torch.abs(__a   )   )

														if torch_device in ["cpu", "mps"]:
																					assert abs(result_sum.item() - 20.41_25   ) < 1e-2
																					assert abs(result_mean.item() - 0.0_2_6_6   ) < 1e-3
														else:
																					# CUDA
																					assert abs(result_sum.item() - 20.41_25   ) < 1e-2
																					assert abs(result_mean.item() - 0.0_2_6_6   ) < 1e-3







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


														"""simple docstring"""


														if torch_device == "mps":
																					return
														_a      	=							self.scheduler_classes[0]
														_a      	=							self.get_scheduler_config()
														_a      	=							scheduler_class(**__a   )

														scheduler.set_timesteps(self.num_inference_steps				,     device=__a   )

														_a      	=							self.dummy_model()
														_a      	=							self.dummy_sample_deter.to(__a   ) * scheduler.init_noise_sigma

														for t in scheduler.timesteps:
																					_a      	=							scheduler.scale_model_input(__a				,     __a   )

																					_a      	=							model(__a				,     __a   )

																					_a      	=							scheduler.step(__a				,     __a				,     __a   )
																					_a      	=							output.prev_sample

														_a      	=							torch.sum(torch.abs(__a   )   )
														_a      	=							torch.mean(torch.abs(__a   )   )

														if str(__a   ).startswith('''cpu'''   ):
																					# The following sum varies between 148 and 156 on mps. Why?
																					assert abs(result_sum.item() - 20.41_25   ) < 1e-2
																					assert abs(result_mean.item() - 0.0_2_6_6   ) < 1e-3
														else:
																					# CUDA
																					assert abs(result_sum.item() - 20.41_25   ) < 1e-2
																					assert abs(result_mean.item() - 0.0_2_6_6   ) < 1e-3





"""simple docstring"""


import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


A	:  List[Any]            =     logging.get_logger(__name__)
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							def __init__(	self       ,  __a      ):
										super().__init__()
										__lowerCAmelCase							       =     nn.ModuleList(__a      )

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = None       ,  __a = False       ,  __a = True       ,  ):
										for i, (image, scale, controlnet) in enumerate(zip(__a       ,  __a       ,  self.nets      )      ):
													__lowerCAmelCase       ,      __lowerCAmelCase							       =     controlnet(
													    __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  __a       ,  )

													# merge samples
													if i == 0:
																__lowerCAmelCase       ,      __lowerCAmelCase							       =     down_samples, mid_sample
													else:
																__lowerCAmelCase							       =     [
																    samples_prev + samples_curr
																    for samples_prev, samples_curr in zip(__a       ,  __a      )
																]
																mid_block_res_sample += mid_sample

										return down_block_res_samples, mid_block_res_sample

							def      snake_case							(	self       ,  __a       ,  __a = True       ,  __a = None       ,  __a = False       ,  __a = None       ,  ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     save_directory
										for controlnet in self.nets:
													controlnet.save_pretrained(
													    __a       ,  is_main_process=__a       ,  save_function=__a       ,  safe_serialization=__a       ,  variant=__a       ,  )

													idx += 1
													__lowerCAmelCase							       =     model_path_to_save + f"_{idx}"



							@classmethod
							def      snake_case							(	cls       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     0
										__lowerCAmelCase							       =     []

										# load controlnet and append to list until no controlnet directory exists anymore
										# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
										# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
										__lowerCAmelCase							       =     pretrained_model_path
										while os.path.isdir(__a      ):
													__lowerCAmelCase							       =     ControlNetModel.from_pretrained(__a       ,  **__a      )
													controlnets.append(__a      )

													idx += 1
													__lowerCAmelCase							       =     pretrained_model_path + f"_{idx}"

										logger.info(f"{len(__a      )} controlnets loaded from {pretrained_model_path}."      )

										if len(__a      ) == 0:
													raise ValueError(
													    f"No ControlNets found under {os.path.dirname(__a      )}. Expected at least {pretrained_model_path + '_0'}."      )

										return cls(__a      )




