


"""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


__snake_case             =   logging.get_logger(__name__)







class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						)     ->		Dict:

       '''simple docstring'''




       super().__init__()
       snake_case   :				Tuple				 =   nn.ModuleList(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     )     ->		Union[ControlNetOutput, Tuple]:

       '''simple docstring'''




       for i, (image, scale, controlnet) in enumerate(zip(UpperCamelCase__						,     UpperCamelCase__						,     self.nets						)						):
           snake_case ,snake_case   :				Optional[Any]				 =   controlnet(
               UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     )

           # merge samples
           if i == 0:
               snake_case ,snake_case   :				Dict				 =   down_samples, mid_sample
           else:
               snake_case   :				Tuple				 =   [
                   samples_prev + samples_curr
                   for samples_prev, samples_curr in zip(UpperCamelCase__						,     UpperCamelCase__						)
               ]
               mid_block_res_sample += mid_sample

       return down_block_res_samples, mid_block_res_sample







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__ = True						,     UpperCamelCase__ = None						,     UpperCamelCase__ = False						,     UpperCamelCase__ = None						,     )     ->		Any:

       '''simple docstring'''




       snake_case   :				int				 =   0
       snake_case   :				List[Any]				 =   save_directory
       for controlnet in self.nets:
           controlnet.save_pretrained(
               UpperCamelCase__						,     is_main_process=UpperCamelCase__						,     save_function=UpperCamelCase__						,     safe_serialization=UpperCamelCase__						,     variant=UpperCamelCase__						,     )

           idx += 1
           snake_case   :				List[str]				 =   model_path_to_save + F'_{idx}'







   @classmethod
   def 			lowerCamelCase		(     cls						,     UpperCamelCase__						,     **UpperCamelCase__						)     ->		List[str]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   0
       snake_case   :				int				 =   []

       # 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`, ...
       snake_case   :				Optional[Any]				 =   pretrained_model_path
       while os.path.isdir(UpperCamelCase__						):
           snake_case   :				int				 =   ControlNetModel.from_pretrained(UpperCamelCase__						,     **UpperCamelCase__						)
           controlnets.append(UpperCamelCase__						)

           idx += 1
           snake_case   :				Dict				 =   pretrained_model_path + F'_{idx}'

       logger.info(F'{len(UpperCamelCase__						)} controlnets loaded from {pretrained_model_path}.'						)

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

       return cls(UpperCamelCase__						)









"""simple docstring"""



import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class _lowerCAmelCase    (				unittest.TestCase     ):







   @property
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       torch.manual_seed(0						)
       snake_case   :				int				 =   UNetaDModel(
           block_out_channels=(32, 64)						,     layers_per_block=2						,     sample_size=32						,     in_channels=3						,     out_channels=3						,     down_block_types=("DownBlock2D", "AttnDownBlock2D")						,     up_block_types=("AttnUpBlock2D", "UpBlock2D")						,     )
       return model







   def 			lowerCamelCase		(     self						)     ->		Any:

       '''simple docstring'''




       snake_case   :				Any				 =   self.dummy_uncond_unet
       snake_case   :				Tuple				 =   KarrasVeScheduler()

       snake_case   :				int				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Optional[Any]				 =   torch.manual_seed(0						)
       snake_case   :				List[Any]				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Dict				 =   pipe(num_inference_steps=2						,     generator=UpperCamelCase__						,     output_type="numpy"						,     return_dict=UpperCamelCase__						)[0]

       snake_case   :				Tuple				 =   image[0, -3:, -3:, -1]
       snake_case   :				Tuple				 =   image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 32, 32, 3)
       snake_case   :				int				 =   np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2







@slow
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):







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

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   "google/ncsnpp-celebahq-256"
       snake_case   :				List[str]				 =   UNetaDModel.from_pretrained(UpperCamelCase__						)
       snake_case   :				Optional[Any]				 =   KarrasVeScheduler()

       snake_case   :				Optional[int]				 =   KarrasVePipeline(unet=UpperCamelCase__						,     scheduler=UpperCamelCase__						)
       pipe.to(UpperCamelCase__						)
       pipe.set_progress_bar_config(disable=UpperCamelCase__						)

       snake_case   :				Dict				 =   torch.manual_seed(0						)
       snake_case   :				Union[str, Any]				 =   pipe(num_inference_steps=20						,     generator=UpperCamelCase__						,     output_type="numpy"						).images

       snake_case   :				int				 =   image[0, -3:, -3:, -1]
       assert image.shape == (1, 256, 256, 3)
       snake_case   :				Any				 =   np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

       assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2





