


'''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__						:   Dict				=    logging.get_logger(__name__)





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	List[str]:
         super().__init__()
         __lowerCamelCase  :      str														=						nn.ModuleList(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Union[ControlNetOutput, Tuple]:
         for i, (image, scale, controlnet) in enumerate(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	self.nets      )      ):
              __lowerCamelCase							,			__lowerCamelCase  :      str														=						controlnet(
                  SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	)

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

         return down_block_res_samples, mid_block_res_sample







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	List[Any]:
         __lowerCamelCase  :      Any														=						0
         __lowerCamelCase  :      Union[str, Any]														=						save_directory
         for controlnet in self.nets:
              controlnet.save_pretrained(
                  SCREAMING_SNAKE_CASE_     ,	is_main_process=SCREAMING_SNAKE_CASE_     ,	save_function=SCREAMING_SNAKE_CASE_     ,	safe_serialization=SCREAMING_SNAKE_CASE_     ,	variant=SCREAMING_SNAKE_CASE_     ,	)

              idx += 1
              __lowerCamelCase  :      List[str]														=						model_path_to_save + f'_{idx}'







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         __lowerCamelCase  :      str														=						0
         __lowerCamelCase  :      Any														=						[]

         # 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  :      str														=						pretrained_model_path
         while os.path.isdir(SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[int]														=						ControlNetModel.from_pretrained(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
              controlnets.append(SCREAMING_SNAKE_CASE_      )

              idx += 1
              __lowerCamelCase  :      Dict														=						pretrained_model_path + f'_{idx}'

         logger.info(f'{len(SCREAMING_SNAKE_CASE_      )} controlnets loaded from {pretrained_model_path}.'      )

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

         return cls(SCREAMING_SNAKE_CASE_      )










'''simple docstring'''
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






@slow
@require_torch_gpu
class        lowerCAmelCase_  (     unittest.TestCase	):






       def   UpperCamelCase						( self   )->      Optional[int]:
            # clean up the VRAM after each test
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()






       def   UpperCamelCase						( self   )->      Union[str, Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]   )

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






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_euler'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe([prompt]			,     generator=_UpperCamelCase			,     guidance_scale=9.0			,     num_inference_steps=20			,     output_type='np'   )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]   )

            assert np.abs(image_slice.flatten() - expected_slice   ).max() < 5e-1






       def   UpperCamelCase						( self   )->      Optional[int]:
            _A				=							StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'   )
            _A				=							sd_pipe.to(_UpperCamelCase   )
            sd_pipe.set_progress_bar_config(disable=_UpperCamelCase   )

            sd_pipe.set_scheduler('sample_dpmpp_2m'   )

            _A				=							'A painting of a squirrel eating a burger'
            _A				=							torch.manual_seed(0   )
            _A				=							sd_pipe(
                [prompt]			,     generator=_UpperCamelCase			,     guidance_scale=7.5			,     num_inference_steps=15			,     output_type='np'			,     use_karras_sigmas=_UpperCamelCase			,     )

            _A				=							output.images

            _A				=							image[0, -3:, -3:, -1]

            assert image.shape == (1, 512, 512, 3)
            _A				=							np.array(
                [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]   )

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





