


'''simple docstring'''







from ....utils import logging


A				:					Optional[int]    						=			logging.get_logger(__name__)


class 			__lowerCamelCase			(	a_	):
    """simple docstring"""


    def __init__(  self      :							Dict				,							SCREAMING_SNAKE_CASE      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Optional[Any]=None				,							SCREAMING_SNAKE_CASE      :							List[Any]=2048):
           _A					:     int     = config.__dict__
           _A					:     Optional[Any]     = modal_hidden_size
           if num_labels:
                  _A					:     List[str]     = num_labels



'''simple docstring'''







# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class 			__lowerCamelCase			(	a_    , a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionControlNetImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}	)
    a													=   IMAGE_TO_IMAGE_IMAGE_PARAMS


    def        A   (  self      :							Tuple):
           torch.manual_seed(0)
           _A					:     List[str]     = UNetaDConditionModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
           torch.manual_seed(0)
           _A					:     Optional[Any]     = ControlNetModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							in_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							cross_attention_dim=32				,							conditioning_embedding_out_channels=(16, 32)				,							)
           torch.manual_seed(0)
           _A					:     Any     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     List[str]     = AutoencoderKL(
               block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,							latent_channels=4				,							)
           torch.manual_seed(0)
           _A					:     List[str]     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           _A					:     Any     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     Tuple     = {
               'unet': unet,
               'controlnet': controlnet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components


    def        A   (  self      :							Union[str, Any]				,							SCREAMING_SNAKE_CASE      :							Any				,							SCREAMING_SNAKE_CASE      :							Tuple=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[int]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Tuple     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = 2
           _A					:     Tuple     = randn_tensor(
               (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							)
           _A					:     Tuple     = floats_tensor(control_image.shape				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Union[str, Any]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
           _A					:     Any     = {
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
               'image': image,
               'control_image': control_image,
           }

           return inputs


    def        A   (  self      :							str):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,							reason='XFormers attention is only available with CUDA and `xformers` installed'				,							)
    def        A   (  self      :							Tuple):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)


    def        A   (  self      :							int):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3)




class 			__lowerCamelCase			(	a_    , a_    , unittest.TestCase	):
    """simple docstring"""
    a													=   StableDiffusionControlNetImgaImgPipeline
    a													=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    a													=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    a													=   frozenset([]	)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


    def        A   (  self      :							List[str]):
           torch.manual_seed(0)
           _A					:     List[str]     = UNetaDConditionModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,							cross_attention_dim=32				,							)
           torch.manual_seed(0)

           def init_weights(SCREAMING_SNAKE_CASE      :							Union[str, Any]):
                  if isinstance(SCREAMING_SNAKE_CASE				,							torch.nn.Convad):
                         torch.nn.init.normal(m.weight)
                         m.bias.data.fill_(1.0)

           _A					:     int     = ControlNetModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							in_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							cross_attention_dim=32				,							conditioning_embedding_out_channels=(16, 32)				,							)
           controlneta.controlnet_down_blocks.apply(SCREAMING_SNAKE_CASE)

           torch.manual_seed(0)
           _A					:     str     = ControlNetModel(
               block_out_channels=(32, 64)				,							layers_per_block=2				,							in_channels=4				,							down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,							cross_attention_dim=32				,							conditioning_embedding_out_channels=(16, 32)				,							)
           controlneta.controlnet_down_blocks.apply(SCREAMING_SNAKE_CASE)

           torch.manual_seed(0)
           _A					:     List[Any]     = DDIMScheduler(
               beta_start=0.0_0085				,							beta_end=0.012				,							beta_schedule='scaled_linear'				,							clip_sample=SCREAMING_SNAKE_CASE				,							set_alpha_to_one=SCREAMING_SNAKE_CASE				,							)
           torch.manual_seed(0)
           _A					:     Tuple     = AutoencoderKL(
               block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']				,							up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']				,							latent_channels=4				,							)
           torch.manual_seed(0)
           _A					:     str     = CLIPTextConfig(
               bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-05				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1000				,							)
           _A					:     List[Any]     = CLIPTextModel(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

           _A					:     List[str]     = MultiControlNetModel([controlneta, controlneta])

           _A					:     List[str]     = {
               'unet': unet,
               'controlnet': controlnet,
               'scheduler': scheduler,
               'vae': vae,
               'text_encoder': text_encoder,
               'tokenizer': tokenizer,
               'safety_checker': None,
               'feature_extractor': None,
           }
           return components


    def        A   (  self      :							Optional[int]				,							SCREAMING_SNAKE_CASE      :							Dict				,							SCREAMING_SNAKE_CASE      :							List[str]=0):
           if str(SCREAMING_SNAKE_CASE).startswith('mps'):
                  _A					:     Optional[Any]     = torch.manual_seed(SCREAMING_SNAKE_CASE)
           else:
                  _A					:     Union[str, Any]     = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)

           _A					:     Union[str, Any]     = 2

           _A					:     List[str]     = [
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							),
               randn_tensor(
                   (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)				,							generator=SCREAMING_SNAKE_CASE				,							device=torch.device(SCREAMING_SNAKE_CASE)				,							),
           ]

           _A					:     str     = floats_tensor(control_image[0].shape				,							rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
           _A					:     Optional[int]     = image.cpu().permute(0				,							2				,							3				,							1)[0]
           _A					:     Optional[int]     = Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert('RGB').resize((64, 64))
           _A					:     Optional[Any]     = {
               'prompt': 'A painting of a squirrel eating a burger',
               'generator': generator,
               'num_inference_steps': 2,
               'guidance_scale': 6.0,
               'output_type': 'numpy',
               'image': image,
               'control_image': control_image,
           }

           return inputs


    def        A   (  self      :							Tuple):
           _A					:     List[str]     = self.get_dummy_components()
           _A					:     List[Any]     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)

           _A					:     int     = 10.0
           _A					:     Union[str, Any]     = 4

           _A					:     str     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     List[Any]     = steps
           _A					:     List[str]     = scale
           _A					:     int     = pipe(**SCREAMING_SNAKE_CASE)[0]

           _A					:     Any     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Union[str, Any]     = steps
           _A					:     Any     = scale
           _A					:     Dict     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=0.1				,							control_guidance_end=0.2)[0]

           _A					:     Tuple     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     str     = steps
           _A					:     List[Any]     = scale
           _A					:     int     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=[0.1, 0.3]				,							control_guidance_end=[0.2, 0.7])[0]

           _A					:     List[str]     = self.get_dummy_inputs(SCREAMING_SNAKE_CASE)
           _A					:     Tuple     = steps
           _A					:     Tuple     = scale
           _A					:     str     = pipe(**SCREAMING_SNAKE_CASE				,							control_guidance_start=0.4				,							control_guidance_end=[0.5, 0.8])[0]

           # make sure that all outputs are different
           assert np.sum(np.abs(output_a - output_a)) > 1e-3
           assert np.sum(np.abs(output_a - output_a)) > 1e-3
           assert np.sum(np.abs(output_a - output_a)) > 1e-3


    def        A   (  self      :							Optional[Any]):
           return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)


    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()				,							reason='XFormers attention is only available with CUDA and `xformers` installed'				,							)
    def        A   (  self      :							Any):
           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)


    def        A   (  self      :							Dict):
           self._test_inference_batch_single_identical(expected_max_diff=2e-3)


    def        A   (  self      :							str):
           _A					:     Optional[int]     = self.get_dummy_components()
           _A					:     Dict     = self.pipeline_class(**SCREAMING_SNAKE_CASE)
           pipe.to(SCREAMING_SNAKE_CASE)
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
           with tempfile.TemporaryDirectory() as tmpdir:
                  try:
                         # save_pretrained is not implemented for Multi-ControlNet
                         pipe.save_pretrained(SCREAMING_SNAKE_CASE)
                  except NotImplementedError:
                         pass




@slow
@require_torch_gpu
class 			__lowerCamelCase			(	unittest.TestCase	):
    """simple docstring"""


    def        A   (  self      :							Optional[Any]):
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()


    def        A   (  self      :							Any):
           _A					:     Dict     = ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny')

           _A					:     List[Any]     = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
               'runwayml/stable-diffusion-v1-5'				,							safety_checker=SCREAMING_SNAKE_CASE				,							controlnet=SCREAMING_SNAKE_CASE)
           pipe.enable_model_cpu_offload()
           pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

           _A					:     List[Any]     = torch.Generator(device='cpu').manual_seed(0)
           _A					:     List[Any]     = 'evil space-punk bird'
           _A					:     int     = load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png').resize((512, 512))
           _A					:     List[str]     = load_image(
               'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png').resize((512, 512))

           _A					:     Dict     = pipe(
               SCREAMING_SNAKE_CASE				,							SCREAMING_SNAKE_CASE				,							control_image=SCREAMING_SNAKE_CASE				,							generator=SCREAMING_SNAKE_CASE				,							output_type='np'				,							num_inference_steps=50				,							strength=0.6				,							)

           _A					:     Optional[int]     = output.images[0]

           assert image.shape == (512, 512, 3)

           _A					:     int     = load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy')

           assert np.abs(expected_image - image).max() < 9e-2






