


# 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     UpperCAmelCase__       (					A_   ,  A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  StableDiffusionControlNetImgaImgPipeline
    UpperCAmelCase__						:		List[Any]      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    UpperCAmelCase__						:		Any      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    UpperCAmelCase__						:		str      =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}		)
    UpperCAmelCase__						:		Any      =  IMAGE_TO_IMAGE_IMAGE_PARAMS






    def _a					(			self    )  ->       str:
         torch.manual_seed(0    )
         __UpperCamelCase    							=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    )
         __UpperCamelCase    							=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    )
         __UpperCamelCase    							=DDIMScheduler(
             beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=A_					,   set_alpha_to_one=A_					,   )
         torch.manual_seed(0    )
         __UpperCamelCase    							=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    )
         __UpperCamelCase    							=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					,   )
         __UpperCamelCase    							=CLIPTextModel(A_    )
         __UpperCamelCase    							=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

         __UpperCamelCase    							={
             '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					,   A_					,   A_=0    )  ->       Dict:
         if str(A_    ).startswith('mps'    ):
              __UpperCamelCase    							=torch.manual_seed(A_    )
         else:
              __UpperCamelCase    							=torch.Generator(device=A_    ).manual_seed(A_    )

         __UpperCamelCase    							=2
         __UpperCamelCase    							=randn_tensor(
             (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=A_					,   device=torch.device(A_    )					,   )
         __UpperCamelCase    							=floats_tensor(control_image.shape					,   rng=random.Random(A_    )    ).to(A_    )
         __UpperCamelCase    							=image.cpu().permute(0					,   2					,   3					,   1    )[0]
         __UpperCamelCase    							=Image.fromarray(np.uinta(A_    )    ).convert('RGB'    ).resize((64, 64)    )
         __UpperCamelCase    							={
             '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:
         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    )  ->       int:
         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     UpperCAmelCase__       (					A_   ,  A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  StableDiffusionControlNetImgaImgPipeline
    UpperCAmelCase__						:		Any      =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
    UpperCAmelCase__						:		Any      =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    UpperCAmelCase__						:		Union[str, Any]      =  frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






    def _a					(			self    )  ->       Union[str, Any]:
         torch.manual_seed(0    )
         __UpperCamelCase    							=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(A_    ):
              if isinstance(A_					,   torch.nn.Convad    ):
                   torch.nn.init.normal(m.weight    )
                   m.bias.data.fill_(1.0    )

         __UpperCamelCase    							=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(A_    )

         torch.manual_seed(0    )
         __UpperCamelCase    							=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(A_    )

         torch.manual_seed(0    )
         __UpperCamelCase    							=DDIMScheduler(
             beta_start=0.0_0085					,   beta_end=0.012					,   beta_schedule='scaled_linear'					,   clip_sample=A_					,   set_alpha_to_one=A_					,   )
         torch.manual_seed(0    )
         __UpperCamelCase    							=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    )
         __UpperCamelCase    							=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					,   )
         __UpperCamelCase    							=CLIPTextModel(A_    )
         __UpperCamelCase    							=CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

         __UpperCamelCase    							=MultiControlNetModel([controlneta, controlneta]    )

         __UpperCamelCase    							={
             '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					,   A_					,   A_=0    )  ->       List[str]:
         if str(A_    ).startswith('mps'    ):
              __UpperCamelCase    							=torch.manual_seed(A_    )
         else:
              __UpperCamelCase    							=torch.Generator(device=A_    ).manual_seed(A_    )

         __UpperCamelCase    							=2

         __UpperCamelCase    							=[
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=A_					,   device=torch.device(A_    )					,   ),
             randn_tensor(
                 (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,   generator=A_					,   device=torch.device(A_    )					,   ),
         ]

         __UpperCamelCase    							=floats_tensor(control_image[0].shape					,   rng=random.Random(A_    )    ).to(A_    )
         __UpperCamelCase    							=image.cpu().permute(0					,   2					,   3					,   1    )[0]
         __UpperCamelCase    							=Image.fromarray(np.uinta(A_    )    ).convert('RGB'    ).resize((64, 64)    )
         __UpperCamelCase    							={
             '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    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.get_dummy_components()
         __UpperCamelCase    							=self.pipeline_class(**A_    )
         pipe.to(A_    )

         __UpperCamelCase    							=10.0
         __UpperCamelCase    							=4

         __UpperCamelCase    							=self.get_dummy_inputs(A_    )
         __UpperCamelCase    							=steps
         __UpperCamelCase    							=scale
         __UpperCamelCase    							=pipe(**A_    )[0]

         __UpperCamelCase    							=self.get_dummy_inputs(A_    )
         __UpperCamelCase    							=steps
         __UpperCamelCase    							=scale
         __UpperCamelCase    							=pipe(**A_					,   control_guidance_start=0.1					,   control_guidance_end=0.2    )[0]

         __UpperCamelCase    							=self.get_dummy_inputs(A_    )
         __UpperCamelCase    							=steps
         __UpperCamelCase    							=scale
         __UpperCamelCase    							=pipe(**A_					,   control_guidance_start=[0.1, 0.3]					,   control_guidance_end=[0.2, 0.7]    )[0]

         __UpperCamelCase    							=self.get_dummy_inputs(A_    )
         __UpperCamelCase    							=steps
         __UpperCamelCase    							=scale
         __UpperCamelCase    							=pipe(**A_					,   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    )  ->       List[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    )  ->       Dict:
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )






    def _a					(			self    )  ->       str:
         self._test_inference_batch_single_identical(expected_max_diff=2E-3    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=self.get_dummy_components()
         __UpperCamelCase    							=self.pipeline_class(**A_    )
         pipe.to(A_    )
         pipe.set_progress_bar_config(disable=A_    )
         with tempfile.TemporaryDirectory() as tmpdir:
              try:
                   # save_pretrained is not implemented for Multi-ControlNet
                   pipe.save_pretrained(A_    )
              except NotImplementedError:
                   pass






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



    """simple docstring"""






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






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							=ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'    )

         __UpperCamelCase    							=StableDiffusionControlNetImgaImgPipeline.from_pretrained(
             'runwayml/stable-diffusion-v1-5'					,   safety_checker=A_					,   controlnet=A_    )
         pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=A_    )

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

         __UpperCamelCase    							=pipe(
             A_					,   A_					,   control_image=A_					,   generator=A_					,   output_type='np'					,   num_inference_steps=50					,   strength=0.6					,   )

         __UpperCamelCase    							=output.images[0]

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

         __UpperCamelCase    							=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









from numpy import exp, pi, sqrt

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					float = 0.0			,     SCREAMING_SNAKE_CASE__			:					float = 1.0				):
     return 1 / sqrt(2 * pi * sigma**2				) * exp(-((x - mu) ** 2) / (2 * sigma**2)				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()





