


# 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"""
     _lowerCamelCase   =      StableDiffusionControlNetImgaImgPipeline
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _lowerCamelCase   =      IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}							)
     _lowerCamelCase   =      IMAGE_TO_IMAGE_IMAGE_PARAMS


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  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					=  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					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        torch.manual_seed(0    )
        __a					=  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					=  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					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  {
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )

        __a					=  2
        __a					=  randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,       generator=__A  ,       device=torch.device(__A    )  ,       )
        __a					=  floats_tensor(control_image.shape  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__A    )    ).convert("""RGB"""    ).resize((64, 64)    )
        __a					=  {
            """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 	snake_case_  (	self    ):
        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 	snake_case_  (	self    ):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )


     def 	snake_case_  (	self    ):
        self._test_inference_batch_single_identical(expected_max_diff=2E-3    )







class     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


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


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  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    )

        __a					=  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    )
        __a					=  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    )
        __a					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        torch.manual_seed(0    )
        __a					=  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					=  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					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  MultiControlNetModel([controlneta, controlneta]    )

        __a					=  {
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )

        __a					=  2

        __a					=  [
            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    )  ,       ),
        ]

        __a					=  floats_tensor(control_image[0].shape  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__A    )    ).convert("""RGB"""    ).resize((64, 64)    )
        __a					=  {
            """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 	snake_case_  (	self    ):
        __a					=  self.get_dummy_components()
        __a					=  self.pipeline_class(**__A    )
        pipe.to(__A    )

        __a					=  10.0
        __a					=  4

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  steps
        __a					=  scale
        __a					=  pipe(**__A    )[0]

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

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

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  steps
        __a					=  scale
        __a					=  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 	snake_case_  (	self    ):
        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 	snake_case_  (	self    ):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )


     def 	snake_case_  (	self    ):
        self._test_inference_batch_single_identical(expected_max_diff=2E-3    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_dummy_components()
        __a					=  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 	snake_case_  (	self    ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     def 	snake_case_  (	self    ):
        __a					=  ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""    )

        __a					=  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    )

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

        __a					=  pipe(
            __A  ,       __A  ,       control_image=__A  ,       generator=__A  ,       output_type="""np"""  ,       num_inference_steps=50  ,       strength=0.6  ,       )

        __a					=  output.images[0]

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

        __a					=  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








"""simple docstring"""







import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A		=						False

class       _a  ( unittest.TestCase):



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



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Any:
									return 12



			@property
			def 				__lowercase					(			self							:				List[str]		)							->					Dict:
									return 12



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Union[str, Any]:
									return 32



			@property
			def 				__lowercase					(			self							:				int		)							->					Dict:
									torch.manual_seed(0		)
									snake_case   :			List[str]						    = VQModel(
									    block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,	up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,	latent_channels=3   ,	num_vq_embeddings=self.num_embed   ,	vq_embed_dim=3   ,	)
									return model



			@property
			def 				__lowercase					(			self							:				str		)							->					List[Any]:
									snake_case   :			Tuple						    = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
									return tokenizer



			@property
			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Any:
									torch.manual_seed(0		)
									snake_case   :			Optional[int]						    = CLIPTextConfig(
									    bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=self.text_embedder_hidden_size   ,	intermediate_size=37   ,	layer_norm_eps=1E-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1000   ,	)
									return CLIPTextModel(_lowercase		)



			@property
			def 				__lowercase					(			self							:				Optional[int]		)							->					Optional[int]:
									torch.manual_seed(0		)

									snake_case   :			List[Any]						    = 12
									snake_case   :			Dict						    = 12

									snake_case   :			Tuple						    = {
									    "attention_bias": True,
									    "cross_attention_dim": 32,
									    "attention_head_dim": height * width,
									    "num_attention_heads": 1,
									    "num_vector_embeds": self.num_embed,
									    "num_embeds_ada_norm": self.num_embeds_ada_norm,
									    "norm_num_groups": 32,
									    "sample_size": width,
									    "activation_fn": "geglu-approximate",
									}

									snake_case   :			Dict						    = TransformeraDModel(**_lowercase		)
									return model



			def 				__lowercase					(			self							:				Optional[int]		)							->					Tuple:
									snake_case   :			Optional[Any]						    = "cpu"

									snake_case   :			Optional[int]						    = self.dummy_vqvae
									snake_case   :			Dict						    = self.dummy_text_encoder
									snake_case   :			Tuple						    = self.dummy_tokenizer
									snake_case   :			List[Any]						    = self.dummy_transformer
									snake_case   :			List[Any]						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			List[Any]						    = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase		)

									snake_case   :			Dict						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Any						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Optional[Any]						    = "teddy bear playing in the pool"

									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Tuple						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[int]						    = output.images

									snake_case   :			int						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			List[str]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

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

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			str						    = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]		)

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



			def 				__lowercase					(			self							:				Union[str, Any]		)							->					Optional[int]:
									snake_case   :			List[str]						    = "cpu"

									snake_case   :			Dict						    = self.dummy_vqvae
									snake_case   :			List[Any]						    = self.dummy_text_encoder
									snake_case   :			Optional[Any]						    = self.dummy_tokenizer
									snake_case   :			int						    = self.dummy_transformer
									snake_case   :			str						    = VQDiffusionScheduler(self.num_embed		)
									snake_case   :			Optional[int]						    = LearnedClassifierFreeSamplingEmbeddings(
									    learnable=_lowercase   ,	hidden_size=self.text_embedder_hidden_size   ,	length=tokenizer.model_max_length		)

									snake_case   :			List[str]						    = VQDiffusionPipeline(
									    vqvae=_lowercase   ,	text_encoder=_lowercase   ,	tokenizer=_lowercase   ,	transformer=_lowercase   ,	scheduler=_lowercase   ,	learned_classifier_free_sampling_embeddings=_lowercase   ,	)
									snake_case   :			Optional[Any]						    = pipe.to(_lowercase		)
									pipe.set_progress_bar_config(disable=_lowercase		)

									snake_case   :			Dict						    = "teddy bear playing in the pool"

									snake_case   :			Union[str, Any]						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe([prompt]   ,	generator=_lowercase   ,	num_inference_steps=2   ,	output_type="np"		)
									snake_case   :			Optional[Any]						    = output.images

									snake_case   :			Dict						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Union[str, Any]						    = pipe(
									    [prompt]   ,	generator=_lowercase   ,	output_type="np"   ,	return_dict=_lowercase   ,	num_inference_steps=2		)[0]

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

									assert image.shape == (1, 24, 24, 3)

									snake_case   :			Optional[Any]						    = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]		)

									assert np.abs(image_slice.flatten() - expected_slice		).max() < 2.0
									assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2



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



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



			def 				__lowercase					(			self							:				Dict		)							->					Tuple:
									snake_case   :			Union[str, Any]						    = load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"		)

									snake_case   :			Tuple						    = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"		)
									snake_case   :			Union[str, Any]						    = pipeline.to(_lowercase		)
									pipeline.set_progress_bar_config(disable=_lowercase		)

									# requires GPU generator for gumbel softmax
									# don't use GPU generator in tests though
									snake_case   :			Tuple						    = torch.Generator(device=_lowercase		).manual_seed(0		)
									snake_case   :			Optional[int]						    = pipeline(
									    "teddy bear playing in the pool"   ,	num_images_per_prompt=1   ,	generator=_lowercase   ,	output_type="np"   ,	)

									snake_case   :			List[str]						    = output.images[0]

									assert image.shape == (256, 256, 3)
									assert np.abs(expected_image - image		).max() < 2.0



