





"""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  __UpperCamelCase       (		lowercase_	, lowercase_	, lowercase_	, unittest.TestCase		):
       lowerCamelCase     :      Dict         =StableDiffusionControlNetImgaImgPipeline
       lowerCamelCase     :      Union[str, Any]         =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       lowerCamelCase     :      Tuple         =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       lowerCamelCase     :      Dict         =IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}		)
       lowerCamelCase     :      Optional[int]         =IMAGE_TO_IMAGE_IMAGE_PARAMS






       def 					__a     (     self   )			->				str:
        torch.manual_seed(0   )
        a   :	int			=       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   :	Union[str, 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   :	str			=       DDIMScheduler(
            beta_start=0.00_085   ,      beta_end=0.012   ,      beta_schedule="scaled_linear"   ,      clip_sample=UpperCamelCase__   ,      set_alpha_to_one=UpperCamelCase__   ,      )
        torch.manual_seed(0   )
        a   :	Optional[int]			=       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   :	int			=       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   :	Dict			=       CLIPTextModel(UpperCamelCase__   )
        a   :	Optional[Any]			=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

        a   :	List[Any]			=       {
            "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   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				Dict:







        if str(UpperCamelCase__   ).startswith("mps"   ):
         a   :	List[str]			=       torch.manual_seed(UpperCamelCase__   )
        else:
         a   :	Optional[int]			=       torch.Generator(device=UpperCamelCase__   ).manual_seed(UpperCamelCase__   )

        a   :	str			=       2
        a   :	Tuple			=       randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,      generator=UpperCamelCase__   ,      device=torch.device(UpperCamelCase__   )   ,      )
        a   :	Dict			=       floats_tensor(control_image.shape   ,      rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
        a   :	Union[str, Any]			=       image.cpu().permute(0   ,      2   ,      3   ,      1   )[0]
        a   :	Optional[Any]			=       Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert("RGB"   ).resize((64, 64)   )
        a   :	Optional[int]			=       {
            "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   )			->				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   )			->				Optional[int]:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3   )






       def 					__a     (     self   )			->				List[Any]:
        self._test_inference_batch_single_identical(expected_max_diff=2E-3   )


class  __UpperCamelCase       (		lowercase_	, lowercase_	, unittest.TestCase		):
       lowerCamelCase     :      int         =StableDiffusionControlNetImgaImgPipeline
       lowerCamelCase     :      int         =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
       lowerCamelCase     :      Optional[int]         =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
       lowerCamelCase     :      Optional[Any]         =frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess






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

        a   :	Optional[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(UpperCamelCase__   )

        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(UpperCamelCase__   )

        torch.manual_seed(0   )
        a   :	Optional[int]			=       DDIMScheduler(
            beta_start=0.00_085   ,      beta_end=0.012   ,      beta_schedule="scaled_linear"   ,      clip_sample=UpperCamelCase__   ,      set_alpha_to_one=UpperCamelCase__   ,      )
        torch.manual_seed(0   )
        a   :	int			=       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   :	int			=       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   :	Optional[int]			=       CLIPTextModel(UpperCamelCase__   )
        a   :	Any			=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"   )

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

        a   :	Optional[int]			=       {
            "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   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				List[str]:







        if str(UpperCamelCase__   ).startswith("mps"   ):
         a   :	List[Any]			=       torch.manual_seed(UpperCamelCase__   )
        else:
         a   :	Optional[int]			=       torch.Generator(device=UpperCamelCase__   ).manual_seed(UpperCamelCase__   )

        a   :	Any			=       2

        a   :	List[Any]			=       [
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,      generator=UpperCamelCase__   ,      device=torch.device(UpperCamelCase__   )   ,      ),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,      generator=UpperCamelCase__   ,      device=torch.device(UpperCamelCase__   )   ,      ),
        ]

        a   :	Tuple			=       floats_tensor(control_image[0].shape   ,      rng=random.Random(UpperCamelCase__   )   ).to(UpperCamelCase__   )
        a   :	str			=       image.cpu().permute(0   ,      2   ,      3   ,      1   )[0]
        a   :	Optional[int]			=       Image.fromarray(np.uinta(UpperCamelCase__   )   ).convert("RGB"   ).resize((64, 64)   )
        a   :	List[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   )			->				Optional[Any]:
        a   :	Tuple			=       self.get_dummy_components()
        a   :	List[str]			=       self.pipeline_class(**UpperCamelCase__   )
        pipe.to(UpperCamelCase__   )

        a   :	int			=       10.0
        a   :	Optional[int]			=       4

        a   :	Dict			=       self.get_dummy_inputs(UpperCamelCase__   )
        a   :	Union[str, Any]			=       steps
        a   :	Optional[Any]			=       scale
        a   :	str			=       pipe(**UpperCamelCase__   )[0]

        a   :	List[str]			=       self.get_dummy_inputs(UpperCamelCase__   )
        a   :	List[Any]			=       steps
        a   :	Any			=       scale
        a   :	List[str]			=       pipe(**UpperCamelCase__   ,      control_guidance_start=0.1   ,      control_guidance_end=0.2   )[0]

        a   :	Union[str, Any]			=       self.get_dummy_inputs(UpperCamelCase__   )
        a   :	Dict			=       steps
        a   :	List[Any]			=       scale
        a   :	List[str]			=       pipe(**UpperCamelCase__   ,      control_guidance_start=[0.1, 0.3]   ,      control_guidance_end=[0.2, 0.7]   )[0]

        a   :	Any			=       self.get_dummy_inputs(UpperCamelCase__   )
        a   :	str			=       steps
        a   :	Optional[int]			=       scale
        a   :	Optional[Any]			=       pipe(**UpperCamelCase__   ,      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[int]:
        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   )			->				Optional[Any]:
        a   :	List[str]			=       self.get_dummy_components()
        a   :	List[Any]			=       self.pipeline_class(**UpperCamelCase__   )
        pipe.to(UpperCamelCase__   )
        pipe.set_progress_bar_config(disable=UpperCamelCase__   )
        with tempfile.TemporaryDirectory() as tmpdir:
         try:
          # save_pretrained is not implemented for Multi-ControlNet
          pipe.save_pretrained(UpperCamelCase__   )
         except NotImplementedError:
          pass


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






       def 					__a     (     self   )			->				List[str]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






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

        a   :	Optional[Any]			=       StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5"   ,      safety_checker=UpperCamelCase__   ,      controlnet=UpperCamelCase__   )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=UpperCamelCase__   )

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

        a   :	Union[str, Any]			=       pipe(
            UpperCamelCase__   ,      UpperCamelCase__   ,      control_image=UpperCamelCase__   ,      generator=UpperCamelCase__   ,      output_type="np"   ,      num_inference_steps=50   ,      strength=0.6   ,      )

        a   :	List[Any]			=       output.images[0]

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

        a   :	Any			=       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"""




# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str=None      )				->Optional[Any]:






	'''simple docstring'''






	if subparsers is not None:
		a   :	Dict			=       subparsers.add_parser("test"      )
	else:
		a   :	Tuple			=       argparse.ArgumentParser("Accelerate test command"      )

	parser.add_argument(
	    "--config_file"					, default=_lowercase					, help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
	        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with 'huggingface'."
	    )					, )

	if subparsers is not None:
		parser.set_defaults(func=_lowercase      )
	return parser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->str:






	'''simple docstring'''






	a   :	List[Any]			=       os.path.sep.join(__file__.split(os.path.sep      )[:-2] + ["test_utils", "scripts", "test_script.py"]      )

	if args.config_file is None:
		a   :	int			=       script_name
	else:
		a   :	int			=       F"""--config_file={args.config_file} {script_name}"""

	a   :	Optional[int]			=       ["accelerate-launch"] + test_args.split()
	a   :	Optional[int]			=       execute_subprocess_async(_lowercase					, env=os.environ.copy()      )
	if result.returncode == 0:
		print("Test is a success! You are ready for your distributed training!"      )






def    _SCREAMING_SNAKE_CASE	(			)				->Tuple:






	'''simple docstring'''






	a   :	Any			=       test_command_parser()
	a   :	Union[str, Any]			=       parser.parse_args()
	test_command(_lowercase      )


if __name__ == "__main__":
		main()


