


'''simple docstring'''

import argparse
import os


lowercase__					  =		{
    "pytorch": {
        "1.13": {
            "torch": "1.13.1",
            "torchvision": "0.14.1",
            "torchaudio": "0.13.1",
            "python": 3.9,
            "cuda": "cu116",
            "install": (
                "python3 -m pip install --no-cache-dir -U torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1"
                " --extra-index-url https://download.pytorch.org/whl/cu116"
            ),
            "base_image": "nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04",
        },
        "1.12": {
            "torch": "1.12.1",
            "torchvision": "0.13.1",
            "torchaudio": "0.12.1",
            "python": 3.9,
            "cuda": "cu113",
            "install": (
                "python3 -m pip install --no-cache-dir -U torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1"
                " --extra-index-url https://download.pytorch.org/whl/cu113"
            ),
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "1.11": {
            "torch": "1.11.0",
            "torchvision": "0.12.0",
            "torchaudio": "0.11.0",
            "python": 3.9,
            "cuda": "cu113",
            "install": (
                "python3 -m pip install --no-cache-dir -U torch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0"
                " --extra-index-url https://download.pytorch.org/whl/cu113"
            ),
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "1.10": {
            "torch": "1.10.2",
            "torchvision": "0.11.3",
            "torchaudio": "0.10.2",
            "python": 3.9,
            "cuda": "cu113",
            "install": (
                "python3 -m pip install --no-cache-dir -U torch==1.10.2 torchvision==0.11.3 torchaudio==0.10.2"
                " --extra-index-url https://download.pytorch.org/whl/cu113"
            ),
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        # torchaudio < 0.10 has no CUDA-enabled binary distributions
        "1.9": {
            "torch": "1.9.1",
            "torchvision": "0.10.1",
            "torchaudio": "0.9.1",
            "python": 3.9,
            "cuda": "cu111",
            "install": (
                "python3 -m pip install --no-cache-dir -U torch==1.9.1 torchvision==0.10.1 torchaudio==0.9.1"
                " --extra-index-url https://download.pytorch.org/whl/cu111"
            ),
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
    },
    "tensorflow": {
        "2.11": {
            "tensorflow": "2.11.1",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.11.1",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "2.10": {
            "tensorflow": "2.10.1",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.10.1",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "2.9": {
            "tensorflow": "2.9.3",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.9.3",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "2.8": {
            "tensorflow": "2.8.2",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.8.2",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "2.7": {
            "tensorflow": "2.7.3",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.7.3",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "2.6": {
            "tensorflow": "2.6.5",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.6.5",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
        "2.5": {
            "tensorflow": "2.5.3",
            "install": "python3 -m pip install --no-cache-dir -U tensorflow==2.5.3",
            "base_image": "nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04",
        },
    },
}


if __name__ == "__main__":
   lowercase__					  =		argparse.ArgumentParser("Choose the framework and version to install")
   parser.add_argument(
       "--framework", help="The framework to install. Should be `torch` or `tensorflow`", type=str, required=True
   )
   parser.add_argument("--version", help="The version of the framework to install.", type=str, required=True)
   lowercase__					  =		parser.parse_args()

   lowercase__					  =		past_versions_testing[args.framework][args.version]

   os.system(f'''echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile''')
   print(f'''echo "export INSTALL_CMD=\'{info["install"]}\'" >> ~/.profile''')

   lowercase__					  =		""
   if args.framework == "pytorch":
      lowercase__					  =		info["cuda"]
   os.system(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')
   print(f'''echo \"export CUDA=\'{cuda}\'\" >> ~/.profile''')





'''simple docstring'''

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


lowercase__					  =		False





@nightly
@require_torch_gpu
class     __SCREAMING_SNAKE_CASE       (   unittest.TestCase			):






     def 			a_       (      self		)							->     Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Dict:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)

          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase		)

          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		generator.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          assert np.abs(image - new_image		).sum() < 1e-5, "Models don't have the same forward pass"






     def 			a_       (      self		)							->     int:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _a            =		"cyberpunk 2077"
          _a            =		load_image(
              "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"		)
          _a            =		torch.manual_seed(0		)
          _a            =		pipe(
              prompt=__UpperCamelCase  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

          _a            =		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

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

