'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_UpperCAmelCase    :     List[Any]         =       False







class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   pass







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







   """simple docstring"""

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			Dict:
        _A    		=  VersatileDiffusionImageVariationPipeline.from_pretrained('shi-labs/versatile-diffusion'     )
        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(
            image=UpperCamelCase__, generator=UpperCamelCase__, guidance_scale=7.5, num_inference_steps=50, output_type='numpy', ).images

        _A    		=  image[0, 2_53:2_56, 2_53:2_56, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _A    		=  np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]     )

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




'''simple docstring'''






import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available
from . import BaseDiffusersCLICommand





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Union[str, Any]				):
     return EnvironmentCommand()







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @staticmethod
   def 	__UpperCAmelCase   (      UpperCamelCase__      :			ArgumentParser     )  ->			List[str]:
        _A    		=  parser.add_parser('env'     )
        download_parser.set_defaults(func=UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  huggingface_hub.__version__

        _A    		=  'not installed'
        _A    		=  'NA'
        if is_torch_available():
             import torch

             _A    		=  torch.__version__
             _A    		=  torch.cuda.is_available()

        _A    		=  'not installed'
        if is_transformers_available():
             import transformers

             _A    		=  transformers.__version__

        _A    		=  'not installed'
        if is_accelerate_available():
             import accelerate

             _A    		=  accelerate.__version__

        _A    		=  'not installed'
        if is_xformers_available():
             import xformers

             _A    		=  xformers.__version__

        _A    		=  {
            '`diffusers` version': version,
            'Platform': platform.platform(),
            'Python version': platform.python_version(),
            'PyTorch version (GPU?)': f'{pt_version} ({pt_cuda_available})',
            'Huggingface_hub version': hub_version,
            'Transformers version': transformers_version,
            'Accelerate version': accelerate_version,
            'xFormers version': xformers_version,
            'Using GPU in script?': '<fill in>',
            'Using distributed or parallel set-up in script?': '<fill in>',
        }

        print('\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'     )
        print(self.format_dict(UpperCamelCase__     )     )

        return info

   @staticmethod
   def 	__UpperCAmelCase   (      UpperCamelCase__      :			List[Any]     )  ->			Dict:
        return "\n".join([f'- {prop}: {val}' for prop, val in d.items()]     ) + "\n"



