


'''simple docstring'''







import gc
import tempfile
import unittest

import numpy as np
import torch

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


lowerCAmelCase    :     Optional[int]					=						False
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     pass
@nightly
@require_torch_gpu
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






     def a	( self   ):

        '''simple docstring'''

        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







     def a	( self   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Union[str, Any]   			=       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,	torch_dtype=torch.floataa   )
        pipe.to(snake_case__   )
        pipe.set_progress_bar_config(disable=snake_case__   )

        _lowerCAmelCase    :							Union[str, Any]   			=       load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )

        _lowerCAmelCase    :							List[Any]   			=       torch.manual_seed(0   )
        _lowerCAmelCase    :							List[str]   			=       pipe.dual_guided(
            prompt='first prompt'      ,	image=snake_case__      ,	text_to_image_strength=0.75      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=2      ,	output_type='numpy'      ,	).images

        with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(snake_case__   )
           _lowerCAmelCase    :							Optional[Any]   			=       VersatileDiffusionPipeline.from_pretrained(snake_case__      ,	torch_dtype=torch.floataa   )
        pipe.to(snake_case__   )
        pipe.set_progress_bar_config(disable=snake_case__   )

        _lowerCAmelCase    :							Tuple   			=       generator.manual_seed(0   )
        _lowerCAmelCase    :							List[Any]   			=       pipe.dual_guided(
            prompt='first prompt'      ,	image=snake_case__      ,	text_to_image_strength=0.75      ,	generator=snake_case__      ,	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   ):

        '''simple docstring'''

        _lowerCAmelCase    :							Tuple   			=       VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'      ,	torch_dtype=torch.floataa   )
        pipe.to(snake_case__   )
        pipe.set_progress_bar_config(disable=snake_case__   )

        _lowerCAmelCase    :							str   			=       'cyberpunk 2077'
        _lowerCAmelCase    :							Optional[int]   			=       load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'   )
        _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )
        _lowerCAmelCase    :							Optional[int]   			=       pipe.dual_guided(
            prompt=snake_case__      ,	image=snake_case__      ,	text_to_image_strength=0.75      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=50      ,	output_type='numpy'      ,	).images

        _lowerCAmelCase    :							Union[str, Any]   			=       image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        _lowerCAmelCase    :							int   			=       np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]   )

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

        _lowerCAmelCase    :							Union[str, Any]   			=       'A painting of a squirrel eating a burger '
        _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )
        _lowerCAmelCase    :							Optional[int]   			=       pipe.text_to_image(
            prompt=snake_case__      ,	generator=snake_case__      ,	guidance_scale=7.5      ,	num_inference_steps=50      ,	output_type='numpy'   ).images

        _lowerCAmelCase    :							Optional[Any]   			=       image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        _lowerCAmelCase    :							Dict   			=       np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]   )

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

        _lowerCAmelCase    :							int   			=       pipe.image_variation(snake_case__      ,	generator=snake_case__      ,	output_type='numpy'   ).images

        _lowerCAmelCase    :							List[Any]   			=       image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        _lowerCAmelCase    :							Dict   			=       np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]   )

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





'''simple docstring'''







from typing import Any






def 			lowercase				(_A					):







       """simple docstring"""







       if not input_list:
              return []
       _lowerCAmelCase    :							Optional[int]   			=       [input_list.count(_A					) for value in input_list]
       _lowerCAmelCase    :							int   			=       max(_A					)  # Gets the maximum count in the input list.
       # Gets values of modes
       return sorted({input_list[i] for i, value in enumerate(_A					) if value == y}					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()

