

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


lowercase_       		=  False


class  A						(							unittest.TestCase ):







    """simple docstring"""





    pass




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







    """simple docstring"""





    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Optional[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion',torch_dtype=torch.floataa		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        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.dual_guided(
            prompt='first prompt',image=lowercase_,text_to_image_strength=0.75,generator=lowercase_,guidance_scale=7.5,num_inference_steps=2,output_type='numpy',).images

        with tempfile.TemporaryDirectory() as tmpdirname:
            pipe.save_pretrained(lowercase_		)
            A__          =   VersatileDiffusionPipeline.from_pretrained(lowercase_,torch_dtype=torch.floataa		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        A__          =   generator.manual_seed(0		)
        A__          =   pipe.dual_guided(
            prompt='first prompt',image=lowercase_,text_to_image_strength=0.75,generator=lowercase_,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 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        A__          =   VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion',torch_dtype=torch.floataa		)
        pipe.to(lowercase_		)
        pipe.set_progress_bar_config(disable=lowercase_		)

        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.dual_guided(
            prompt=lowercase_,image=lowercase_,text_to_image_strength=0.75,generator=lowercase_,guidance_scale=7.5,num_inference_steps=5_0,output_type='numpy',).images

        A__          =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        A__          =   np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

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

        A__          =   'A painting of a squirrel eating a burger '
        A__          =   torch.manual_seed(0		)
        A__          =   pipe.text_to_image(
            prompt=lowercase_,generator=lowercase_,guidance_scale=7.5,num_inference_steps=5_0,output_type='numpy'		).images

        A__          =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        A__          =   np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

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

        A__          =   pipe.image_variation(lowercase_,generator=lowercase_,output_type='numpy'		).images

        A__          =   image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

        assert image.shape == (1, 5_1_2, 5_1_2, 3)
        A__          =   np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

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




from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    return {key.lstrip('-'      ): value for key, value in zip(unknown_args[::2]    ,			unknown_args[1::2]      )}
def 						_snake_case(    )							->      Dict:





    '''simple docstring'''






    A__          =   ArgumentParser(
        'HuggingFace Datasets CLI tool'    ,			usage='datasets-cli <command> [<args>]'    ,			allow_abbrev=SCREAMING_SNAKE_CASE__      )
    A__          =   parser.add_subparsers(help='datasets-cli command helpers'      )
    set_verbosity_info()

    # Register commands
    ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE__      )
    EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE__      )
    TestCommand.register_subcommand(SCREAMING_SNAKE_CASE__      )
    RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE__      )
    DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE__      )

    # Parse args
    A__      ,						A__          =   parser.parse_known_args()
    if not hasattr(SCREAMING_SNAKE_CASE__    ,			'func'      ):
        parser.print_help()
        exit(1      )
    A__          =   parse_unknown_args(SCREAMING_SNAKE_CASE__      )

    # Run
    A__          =   args.func(SCREAMING_SNAKE_CASE__    ,			**SCREAMING_SNAKE_CASE__      )
    service.run()


if __name__ == "__main__":
     main()

