import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


snake_case__				= False



class  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     pass





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

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
           # remove text_unet
           pipe.remove_unused_weights()
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				'''A painting of a squirrel eating a burger '''
           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				generator=A_				,				guidance_scale=7.5				,				num_inference_steps=2				,				output_type='''numpy'''						).images

           with tempfile.TemporaryDirectory() as tmpdirname:
                 pipe.save_pretrained(A_						)
                 _lowerCamelCase			    =				VersatileDiffusionTextToImagePipeline.from_pretrained(A_						)
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				generator.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				generator=A_				,				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 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				VersatileDiffusionTextToImagePipeline.from_pretrained(
               '''shi-labs/versatile-diffusion'''				,				torch_dtype=torch.floataa						)
           pipe.to(A_						)
           pipe.set_progress_bar_config(disable=A_						)

           _lowerCamelCase			    =				'''A painting of a squirrel eating a burger '''
           _lowerCamelCase			    =				torch.manual_seed(0						)
           _lowerCamelCase			    =				pipe(
               prompt=A_				,				generator=A_				,				guidance_scale=7.5				,				num_inference_steps=50				,				output_type='''numpy'''						).images

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

           assert image.shape == (1, 5_12, 5_12, 3)
           _lowerCamelCase			    =				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-2
from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip