"""simple docstring"""




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


_a				:		List[str]	   =       False
class 						__A							(			unittest.TestCase		):
     pass







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






     def    __A  (		self  ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					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						:	List[str]        =					"""A painting of a squirrel eating a burger """
       _lowerCAmelCase						:	Optional[Any]        =					torch.manual_seed(0  )
       _lowerCAmelCase						:	Optional[Any]        =					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						:	str        =					VersatileDiffusionTextToImagePipeline.from_pretrained(a__  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:	Optional[int]        =					generator.manual_seed(0  )
       _lowerCAmelCase						:	int        =					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    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					VersatileDiffusionTextToImagePipeline.from_pretrained(
           """shi-labs/versatile-diffusion"""    ,					torch_dtype=torch.floataa  )
       pipe.to(a__  )
       pipe.set_progress_bar_config(disable=a__  )

       _lowerCAmelCase						:	Dict        =					"""A painting of a squirrel eating a burger """
       _lowerCAmelCase						:	Any        =					torch.manual_seed(0  )
       _lowerCAmelCase						:	List[str]        =					pipe(
           prompt=a__    ,					generator=a__    ,					guidance_scale=7.5    ,					num_inference_steps=50    ,					output_type="""numpy"""  ).images

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

       assert image.shape == (1, 512, 512, 3)
       _lowerCAmelCase						:	Dict        =					np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]  )

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





"""simple docstring"""




def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	str      ,_lowerCamelCase						:	bool = False     )	->				str:

  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected string as input, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )
  if not isinstance(_lowerCamelCase      ,_lowerCamelCase     ):
    _lowerCAmelCase						:	Union[str, Any]        =					f"Expected boolean as use_pascal parameter, found {type(_lowerCamelCase     )}"
    raise ValueError(_lowerCamelCase     )

  _lowerCAmelCase						:	str        =					input_str.split("""_"""     )

  _lowerCAmelCase						:	Optional[int]        =					0 if use_pascal else 1

  _lowerCAmelCase						:	List[Any]        =					words[start_index:]

  _lowerCAmelCase						:	Union[str, Any]        =					[word[0].upper() + word[1:] for word in words_to_capitalize]

  _lowerCAmelCase						:	List[Any]        =					"""""" if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]     )


if __name__ == "__main__":
     from doctest import testmod

     testmod()




