
"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

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


_a   :      Optional[int]       =				False






@nightly
@require_torch_gpu
class   _UpperCAmelCase					(       unittest.TestCase):
    def        lowerCamelCase__						(     self  ):
      # clean up the VRAM after each test
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[str]            = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
      # remove text_unet
      pipe.remove_unused_weights()
      pipe.to(snake_case_  )
      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    str            = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

      _snake_case  :    List[Any]            = torch.manual_seed(0  )
      _snake_case  :    Dict            = pipe(
          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_  )
        _snake_case  :    Union[str, Any]            = VersatileDiffusionDualGuidedPipeline.from_pretrained(snake_case_  )

      pipe.to(snake_case_  )
      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    List[str]            = generator.manual_seed(0  )
      _snake_case  :    Optional[int]            = pipe(
          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        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"  )
      pipe.remove_unused_weights()
      pipe.to(snake_case_  )
      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    Dict            = "cyberpunk 2077"
      _snake_case  :    Optional[Any]            = load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
      _snake_case  :    List[Any]            = torch.manual_seed(0  )
      _snake_case  :    List[str]            = pipe(
          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

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

      assert image.shape == (1, 5_12, 5_12, 3)
      _snake_case  :    int            = np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]  )

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





"""simple docstring"""







from .stack import Stack
def _A		(		lowercase						):







    """simple docstring"""
    a											=Stack()
    a											={'''(''': ''')''', '''[''': ''']''', '''{''': '''}'''}
    for bracket in parentheses:
        if bracket in bracket_pairs:
            stack.push(lowercase						)
        elif bracket in (")", "]", "}"):
            if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                return False
    return stack.is_empty()


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

       testmod()

       lowerCamelCase_  :  List[str]     		=    ["""((()))""", """((())""", """(()))"""]
       print("""Balanced parentheses demonstration:\n""")
       for example in examples:
              lowerCamelCase_  :  str     		=    """""" if balanced_parentheses(example) else """not """
              print(F'{example} is {not_str}balanced')