






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		:	Union[str, Any]      					=			False
@nightly
@require_torch_gpu
class    a   (			unittest.TestCase       ):



      """simple docstring"""


      def      UpperCAmelCase				(				self :      Dict )							->				List[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()
      def      UpperCAmelCase				(				self :      Optional[Any] )							->				List[Any]:
        __UpperCAmelCase							:     str									=  VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowercase )
        pipe.set_progress_bar_config(disable=__lowercase )

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

        __UpperCAmelCase							:     Tuple									=  torch.manual_seed(0 )
        __UpperCAmelCase							:     int									=  pipe(
            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 )
          __UpperCAmelCase							:     Optional[Any]									=  VersatileDiffusionDualGuidedPipeline.from_pretrained(__lowercase )

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

        __UpperCAmelCase							:     Optional[int]									=  generator.manual_seed(0 )
        __UpperCAmelCase							:     Union[str, Any]									=  pipe(
            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      UpperCAmelCase				(				self :      Any )							->				Tuple:
        __UpperCAmelCase							:     int									=  VersatileDiffusionDualGuidedPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
        pipe.remove_unused_weights()
        pipe.to(__lowercase )
        pipe.set_progress_bar_config(disable=__lowercase )

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

        __UpperCAmelCase							:     int									=  image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        __UpperCAmelCase							:     str									=  np.array([0.0_787, 0.0_849, 0.0_826, 0.0_812, 0.0_807, 0.0_795, 0.0_818, 0.0_798, 0.0_779] )

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










def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int		):
  for index, item in enumerate(__lowerCamelCase		):
    if item == target:
      return index
  return -1






def     lowerCamelCase__				(   __lowerCamelCase      :					list  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int  ,		__lowerCamelCase      :					int		):
  if not (0 <= high < len(__lowerCamelCase		) and 0 <= low < len(__lowerCamelCase		)):
    raise Exception("""Invalid upper or lower bound!"""		)
  if high < low:
    return -1
  if sequence[low] == target:
    return low
  if sequence[high] == target:
    return high
  return rec_linear_search(__lowerCamelCase  ,		low + 1  ,		high - 1  ,		__lowerCamelCase		)


if __name__ == "__main__":
       a		:	List[str]      					=			input("Enter numbers separated by comma:\n").strip()
       a		:	Tuple      					=			[int(item.strip()) for item in user_input.split(",")]

       a		:	List[str]      					=			int(input("Enter a single number to be found in the list:\n").strip())
       a		:	Optional[Any]      					=			linear_search(sequence, target)
       if result != -1:
              print(f"""linear_search({sequence}, {target}) = {result}""")
       else:
              print(f"""{target} was not found in {sequence}""")


