






"""simple docstring"""


from __future__ import annotations



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  if not list_of_ints:
    return []

  if min(__UpperCamelCase					) < 0:
    raise ValueError('''All numbers must be positive'''					)

  __A		     =					max(len(bin(__UpperCamelCase					)[2:]					) for x in list_of_ints					)
  return _msd_radix_sort(__UpperCamelCase						,			__UpperCamelCase					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if bit_position == 0 or len(__UpperCamelCase					) in [0, 1]:
    return list_of_ints

  __A		     =					[]
  __A		     =					[]
  # Split numbers based on bit at bit_position from the right
  for number in list_of_ints:
    if (number >> (bit_position - 1)) & 1:
      # number has a one at bit bit_position
      ones.append(__UpperCamelCase					)
    else:
      # number has a zero at bit bit_position
      zeros.append(__UpperCamelCase					)

    # recursively split both lists further
  __A		     =					_msd_radix_sort(__UpperCamelCase						,			bit_position - 1					)
  __A		     =					_msd_radix_sort(__UpperCamelCase						,			bit_position - 1					)

  # recombine lists
  __A		     =					zeros
  res.extend(__UpperCamelCase					)

  return res



def    lowerCAmelCase							(       __UpperCamelCase					):




  """simple docstring"""







  __A		     =					len(__UpperCamelCase					)
  if not list_of_ints or length == 1:
    return

  if min(__UpperCamelCase					) < 0:
    raise ValueError('''All numbers must be positive'''					)

  __A		     =					max(len(bin(__UpperCamelCase					)[2:]					) for x in list_of_ints					)
  _msd_radix_sort_inplace(__UpperCamelCase						,			__UpperCamelCase						,			0						,			__UpperCamelCase					)



def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  if bit_position == 0 or end_index - begin_index <= 1:
    return

  bit_position -= 1

  __A		     =					begin_index
  __A		     =					end_index - 1
  while i <= j:
    __A		     =					False
    if not (list_of_ints[i] >> bit_position) & 1:
      # found zero at the beginning
      i += 1
      __A		     =					True
    if (list_of_ints[j] >> bit_position) & 1:
      # found one at the end
      j -= 1
      __A		     =					True

    if changed:
      continue

    __A     ,       __A		     =					list_of_ints[j], list_of_ints[i]
    j -= 1
    if j != i:
      i += 1

  _msd_radix_sort_inplace(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)
  _msd_radix_sort_inplace(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					)


if __name__ == "__main__":
       import doctest

       doctest.testmod()








'''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


lowercase__					  =		False





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






     def 			a_       (      self		)							->     Union[str, Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			a_       (      self		)							->     Dict:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _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(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=2  ,				output_type="numpy"  ,				).images

          with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__UpperCamelCase		)
               _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained(__UpperCamelCase		)

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

          _a            =		generator.manual_seed(0		)
          _a            =		pipe(
              prompt="first prompt"  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				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		)							->     int:
          _a            =		VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"		)
          pipe.remove_unused_weights()
          pipe.to(__UpperCamelCase		)
          pipe.set_progress_bar_config(disable=__UpperCamelCase		)

          _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(
              prompt=__UpperCamelCase  ,				image=__UpperCamelCase  ,				text_to_image_strength=0.75  ,				generator=__UpperCamelCase  ,				guidance_scale=7.5  ,				num_inference_steps=50  ,				output_type="numpy"  ,				).images

          _a            =		image[0, 253:256, 253:256, -1]

          assert image.shape == (1, 512, 512, 3)
          _a            =		np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79]		)

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

