


A	:		List[Any]    =       0  # The first color of the flag.
A	:		Tuple    =       1  # The second color of the flag.
A	:		List[str]    =       2  # The third color of the flag.
A	:		List[str]    =       (red, white, blue)






def 			UpperCamelCase (     __magic_name__					:							list					)       ->  list:
		"""simple docstring"""




		if not sequence:
				return []
		if len(__magic_name__					) == 1:
				return list(__magic_name__					)
		lowercase__					      =		0
		lowercase__					      =		len(__magic_name__					) - 1
		lowercase__					      =		0
		while mid <= high:
				if sequence[mid] == colors[0]:
						lowercase__      ,     lowercase__					      =		sequence[mid], sequence[low]
						low += 1
						mid += 1
				elif sequence[mid] == colors[1]:
						mid += 1
				elif sequence[mid] == colors[2]:
						lowercase__      ,     lowercase__					      =		sequence[high], sequence[mid]
						high -= 1
				else:
						lowercase__					      =		f'''The elements inside the sequence must contains only {colors} values'''
						raise ValueError(__magic_name__					)
		return sequence


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A	:		Optional[int]    =       input('Enter numbers separated by commas:\n').strip()
	A	:		List[str]    =       [int(item.strip()) for item in user_input.split(',')]
	print(F'{dutch_national_flag_sort(unsorted)}')





import gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
	import jax
	import jax.numpy as jnp



@slow
@require_flax
class 							A   (	unittest.TestCase							):
					'''simple docstring'''






					def   lowerCamelCase__					(self   :						Any				, _UpperCAmelCase   :						List[str]				, _UpperCAmelCase   :						Union[str, Any]		)						->       int:




							"""simple docstring"""


							return f'''gaussian_noise_s={seed}_shape={"_".join([str(_UpperCAmelCase		) for s in shape]		)}.npy'''

					def   lowerCamelCase__					(self   :						Any		)						->       int:




							"""simple docstring"""


							super().tearDown()
							gc.collect()

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						Any=0				, _UpperCAmelCase   :						Optional[Any]=(4, 4, 64, 64)				, _UpperCAmelCase   :						Any=False		)						->       List[Any]:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase				, _UpperCAmelCase		)		)				, dtype=_UpperCAmelCase		)
							return image

					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						str=False				, _UpperCAmelCase   :						str="CompVis/stable-diffusion-v1-4"		)						->       Dict:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		"""bf16""" if fpaa else None

							lowercase__      ,     lowercase__					      =		FlaxUNetaDConditionModel.from_pretrained(
							    _UpperCAmelCase				, subfolder="""unet"""				, dtype=_UpperCAmelCase				, revision=_UpperCAmelCase		)
							return model, params

					def   lowerCamelCase__					(self   :						List[Any]				, _UpperCAmelCase   :						int=0				, _UpperCAmelCase   :						Optional[int]=(4, 77, 768)				, _UpperCAmelCase   :						Any=False		)						->       Optional[Any]:




							"""simple docstring"""


							lowercase__					      =		jnp.bfloataa if fpaa else jnp.floataa
							lowercase__					      =		jnp.array(load_hf_numpy(self.get_file_format(_UpperCAmelCase				, _UpperCAmelCase		)		)				, dtype=_UpperCAmelCase		)
							return hidden_states

					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [-0.2_323, -0.1_304, 0.0_813, -0.3_093, -0.0_919, -0.1_571, -0.1_125, -0.5_806]],
					        [17, 0.55, [-0.0_831, -0.2_443, 0.0_901, -0.0_919, 0.3_396, 0.0_103, -0.3_743, 0.0_701]],
					        [8, 0.89, [-0.4_863, 0.0_859, 0.0_875, -0.1_658, 0.9_199, -0.0_114, 0.4_839, 0.4_639]],
					        [3, 1000, [-0.5_649, 0.2_402, -0.5_518, 0.1_248, 1.1_328, -0.2_443, -0.0_325, -1.0_078]],
					        # fmt: on
					    ]		)
					def   lowerCamelCase__					(self   :						int				, _UpperCAmelCase   :						str				, _UpperCAmelCase   :						Union[str, Any]				, _UpperCAmelCase   :						int		)						->       str:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.get_unet_model(model_id="""CompVis/stable-diffusion-v1-4"""				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_latents(_UpperCAmelCase				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_encoder_hidden_states(_UpperCAmelCase				, fpaa=_UpperCAmelCase		)

							lowercase__					      =		model.apply(
							    {"""params""": params}				, _UpperCAmelCase				, jnp.array(_UpperCAmelCase				, dtype=jnp.intaa		)				, encoder_hidden_states=_UpperCAmelCase				, ).sample

							assert sample.shape == latents.shape

							lowercase__					      =		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)				, dtype=jnp.floataa		)
							lowercase__					      =		jnp.array(_UpperCAmelCase				, dtype=jnp.floataa		)

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
							assert jnp.allclose(_UpperCAmelCase				, _UpperCAmelCase				, atol=1E-2		)


					@parameterized.expand(
					    [
					        # fmt: off
					        [83, 4, [0.1_514, 0.0_807, 0.1_624, 0.1_016, -0.1_896, 0.0_263, 0.0_677, 0.2_310]],
					        [17, 0.55, [0.1_164, -0.0_216, 0.0_170, 0.1_589, -0.3_120, 0.1_005, -0.0_581, -0.1_458]],
					        [8, 0.89, [-0.1_758, -0.0_169, 0.1_004, -0.1_411, 0.1_312, 0.1_103, -0.1_996, 0.2_139]],
					        [3, 1000, [0.1_214, 0.0_352, -0.0_731, -0.1_562, -0.0_994, -0.0_906, -0.2_340, -0.0_539]],
					        # fmt: on
					    ]		)
					def   lowerCamelCase__					(self   :						Optional[Any]				, _UpperCAmelCase   :						Tuple				, _UpperCAmelCase   :						int				, _UpperCAmelCase   :						List[str]		)						->       str:




							"""simple docstring"""


							lowercase__      ,     lowercase__					      =		self.get_unet_model(model_id="""stabilityai/stable-diffusion-2"""				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_latents(_UpperCAmelCase				, shape=(4, 4, 96, 96)				, fpaa=_UpperCAmelCase		)
							lowercase__					      =		self.get_encoder_hidden_states(_UpperCAmelCase				, shape=(4, 77, 1024)				, fpaa=_UpperCAmelCase		)

							lowercase__					      =		model.apply(
							    {"""params""": params}				, _UpperCAmelCase				, jnp.array(_UpperCAmelCase				, dtype=jnp.intaa		)				, encoder_hidden_states=_UpperCAmelCase				, ).sample

							assert sample.shape == latents.shape

							lowercase__					      =		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())		)				, dtype=jnp.floataa		)
							lowercase__					      =		jnp.array(_UpperCAmelCase				, dtype=jnp.floataa		)

							# Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
							assert jnp.allclose(_UpperCAmelCase				, _UpperCAmelCase				, atol=1E-2		)

