



'''simple docstring'''

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
	import jax
	import jax.numpy as jnp
	from flax.jax_utils import replicate
	from flax.training.common_utils import shard






@slow
@require_flax
class 						a_		(     unittest.TestCase    ):


							def    UpperCamelCase     (				self   :						List[str]			)			->					Tuple:
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()


							def    UpperCamelCase     (				self   :						Dict			)			->					Any:
										snake_case							,  snake_case:    Optional[Any]      							=FlaxStableDiffusionPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2'      ,						revision='bf16'      ,						dtype=jnp.bfloataa      ,						)

										snake_case:    Union[str, Any]      							='A painting of a squirrel eating a burger'
										snake_case:    Optional[int]      							=jax.device_count()
										snake_case:    Optional[int]      							=num_samples * [prompt]
										snake_case:    Optional[Any]      							=sd_pipe.prepare_inputs(a_			)

										snake_case:    List[Any]      							=replicate(a_			)
										snake_case:    Union[str, Any]      							=shard(a_			)

										snake_case:    int      							=jax.random.PRNGKey(0			)
										snake_case:    Union[str, Any]      							=jax.random.split(a_      ,						jax.device_count()			)

										snake_case:    Tuple      							=sd_pipe(a_      ,						a_      ,						a_      ,						num_inference_steps=2_5      ,						jit=a_			)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										snake_case:    Any      							=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
										snake_case:    Any      							=images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										snake_case:    str      							=jnp.asarray(jax.device_get(image_slice.flatten()			)			)
										snake_case:    str      							=jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2]			)
										print(F'''output_slice: {output_slice}'''			)
										assert jnp.abs(output_slice - expected_slice			).max() < 1E-2




							def    UpperCamelCase     (				self   :						List[Any]			)			->					Optional[int]:
										snake_case:    List[str]      							='stabilityai/stable-diffusion-2'
										snake_case							,  snake_case:    Tuple      							=FlaxDPMSolverMultistepScheduler.from_pretrained(a_      ,						subfolder='scheduler'			)
										snake_case							,  snake_case:    int      							=FlaxStableDiffusionPipeline.from_pretrained(
										    a_      ,						scheduler=a_      ,						revision='bf16'      ,						dtype=jnp.bfloataa      ,						)
										snake_case:    Optional[int]      							=scheduler_params

										snake_case:    Optional[int]      							='A painting of a squirrel eating a burger'
										snake_case:    Dict      							=jax.device_count()
										snake_case:    List[Any]      							=num_samples * [prompt]
										snake_case:    Dict      							=sd_pipe.prepare_inputs(a_			)

										snake_case:    List[str]      							=replicate(a_			)
										snake_case:    Any      							=shard(a_			)

										snake_case:    Optional[int]      							=jax.random.PRNGKey(0			)
										snake_case:    Optional[Any]      							=jax.random.split(a_      ,						jax.device_count()			)

										snake_case:    List[Any]      							=sd_pipe(a_      ,						a_      ,						a_      ,						num_inference_steps=2_5      ,						jit=a_			)[0]
										assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

										snake_case:    Dict      							=images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
										snake_case:    Tuple      							=images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

										snake_case:    Dict      							=jnp.asarray(jax.device_get(image_slice.flatten()			)			)
										snake_case:    Tuple      							=jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7]			)
										print(F'''output_slice: {output_slice}'''			)
										assert jnp.abs(output_slice - expected_slice			).max() < 1E-2









'''simple docstring'''

from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge






@total_ordering
class 						a_		(     snake_case    ):


							def __lt__(				self   :						List[Any]      ,						a_   :						Optional[int]			)			->					List[str]:
										return self[-1] < other[-1]




							def __eq__(				self   :						int      ,						a_   :						Union[str, Any]			)			->					List[str]:
										return self[-1] == other[-1]







def a_     (    __UpperCAmelCase					)				->					list:





			"""simple docstring"""







			snake_case:    list[Stack]      							=[]
			# sort into stacks
			for element in collection:
						snake_case:    int      							=Stack([element]					)
						snake_case:    Union[str, Any]      							=bisect_left(__UpperCAmelCase				,							__UpperCAmelCase					)
						if i != len(__UpperCAmelCase					):
									stacks[i].append(__UpperCAmelCase					)
						else:
									stacks.append(__UpperCAmelCase					)

    # use a heap-based merge to merge stack efficiently
			snake_case:    int      							=merge(*(reversed(__UpperCAmelCase					) for stack in stacks)					)
			return collection


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




