



"""simple docstring"""
from __future__ import annotations

from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class 				lowerCamelCase  (			_lowerCAmelCase				):






					'''simple docstring'''





					def __lt__(       self:							Dict						,     snake_case:							int						)   ->	Optional[int]:
											return self[-1] < other[-1]






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

def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :list[Stack]						=			[]
						# sort into stacks
						for element in collection:
												snake_case_ :Optional[Any]						=			Stack([element]	)
												snake_case_ :List[str]						=			bisect_left(_lowercase,					_lowercase	)
												if i != len(_lowercase	):
																		stacks[i].append(_lowercase	)
												else:
																		stacks.append(_lowercase	)

    # use a heap-based merge to merge stack efficiently
						snake_case_ :Optional[int]						=			merge(*(reversed(_lowercase	) 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))





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

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, 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 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





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






					def    lowerCAmelCase_				(       self:							Tuple						)   ->	Any:
											snake_case_,       snake_case_ :List[str]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-canny"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :Union[str, Any]						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :Union[str, Any]						=			controlnet_params

											snake_case_ :Union[str, Any]						=			"""bird"""
											snake_case_ :List[Any]						=			jax.device_count()
											snake_case_ :List[Any]						=			pipe.prepare_text_inputs([prompts] * num_samples						)

											snake_case_ :List[str]						=			load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""						)
											snake_case_ :List[str]						=			pipe.prepare_image_inputs([canny_image] * num_samples						)

											snake_case_ :Any						=			jax.random.PRNGKey(0						)
											snake_case_ :List[str]						=			jax.random.split(snake_case						,     jax.device_count()						)

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

											snake_case_ :Dict						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :Union[str, Any]						=			images[0, 253:256, 253:256, -1]

											snake_case_ :str						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Dict						=			jnp.array(
											    [0.1_6_7_9_6_9, 0.1_1_6_6_9_9, 0.0_8_1_5_4_3, 0.1_5_4_2_9_7, 0.1_3_2_8_1_2, 0.1_0_8_8_8_7, 0.1_6_9_9_2_2, 0.1_6_9_9_2_2, 0.2_0_5_0_7_8]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2






					def    lowerCAmelCase_				(       self:							int						)   ->	Dict:
											snake_case_,       snake_case_ :List[Any]						=			FlaxControlNetModel.from_pretrained(
											    """lllyasviel/sd-controlnet-openpose"""						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_,       snake_case_ :int						=			FlaxStableDiffusionControlNetPipeline.from_pretrained(
											    """runwayml/stable-diffusion-v1-5"""						,     controlnet=snake_case						,     from_pt=snake_case						,     dtype=jnp.bfloataa						)
											snake_case_ :str						=			controlnet_params

											snake_case_ :Optional[int]						=			"""Chef in the kitchen"""
											snake_case_ :Union[str, Any]						=			jax.device_count()
											snake_case_ :Any						=			pipe.prepare_text_inputs([prompts] * num_samples						)

											snake_case_ :str						=			load_image(
											    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""						)
											snake_case_ :Optional[Any]						=			pipe.prepare_image_inputs([pose_image] * num_samples						)

											snake_case_ :str						=			jax.random.PRNGKey(0						)
											snake_case_ :str						=			jax.random.split(snake_case						,     jax.device_count()						)

											snake_case_ :Tuple						=			replicate(snake_case						)
											snake_case_ :str						=			shard(snake_case						)
											snake_case_ :int						=			shard(snake_case						)

											snake_case_ :List[str]						=			pipe(
											    prompt_ids=snake_case						,     image=snake_case						,     params=snake_case						,     prng_seed=snake_case						,     num_inference_steps=50						,     jit=snake_case						,     ).images
											assert images.shape == (jax.device_count(), 1, 768, 512, 3)

											snake_case_ :str						=			images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]						)
											snake_case_ :int						=			images[0, 253:256, 253:256, -1]

											snake_case_ :Dict						=			jnp.asarray(jax.device_get(image_slice.flatten()						)						)
											snake_case_ :Optional[int]						=			jnp.array(
											    [[0.2_7_1_4_8_4, 0.2_6_1_7_1_9, 0.2_7_5_3_9_1, 0.2_7_7_3_4_4, 0.2_7_9_2_9_7, 0.2_9_1_0_1_6, 0.2_9_4_9_2_2, 0.3_0_2_7_3_4, 0.3_0_2_7_3_4]]						)
											print(f"""output_slice: {output_slice}"""						)
											assert jnp.abs(output_slice - expected_slice						).max() < 1E-2
