






import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      StableDiffusionXLImgaImgPipeline
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
	UpperCamelCase__								=      PipelineTesterMixin.required_optional_params - {"latents"}
	UpperCamelCase__								=      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	UpperCamelCase__								=      IMAGE_TO_IMAGE_IMAGE_PARAMS
	UpperCamelCase__								=      IMAGE_TO_IMAGE_IMAGE_PARAMS

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							torch.manual_seed(0    )
							_UpperCAmelCase									=			UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,    layers_per_block=2       ,    sample_size=32       ,    in_channels=4       ,    out_channels=4       ,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,    attention_head_dim=(2, 4)       ,    use_linear_projection=UpperCAmelCase       ,    addition_embed_type='text_time'       ,    addition_time_embed_dim=8       ,    transformer_layers_per_block=(1, 2)       ,    projection_class_embeddings_input_dim=80       ,    cross_attention_dim=64       ,    )
							_UpperCAmelCase									=			EulerDiscreteScheduler(
							    beta_start=0.0_00_85       ,    beta_end=0.0_12       ,    steps_offset=1       ,    beta_schedule='scaled_linear'       ,    timestep_spacing='leading'       ,    )
							torch.manual_seed(0    )
							_UpperCAmelCase									=			AutoencoderKL(
							    block_out_channels=[32, 64]       ,    in_channels=3       ,    out_channels=3       ,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,    latent_channels=4       ,    sample_size=128       ,    )
							torch.manual_seed(0    )
							_UpperCAmelCase									=			CLIPTextConfig(
							    bos_token_id=0       ,    eos_token_id=2       ,    hidden_size=32       ,    intermediate_size=37       ,    layer_norm_eps=1e-05       ,    num_attention_heads=4       ,    num_hidden_layers=5       ,    pad_token_id=1       ,    vocab_size=1000       ,    hidden_act='gelu'       ,    projection_dim=32       ,    )
							_UpperCAmelCase									=			CLIPTextModel(UpperCAmelCase    )
							_UpperCAmelCase									=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,    local_files_only=UpperCAmelCase    )

							_UpperCAmelCase									=			CLIPTextModelWithProjection(UpperCAmelCase    )
							_UpperCAmelCase									=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       ,    local_files_only=UpperCAmelCase    )

							_UpperCAmelCase									=			{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'text_encoder_2': text_encoder_a,
							    'tokenizer_2': tokenizer_a,
							    # "safety_checker": None,
							    # "feature_extractor": None,
							}
							return components

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							_UpperCAmelCase									=			floats_tensor((1, 3, 32, 32)       ,    rng=random.Random(UpperCAmelCase    )    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			image / 2 + 0.5
							if str(UpperCAmelCase    ).startswith('mps'    ):
													_UpperCAmelCase									=			torch.manual_seed(UpperCAmelCase    )
							else:
													_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': image,
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 5.0,
							    'output_type': 'numpy',
							    'strength': 0.75,
							}
							return inputs

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			'cpu'  # ensure determinism for the device-dependent torch.Generator
							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			StableDiffusionXLImgaImgPipeline(**UpperCAmelCase    )
							_UpperCAmelCase									=			sd_pipe.to(UpperCAmelCase    )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			sd_pipe(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)

							_UpperCAmelCase									=			np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]    )

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

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_attention_slicing_forward_pass(expected_max_diff=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().test_inference_batch_single_identical(expected_max_diff=3e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.get_dummy_components()
							_UpperCAmelCase									=			StableDiffusionXLImgaImgPipeline(**UpperCAmelCase    )
							_UpperCAmelCase									=			sd_pipe.to(UpperCAmelCase    )
							_UpperCAmelCase									=			sd_pipe.to(UpperCAmelCase    )
							sd_pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							# forward without prompt embeds
							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			3 * ['this is a negative prompt']
							_UpperCAmelCase									=			negative_prompt
							_UpperCAmelCase									=			3 * [inputs['prompt']]

							_UpperCAmelCase									=			sd_pipe(**UpperCAmelCase    )
							_UpperCAmelCase									=			output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							_UpperCAmelCase									=			self.get_dummy_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			3 * ['this is a negative prompt']
							_UpperCAmelCase									=			3 * [inputs.pop('prompt'    )]

							(
							    (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, (
							    _UpperCAmelCase
							)					, 
							)									=			sd_pipe.encode_prompt(UpperCAmelCase       ,    negative_prompt=UpperCAmelCase    )

							_UpperCAmelCase									=			sd_pipe(
							    **UpperCAmelCase       ,    prompt_embeds=UpperCAmelCase       ,    negative_prompt_embeds=UpperCAmelCase       ,    pooled_prompt_embeds=UpperCAmelCase       ,    negative_pooled_prompt_embeds=UpperCAmelCase       ,    )
							_UpperCAmelCase									=			output.images[0, -3:, -3:, -1]

							# make sure that it's equal
							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()    ).max() < 1e-4





@slow
@require_torch_gpu
class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

	def        UpperCamelCase    (							self       ,    UpperCAmelCase       ,    UpperCAmelCase="cpu"       ,    UpperCAmelCase=torch.floataa       ,    UpperCAmelCase=0    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.Generator(device=UpperCAmelCase    ).manual_seed(UpperCAmelCase    )
							_UpperCAmelCase									=			np.random.RandomState(UpperCAmelCase    ).standard_normal((1, 4, 64, 64)    )
							_UpperCAmelCase									=			torch.from_numpy(UpperCAmelCase    ).to(device=UpperCAmelCase       ,    dtype=UpperCAmelCase    )
							_UpperCAmelCase									=			{
							    'prompt': 'a photograph of an astronaut riding a horse',
							    'latents': latents,
							    'generator': generator,
							    'num_inference_steps': 3,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'    )
							pipe.to(UpperCAmelCase    )
							pipe.set_progress_bar_config(disable=UpperCAmelCase    )

							_UpperCAmelCase									=			self.get_inputs(UpperCAmelCase    )
							_UpperCAmelCase									=			pipe(**UpperCAmelCase    ).images
							_UpperCAmelCase									=			image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							_UpperCAmelCase									=			np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]    )
							assert np.abs(image_slice - expected_slice    ).max() < 7e-3








def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						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(circle_sort(unsorted))
