
'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class      A							(    __UpperCAmelCase						,  unittest.TestCase			):
			lowerCamelCase							:							Optional[int]    						=					KandinskyPipeline
			lowerCamelCase							:							List[Any]    						=					[
			    """prompt""",
			    """image_embeds""",
			    """negative_image_embeds""",
			]
			lowerCamelCase							:							Any    						=					["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
			lowerCamelCase							:							int    						=					[
			    """generator""",
			    """height""",
			    """width""",
			    """latents""",
			    """guidance_scale""",
			    """negative_prompt""",
			    """num_inference_steps""",
			    """return_dict""",
			    """guidance_scale""",
			    """num_images_per_prompt""",
			    """output_type""",
			    """return_dict""",
			]
			lowerCamelCase							:							Optional[Any]    						=					False



			@property
			def A__	(   self    )     ->						Union[str, Any]:




									'''simple docstring'''



									return 32



			@property
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									return 32



			@property
			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									return self.time_input_dim



			@property
			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									return self.time_input_dim * 4



			@property
			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									return 100



			@property
			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									lowercase__								=    XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""    )
									return tokenizer



			@property
			def A__	(   self    )     ->						List[str]:




									'''simple docstring'''



									torch.manual_seed(0    )
									lowercase__								=    MCLIPConfig(
									    numDims=self.cross_attention_dim    ,       transformerDimensions=self.text_embedder_hidden_size    ,       hidden_size=self.text_embedder_hidden_size    ,       intermediate_size=37    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       vocab_size=1_005    ,       )

									lowercase__								=    MultilingualCLIP(lowerCamelCase__    )
									lowercase__								=    text_encoder.eval()

									return text_encoder



			@property
			def A__	(   self    )     ->						List[Any]:




									'''simple docstring'''



									torch.manual_seed(0    )

									lowercase__								=    {
									    """in_channels""": 4,
									    # Out channels is double in channels because predicts mean and variance
									    """out_channels""": 8,
									    """addition_embed_type""": """text_image""",
									    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
									    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
									    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
									    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
									    """layers_per_block""": 1,
									    """encoder_hid_dim""": self.text_embedder_hidden_size,
									    """encoder_hid_dim_type""": """text_image_proj""",
									    """cross_attention_dim""": self.cross_attention_dim,
									    """attention_head_dim""": 4,
									    """resnet_time_scale_shift""": """scale_shift""",
									    """class_embed_type""": None,
									}

									lowercase__								=    UNetaDConditionModel(**lowerCamelCase__    )
									return model



			@property
			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									return {
									    "block_out_channels": [32, 64],
									    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
									    "in_channels": 3,
									    "latent_channels": 4,
									    "layers_per_block": 1,
									    "norm_num_groups": 8,
									    "norm_type": "spatial",
									    "num_vq_embeddings": 12,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}



			@property
			def A__	(   self    )     ->						Any:




									'''simple docstring'''



									torch.manual_seed(0    )
									lowercase__								=    VQModel(**self.dummy_movq_kwargs    )
									return model



			def A__	(   self    )     ->						Tuple:




									'''simple docstring'''



									lowercase__								=    self.dummy_text_encoder
									lowercase__								=    self.dummy_tokenizer
									lowercase__								=    self.dummy_unet
									lowercase__								=    self.dummy_movq

									lowercase__								=    DDIMScheduler(
									    num_train_timesteps=1_000    ,       beta_schedule="""linear"""    ,       beta_start=0.0_00_85    ,       beta_end=0.0_12    ,       clip_sample=lowerCamelCase__    ,       set_alpha_to_one=lowerCamelCase__    ,       steps_offset=1    ,       prediction_type="""epsilon"""    ,       thresholding=lowerCamelCase__    ,       )

									lowercase__								=    {
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """movq""": movq,
									}
									return components



			def A__	(   self    ,       lowerCamelCase__    ,       lowerCamelCase__=0    )     ->						str:




									'''simple docstring'''



									lowercase__								=    floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )
									lowercase__								=    floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1    )    ).to(lowerCamelCase__    )
									if str(lowerCamelCase__    ).startswith("""mps"""    ):
															lowercase__								=    torch.manual_seed(lowerCamelCase__    )
									else:
															lowercase__								=    torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									lowercase__								=    {
									    """prompt""": """horse""",
									    """image_embeds""": image_embeds,
									    """negative_image_embeds""": negative_image_embeds,
									    """generator""": generator,
									    """height""": 64,
									    """width""": 64,
									    """guidance_scale""": 4.0,
									    """num_inference_steps""": 2,
									    """output_type""": """np""",
									}
									return inputs



			def A__	(   self    )     ->						Optional[Any]:




									'''simple docstring'''



									lowercase__								=    """cpu"""

									lowercase__								=    self.get_dummy_components()

									lowercase__								=    self.pipeline_class(**lowerCamelCase__    )
									lowercase__								=    pipe.to(lowerCamelCase__    )

									pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									lowercase__								=    pipe(**self.get_dummy_inputs(lowerCamelCase__    )    )
									lowercase__								=    output.images

									lowercase__								=    pipe(
									    **self.get_dummy_inputs(lowerCamelCase__    )    ,       return_dict=lowerCamelCase__    ,       )[0]

									lowercase__								=    image[0, -3:, -3:, -1]
									lowercase__								=    image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									lowercase__								=    np.array(
									    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]    )

									assert (
									    np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2
									), F''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
									assert (
									    np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1e-2
									), F''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''





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



			def A__	(   self    )     ->						Optional[int]:




									'''simple docstring'''



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



			def A__	(   self    )     ->						Dict:




									'''simple docstring'''



									lowercase__								=    load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""    )

									lowercase__								=    KandinskyPriorPipeline.from_pretrained(
									    """kandinsky-community/kandinsky-2-1-prior"""    ,       torch_dtype=torch.floataa    )
									pipe_prior.to(lowerCamelCase__    )

									lowercase__								=    KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""    ,       torch_dtype=torch.floataa    )
									lowercase__								=    pipeline.to(lowerCamelCase__    )
									pipeline.set_progress_bar_config(disable=lowerCamelCase__    )

									lowercase__								=    """red cat, 4k photo"""

									lowercase__								=    torch.Generator(device="""cuda"""    ).manual_seed(0    )
									lowercase__	,  lowercase__								=    pipe_prior(
									    lowerCamelCase__    ,       generator=lowerCamelCase__    ,       num_inference_steps=5    ,       negative_prompt=""""""    ,       ).to_tuple()

									lowercase__								=    torch.Generator(device="""cuda"""    ).manual_seed(0    )
									lowercase__								=    pipeline(
									    lowerCamelCase__    ,       image_embeds=lowerCamelCase__    ,       negative_image_embeds=lowerCamelCase__    ,       generator=lowerCamelCase__    ,       num_inference_steps=100    ,       output_type="""np"""    ,       )

									lowercase__								=    output.images[0]

									assert image.shape == (512, 512, 3)

									assert_mean_pixel_difference(lowerCamelCase__    ,       lowerCamelCase__    )








'''simple docstring'''



def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if right <= left:
												return
						lowercase__								=    lowercase__								=    left
						lowercase__								=    right
						lowercase__								=    sorting[left]
						while i <= b:
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[a]
																		a += 1
																		i += 1
												elif sorting[i] > pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[b]
																		b -= 1
												else:
																		i += 1
						quick_sort_apartition(lowercase__						,						lowercase__						,						a - 1		)
						quick_sort_apartition(lowercase__						,						b + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						if left < right:
												lowercase__								=    lomuto_partition(lowercase__						,						lowercase__						,						lowercase__		)
												quick_sort_lomuto_partition(lowercase__						,						lowercase__						,						pivot_index - 1		)
												quick_sort_lomuto_partition(lowercase__						,						pivot_index + 1						,						lowercase__		)





def 	_A						(							lowercase__						,						lowercase__						,						lowercase__		):
						lowercase__								=    sorting[right]
						lowercase__								=    left
						for i in range(lowercase__						,						lowercase__		):
												if sorting[i] < pivot:
																		lowercase__	,  lowercase__								=    sorting[i], sorting[store_index]
																		store_index += 1
						lowercase__	,  lowercase__								=    sorting[store_index], sorting[right]
						return store_index





def 	_A						(							lowercase__		):
						if len(lowercase__		) <= 1:
												return sorting
						return (
						    three_way_radix_quicksort([i for i in sorting if i < sorting[0]]		)
						    + [i for i in sorting if i == sorting[0]]
						    + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]		)
						)


if __name__ == "__main__":
				import doctest

				doctest.testmod(verbose=True)

				__A       =      input("Enter numbers separated by a comma:\n").strip()
				__A       =      [int(item) for item in user_input.split(",")]
				quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
				print(unsorted)






