


"""simple docstring"""


import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


A     :   Any			    =			False
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

							@property
							def 			snake_case				(     self	):
										return 12

							@property
							def 			snake_case				(     self	):
										return 12

							@property
							def 			snake_case				(     self	):
										return 32

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			VQModel(
										    block_out_channels=[32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,   latent_channels=3    ,   num_vq_embeddings=self.num_embed    ,   vq_embed_dim=3    ,   )
										return model

							@property
							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"	)
										return tokenizer

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)
										__lowerCAmelCase				    =			CLIPTextConfig(
										    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=self.text_embedder_hidden_size    ,   intermediate_size=37    ,   layer_norm_eps=1e-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   )
										return CLIPTextModel(__a	)

							@property
							def 			snake_case				(     self	):
										torch.manual_seed(0	)

										__lowerCAmelCase				    =			12
										__lowerCAmelCase				    =			12

										__lowerCAmelCase				    =			{
										    "attention_bias": True,
										    "cross_attention_dim": 32,
										    "attention_head_dim": height * width,
										    "num_attention_heads": 1,
										    "num_vector_embeds": self.num_embed,
										    "num_embeds_ada_norm": self.num_embeds_ada_norm,
										    "norm_num_groups": 32,
										    "sample_size": width,
										    "activation_fn": "geglu-approximate",
										}

										__lowerCAmelCase				    =			TransformeraDModel(**__a	)
										return model

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"

										__lowerCAmelCase				    =			self.dummy_vqvae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			self.dummy_tokenizer
										__lowerCAmelCase				    =			self.dummy_transformer
										__lowerCAmelCase				    =			VQDiffusionScheduler(self.num_embed	)
										__lowerCAmelCase				    =			LearnedClassifierFreeSamplingEmbeddings(learnable=__a	)

										__lowerCAmelCase				    =			VQDiffusionPipeline(
										    vqvae=__a    ,   text_encoder=__a    ,   tokenizer=__a    ,   transformer=__a    ,   scheduler=__a    ,   learned_classifier_free_sampling_embeddings=__a    ,   )
										__lowerCAmelCase				    =			pipe.to(__a	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			"teddy bear playing in the pool"

										__lowerCAmelCase				    =			torch.Generator(device=__a	).manual_seed(0	)
										__lowerCAmelCase				    =			pipe([prompt]    ,   generator=__a    ,   num_inference_steps=2    ,   output_type="np"	)
										__lowerCAmelCase				    =			output.images

										__lowerCAmelCase				    =			torch.Generator(device=__a	).manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    [prompt]    ,   generator=__a    ,   output_type="np"    ,   return_dict=__a    ,   num_inference_steps=2	)[0]

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

										assert image.shape == (1, 24, 24, 3)

										__lowerCAmelCase				    =			np.array([0.6_5_5_1, 0.6_1_6_8, 0.5_0_0_8, 0.5_6_7_6, 0.5_6_5_9, 0.4_2_9_5, 0.6_0_7_3, 0.5_5_9_9, 0.4_9_9_2]	)

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



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			"cpu"

										__lowerCAmelCase				    =			self.dummy_vqvae
										__lowerCAmelCase				    =			self.dummy_text_encoder
										__lowerCAmelCase				    =			self.dummy_tokenizer
										__lowerCAmelCase				    =			self.dummy_transformer
										__lowerCAmelCase				    =			VQDiffusionScheduler(self.num_embed	)
										__lowerCAmelCase				    =			LearnedClassifierFreeSamplingEmbeddings(
										    learnable=__a    ,   hidden_size=self.text_embedder_hidden_size    ,   length=tokenizer.model_max_length	)

										__lowerCAmelCase				    =			VQDiffusionPipeline(
										    vqvae=__a    ,   text_encoder=__a    ,   tokenizer=__a    ,   transformer=__a    ,   scheduler=__a    ,   learned_classifier_free_sampling_embeddings=__a    ,   )
										__lowerCAmelCase				    =			pipe.to(__a	)
										pipe.set_progress_bar_config(disable=__a	)

										__lowerCAmelCase				    =			"teddy bear playing in the pool"

										__lowerCAmelCase				    =			torch.Generator(device=__a	).manual_seed(0	)
										__lowerCAmelCase				    =			pipe([prompt]    ,   generator=__a    ,   num_inference_steps=2    ,   output_type="np"	)
										__lowerCAmelCase				    =			output.images

										__lowerCAmelCase				    =			torch.Generator(device=__a	).manual_seed(0	)
										__lowerCAmelCase				    =			pipe(
										    [prompt]    ,   generator=__a    ,   output_type="np"    ,   return_dict=__a    ,   num_inference_steps=2	)[0]

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

										assert image.shape == (1, 24, 24, 3)

										__lowerCAmelCase				    =			np.array([0.6_6_9_3, 0.6_0_7_5, 0.4_9_5_9, 0.5_7_0_1, 0.5_5_8_3, 0.4_3_3_3, 0.6_1_7_1, 0.5_6_8_4, 0.4_9_8_8]	)

										assert np.abs(image_slice.flatten() - expected_slice	).max() < 2.0
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2
@slow
@require_torch_gpu
class   _UpperCamelCase  (   unittest.TestCase		):
							'''simple docstring'''







							def 			snake_case				(     self	):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()



							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			load_numpy(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										    "/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"	)

										__lowerCAmelCase				    =			VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq"	)
										__lowerCAmelCase				    =			pipeline.to(__a	)
										pipeline.set_progress_bar_config(disable=__a	)

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										__lowerCAmelCase				    =			torch.Generator(device=__a	).manual_seed(0	)
										__lowerCAmelCase				    =			pipeline(
										    "teddy bear playing in the pool"    ,   num_images_per_prompt=1    ,   generator=__a    ,   output_type="np"    ,   )

										__lowerCAmelCase				    =			output.images[0]

										assert image.shape == (2_56, 2_56, 3)
										assert np.abs(expected_image - image	).max() < 2.0








"""simple docstring"""


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			False
			while is_sorted is False:  # Until all the indices are traversed keep looping
						__lowerCAmelCase				    =			True
						for i in range(0    , len(_UpperCamelCase      ) - 1    , 2      ):  # iterating over all even indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase    ,	__lowerCAmelCase				    =			input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase				    =			False

						for i in range(1    , len(_UpperCamelCase      ) - 1    , 2      ):  # iterating over all odd indices
									if input_list[i] > input_list[i + 1]:
												__lowerCAmelCase    ,	__lowerCAmelCase				    =			input_list[i + 1], input_list[i]
												# swapping if elements not in order
												__lowerCAmelCase				    =			False
			return input_list


if __name__ == "__main__":
	print("Enter list to be sorted")
	A     :   Union[str, Any]			    =			[int(x) for x in input().split()]
	# inputing elements of the list in one line
	A     :   str			    =			odd_even_sort(input_list)
	print("The sorted list is")
	print(sorted_list)




