





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		1
				lowerCamelCase__								=		3
				lowerCamelCase__								=		(3_2, 3_2)

				lowerCamelCase__								=		floats_tensor((batch_size, num_channels) + sizes       ,  rng=random.Random(0							)							).to(__lowerCAmelCase							)
				return image





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		UNetaDConditionModel(
				    block_out_channels=(3_2, 6_4)       ,  layers_per_block=2       ,  sample_size=3_2       ,  in_channels=4       ,  out_channels=4       ,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')       ,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')       ,  cross_attention_dim=3_2       ,  )
				return model





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		AutoencoderKL(
				    block_out_channels=[3_2, 6_4]       ,  in_channels=3       ,  out_channels=3       ,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']       ,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']       ,  latent_channels=4       ,  )
				return model





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__								=		RobertaSeriesConfig(
				    hidden_size=3_2       ,  project_dim=3_2       ,  intermediate_size=3_7       ,  layer_norm_eps=1E-05       ,  num_attention_heads=4       ,  num_hidden_layers=5       ,  pad_token_id=1       ,  vocab_size=5_0_0_6       ,  )
				return RobertaSeriesModelWithTransformation(__lowerCAmelCase							)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				def extract(*__lowerCAmelCase       ,  **__lowerCAmelCase							):






					class 					__A    :

								'''simple docstring'''







								def __init__(    self							):



									'''simple docstring'''







									lowerCamelCase__								=		torch.ones([0]							)





								def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



									'''simple docstring'''







									self.pixel_values.to(__lowerCAmelCase							)
									return self



					return Out()

				return extract





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				lowerCamelCase__								=		self.dummy_cond_unet
				lowerCamelCase__								=		PNDMScheduler(skip_prk_steps=__lowerCAmelCase							)
				lowerCamelCase__								=		self.dummy_vae
				lowerCamelCase__								=		self.dummy_text_encoder
				lowerCamelCase__								=		XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
				lowerCamelCase__								=		7_7

				lowerCamelCase__								=		self.dummy_image.to(__lowerCAmelCase							)
				lowerCamelCase__								=		init_image / 2 + 0.5

				# make sure here that pndm scheduler skips prk
				lowerCamelCase__								=		AltDiffusionImgaImgPipeline(
				    unet=__lowerCAmelCase       ,  scheduler=__lowerCAmelCase       ,  vae=__lowerCAmelCase       ,  text_encoder=__lowerCAmelCase       ,  tokenizer=__lowerCAmelCase       ,  safety_checker=__lowerCAmelCase       ,  feature_extractor=self.dummy_extractor       ,  )
				lowerCamelCase__								=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,  do_normalize=__lowerCAmelCase							)
				lowerCamelCase__								=		alt_pipe.to(__lowerCAmelCase							)
				alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

				lowerCamelCase__								=		'''A painting of a squirrel eating a burger'''
				lowerCamelCase__								=		torch.Generator(device=__lowerCAmelCase							).manual_seed(0							)
				lowerCamelCase__								=		alt_pipe(
				    [prompt]       ,  generator=__lowerCAmelCase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type='''np'''       ,  image=__lowerCAmelCase       ,  )

				lowerCamelCase__								=		output.images

				lowerCamelCase__								=		torch.Generator(device=__lowerCAmelCase							).manual_seed(0							)
				lowerCamelCase__								=		alt_pipe(
				    [prompt]       ,  generator=__lowerCAmelCase       ,  guidance_scale=6.0       ,  num_inference_steps=2       ,  output_type='''np'''       ,  image=__lowerCAmelCase       ,  return_dict=__lowerCAmelCase       ,  )[0]

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

				assert image.shape == (1, 3_2, 3_2, 3)
				lowerCamelCase__								=		np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]							)

				assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 5E-3





			@unittest.skipIf(torch_device != '''cuda'''       ,  '''This test requires a GPU'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.dummy_cond_unet
				lowerCamelCase__								=		PNDMScheduler(skip_prk_steps=__lowerCAmelCase							)
				lowerCamelCase__								=		self.dummy_vae
				lowerCamelCase__								=		self.dummy_text_encoder
				lowerCamelCase__								=		XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''							)
				lowerCamelCase__								=		7_7

				lowerCamelCase__								=		self.dummy_image.to(__lowerCAmelCase							)

				# put models in fp16
				lowerCamelCase__								=		unet.half()
				lowerCamelCase__								=		vae.half()
				lowerCamelCase__								=		bert.half()

				# make sure here that pndm scheduler skips prk
				lowerCamelCase__								=		AltDiffusionImgaImgPipeline(
				    unet=__lowerCAmelCase       ,  scheduler=__lowerCAmelCase       ,  vae=__lowerCAmelCase       ,  text_encoder=__lowerCAmelCase       ,  tokenizer=__lowerCAmelCase       ,  safety_checker=__lowerCAmelCase       ,  feature_extractor=self.dummy_extractor       ,  )
				lowerCamelCase__								=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor       ,  do_normalize=__lowerCAmelCase							)
				lowerCamelCase__								=		alt_pipe.to(__lowerCAmelCase							)
				alt_pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

				lowerCamelCase__								=		'''A painting of a squirrel eating a burger'''
				lowerCamelCase__								=		torch.manual_seed(0							)
				lowerCamelCase__								=		alt_pipe(
				    [prompt]       ,  generator=__lowerCAmelCase       ,  num_inference_steps=2       ,  output_type='''np'''       ,  image=__lowerCAmelCase       ,  ).images

				assert image.shape == (1, 3_2, 3_2, 3)





			@unittest.skipIf(torch_device != '''cuda'''       ,  '''This test requires a GPU'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/img2img/sketch-mountains-input.jpg'''							)
				# resize to resolution that is divisible by 8 but not 16 or 32
				lowerCamelCase__								=		init_image.resize((7_6_0, 5_0_4)							)

				lowerCamelCase__								=		'''BAAI/AltDiffusion'''
				lowerCamelCase__								=		AltDiffusionImgaImgPipeline.from_pretrained(
				    __lowerCAmelCase       ,  safety_checker=__lowerCAmelCase       ,  )
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)
				pipe.enable_attention_slicing()

				lowerCamelCase__								=		'''A fantasy landscape, trending on artstation'''

				lowerCamelCase__								=		torch.manual_seed(0							)
				lowerCamelCase__								=		pipe(
				    prompt=__lowerCAmelCase       ,  image=__lowerCAmelCase       ,  strength=0.75       ,  guidance_scale=7.5       ,  generator=__lowerCAmelCase       ,  output_type='''np'''       ,  )
				lowerCamelCase__								=		output.images[0]

				lowerCamelCase__								=		image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

				assert image.shape == (5_0_4, 7_6_0, 3)
				lowerCamelCase__								=		np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]							)

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






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

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		load_image(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/img2img/sketch-mountains-input.jpg'''							)
				lowerCamelCase__								=		init_image.resize((7_6_8, 5_1_2)							)
				lowerCamelCase__								=		load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy'''							)

				lowerCamelCase__								=		'''BAAI/AltDiffusion'''
				lowerCamelCase__								=		AltDiffusionImgaImgPipeline.from_pretrained(
				    __lowerCAmelCase       ,  safety_checker=__lowerCAmelCase       ,  )
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)
				pipe.enable_attention_slicing()

				lowerCamelCase__								=		'''A fantasy landscape, trending on artstation'''

				lowerCamelCase__								=		torch.manual_seed(0							)
				lowerCamelCase__								=		pipe(
				    prompt=__lowerCAmelCase       ,  image=__lowerCAmelCase       ,  strength=0.75       ,  guidance_scale=7.5       ,  generator=__lowerCAmelCase       ,  output_type='''np'''       ,  )
				lowerCamelCase__								=		output.images[0]

				assert image.shape == (5_1_2, 7_6_8, 3)
				# img2img is flaky across GPUs even in fp32, so using MAE here
				assert np.abs(expected_image - image							).max() < 1E-2










def lowerCAmelCase__(__snake_case							)				->      list:







	'''simple docstring'''







	lowerCamelCase__								=		len(__snake_case							)
	for _ in range(__snake_case							):
		for i in range(_ % 2       ,arr_size - 1       ,2							):
			if arr[i + 1] < arr[i]:
				lowerCamelCase__				,	lowerCamelCase__								=		arr[i + 1], arr[i]

	return arr


if __name__ == "__main__":
			_a 						=      list(range(10, 0, -1))
			print(f"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")



