



'''simple docstring'''
import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						int												=  [tensor.shape for tensor in tensor_list]
						return all(shape == shapes[0] for shape in shapes[1:]				)






class 				lowercase__      (				lowercase		,   lowercase		,   lowercase		,   unittest.TestCase  ):
	lowercase__    							=     StableDiffusionLatentUpscalePipeline
	lowercase__    							=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    """height""",
	    """width""",
	    """cross_attention_kwargs""",
	    """negative_prompt_embeds""",
	    """prompt_embeds""",
	}
	lowercase__    							=     PipelineTesterMixin.required_optional_params - {"""num_images_per_prompt"""}
	lowercase__    							=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	lowercase__    							=     frozenset(
	    []  )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	lowercase__    							=     frozenset([]  )

	lowercase__    							=     True





	@property
	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Dict												=  1
							_UpperCamelCase   :						str												=  4
							_UpperCamelCase   :						Optional[int]												=  (16, 16)

							_UpperCamelCase   :						Optional[int]												=  floats_tensor((batch_size, num_channels) + sizes					,rng=random.Random(0					)					).to(lowerCamelCase__					)
							return image





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							torch.manual_seed(0					)
							_UpperCamelCase   :						List[str]												=  UNetaDConditionModel(
							    act_fn='gelu'					,attention_head_dim=8					,norm_num_groups=lowerCamelCase__					,block_out_channels=[32, 32, 64, 64]					,time_cond_proj_dim=160					,conv_in_kernel=1					,conv_out_kernel=1					,cross_attention_dim=32					,down_block_types=(
							        'KDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							        'KCrossAttnDownBlock2D',
							    )					,in_channels=8					,mid_block_type=lowerCamelCase__					,only_cross_attention=lowerCamelCase__					,out_channels=5					,resnet_time_scale_shift='scale_shift'					,time_embedding_type='fourier'					,timestep_post_act='gelu'					,up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')					,)
							_UpperCamelCase   :						Dict												=  AutoencoderKL(
							    block_out_channels=[32, 32, 64, 64]					,in_channels=3					,out_channels=3					,down_block_types=[
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							        'DownEncoderBlock2D',
							    ]					,up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']					,latent_channels=4					,)
							_UpperCamelCase   :						List[Any]												=  EulerDiscreteScheduler(prediction_type='sample'					)
							_UpperCamelCase   :						List[Any]												=  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='quick_gelu'					,projection_dim=512					,)
							_UpperCamelCase   :						int												=  CLIPTextModel(lowerCamelCase__					)
							_UpperCamelCase   :						str												=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

							_UpperCamelCase   :						str												=  {
							    'unet': model.eval(),
							    'vae': vae.eval(),
							    'scheduler': scheduler,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							}

							return components





	def       UpperCamelCase_    (					self   :							int					,lowerCamelCase__   :							str					,lowerCamelCase__   :							Dict=0					):







							'''simple docstring'''
							if str(lowerCamelCase__					).startswith('mps'					):
													_UpperCamelCase   :						int												=  torch.manual_seed(lowerCamelCase__					)
							else:
													_UpperCamelCase   :						Union[str, Any]												=  torch.Generator(device=lowerCamelCase__					).manual_seed(lowerCamelCase__					)
							_UpperCamelCase   :						List[str]												=  {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'image': self.dummy_image.cpu(),
							    'generator': generator,
							    'num_inference_steps': 2,
							    'output_type': 'numpy',
							}
							return inputs





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							_UpperCamelCase   :						Any												=  'cpu'

							_UpperCamelCase   :						Any												=  self.get_dummy_components()
							_UpperCamelCase   :						Any												=  self.pipeline_class(**lowerCamelCase__					)
							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						int												=  pipe(**lowerCamelCase__					).images
							_UpperCamelCase   :						str												=  image[0, -3:, -3:, -1]

							self.assertEqual(image.shape					,(1, 256, 256, 3)					)
							_UpperCamelCase   :						List[str]												=  np.array(
							    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]					)
							_UpperCamelCase   :						Union[str, Any]												=  np.abs(image_slice.flatten() - expected_slice					).max()
							self.assertLessEqual(lowerCamelCase__					,1E-3					)





	def       UpperCamelCase_    (					self   :							Tuple					):







							'''simple docstring'''
							super().test_attention_slicing_forward_pass(expected_max_diff=7E-3					)





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							super().test_cpu_offload_forward_pass(expected_max_diff=3E-3					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3					)





	def       UpperCamelCase_    (					self   :							str					):







							'''simple docstring'''
							super().test_inference_batch_single_identical(expected_max_diff=7E-3					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3					)





	def       UpperCamelCase_    (					self   :							List[Any]					):







							'''simple docstring'''
							super().test_save_load_local(expected_max_difference=3E-3					)





	def       UpperCamelCase_    (					self   :							Optional[Any]					):







							'''simple docstring'''
							super().test_save_load_optional_components(expected_max_difference=3E-3					)





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						int												=  [
							    'DDIMScheduler',
							    'DDPMScheduler',
							    'PNDMScheduler',
							    'HeunDiscreteScheduler',
							    'EulerAncestralDiscreteScheduler',
							    'KDPM2DiscreteScheduler',
							    'KDPM2AncestralDiscreteScheduler',
							    'DPMSolverSDEScheduler',
							]
							_UpperCamelCase   :						str												=  self.get_dummy_components()
							_UpperCamelCase   :						int												=  self.pipeline_class(**lowerCamelCase__					)

							# make sure that PNDM does not need warm-up
							pipe.scheduler.register_to_config(skip_prk_steps=lowerCamelCase__					)

							pipe.to(lowerCamelCase__					)
							pipe.set_progress_bar_config(disable=lowerCamelCase__					)
							_UpperCamelCase   :						Any												=  self.get_dummy_inputs(lowerCamelCase__					)
							_UpperCamelCase   :						Tuple												=  2

							_UpperCamelCase   :						Optional[int]												=  []
							for scheduler_enum in KarrasDiffusionSchedulers:
													if scheduler_enum.name in skip_schedulers:
																			# no sigma schedulers are not supported
																			# no schedulers
																			continue

													_UpperCamelCase   :						Dict												=  getattr(lowerCamelCase__					,scheduler_enum.name					)
													_UpperCamelCase   :						List[Any]												=  scheduler_cls.from_config(pipe.scheduler.config					)
													_UpperCamelCase   :						Optional[Any]												=  pipe(**lowerCamelCase__					)[0]
													outputs.append(lowerCamelCase__					)

							assert check_same_shape(lowerCamelCase__					)






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





	def       UpperCamelCase_    (					self   :							Any					):







							'''simple docstring'''
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





	def       UpperCamelCase_    (					self   :							Union[str, Any]					):







							'''simple docstring'''
							_UpperCamelCase   :						str												=  torch.manual_seed(33					)

							_UpperCamelCase   :						Optional[int]												=  StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'					,torch_dtype=torch.floataa					)
							pipe.to('cuda'					)

							_UpperCamelCase   :						int												=  StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'					,torch_dtype=torch.floataa					)
							upscaler.to('cuda'					)

							_UpperCamelCase   :						str												=  'a photo of an astronaut high resolution, unreal engine, ultra realistic'

							_UpperCamelCase   :						str												=  pipe(lowerCamelCase__					,generator=lowerCamelCase__					,output_type='latent'					).images

							_UpperCamelCase   :						Any												=  upscaler(
							    prompt=lowerCamelCase__					,image=lowerCamelCase__					,num_inference_steps=20					,guidance_scale=0					,generator=lowerCamelCase__					,output_type='np'					,).images[0]

							_UpperCamelCase   :						Optional[int]												=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'					)
							assert np.abs((expected_image - image).mean()					) < 5E-2





	def       UpperCamelCase_    (					self   :							List[str]					):







							'''simple docstring'''
							_UpperCamelCase   :						Union[str, Any]												=  torch.manual_seed(33					)

							_UpperCamelCase   :						str												=  StableDiffusionLatentUpscalePipeline.from_pretrained(
							    'stabilityai/sd-x2-latent-upscaler'					,torch_dtype=torch.floataa					)
							upscaler.to('cuda'					)

							_UpperCamelCase   :						Any												=  'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

							_UpperCamelCase   :						Dict												=  load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'					)

							_UpperCamelCase   :						List[str]												=  upscaler(
							    prompt=lowerCamelCase__					,image=lowerCamelCase__					,num_inference_steps=20					,guidance_scale=0					,generator=lowerCamelCase__					,output_type='np'					,).images[0]

							_UpperCamelCase   :						Dict												=  load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'					)
							assert np.abs((expected_image - image).max()					) < 5E-2







'''simple docstring'''
from math import ceil






def A__       (	UpperCAmelCase_ = 1_0_0_1				):
						_UpperCamelCase   :						int												=  1

						for i in range(1    ,    int(ceil(n / 2.0				)				)				):
												_UpperCamelCase   :						Dict												=  2 * i + 1
												_UpperCamelCase   :						Tuple												=  2 * i
												_UpperCamelCase   :						Any												=  total + 4 * odd**2 - 6 * even

						return total


if __name__ == "__main__":
					import sys

					if len(sys.argv) == 1:
										print(solution())
					else:
										try:
															snake_case_					:      Tuple    			=    int(sys.argv[1])
															print(solution(n))
										except ValueError:
															print('Invalid entry - please enter a number')


