




import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, 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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase							,UpperCAmelCase							,UpperCAmelCase							,unittest.TestCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Any          = StableDiffusionInstructPixaPixPipeline
	_UpperCAmelCase	:		Optional[Any]          = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
	_UpperCAmelCase	:		Optional[int]          = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCAmelCase	:		List[str]          = IMAGE_TO_IMAGE_IMAGE_PARAMS
	_UpperCAmelCase	:		Optional[int]          = IMAGE_TO_IMAGE_IMAGE_PARAMS



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				torch.manual_seed(0     )
				_snake_case          =      UNetaDConditionModel(
				    block_out_channels=(32, 64)      ,  layers_per_block=2      ,  sample_size=32      ,  in_channels=8      ,  out_channels=4      ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')      ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')      ,  cross_attention_dim=32      ,  )
				_snake_case          =      PNDMScheduler(skip_prk_steps=lowercase     )
				torch.manual_seed(0     )
				_snake_case          =      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      ,  )
				torch.manual_seed(0     )
				_snake_case          =      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=1_000      ,  )
				_snake_case          =      CLIPTextModel(lowercase     )
				_snake_case          =      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

				_snake_case          =      {
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'safety_checker': None,
				    'feature_extractor': None,
				}
				return components



	def 			A							(  self     :   List[Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   Optional[Any]=0     ):


				'''simple docstring'''


				_snake_case          =      floats_tensor((1, 3, 32, 32)      ,  rng=random.Random(lowercase     )     ).to(lowercase     )
				_snake_case          =      image.cpu().permute(0      ,  2      ,  3      ,  1     )[0]
				_snake_case          =      Image.fromarray(np.uinta(lowercase     )     ).convert('RGB'     )
				if str(lowercase     ).startswith('mps'     ):
							_snake_case          =      torch.manual_seed(lowercase     )
				else:
							_snake_case          =      torch.Generator(device=lowercase     ).manual_seed(lowercase     )
				_snake_case          =      {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 6.0,
				    'image_guidance_scale': 1,
				    'output_type': 'numpy',
				}
				return inputs



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case          =      self.get_dummy_components()
				_snake_case          =      StableDiffusionInstructPixaPixPipeline(**lowercase     )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      self.get_dummy_inputs(lowercase     )
				_snake_case          =      sd_pipe(**lowercase     ).images
				_snake_case          =      image[0, -3:, -3:, -1]
				assert image.shape == (1, 32, 32, 3)
				_snake_case          =      np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case          =      self.get_dummy_components()
				_snake_case          =      StableDiffusionInstructPixaPixPipeline(**lowercase     )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      self.get_dummy_inputs(lowercase     )
				_snake_case          =      'french fries'
				_snake_case          =      sd_pipe(**lowercase      ,  negative_prompt=lowercase     )
				_snake_case          =      output.images
				_snake_case          =      image[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				_snake_case          =      np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3



	def 			A							(  self     :   str     ):


				'''simple docstring'''


				_snake_case          =      'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case          =      self.get_dummy_components()
				_snake_case          =      StableDiffusionInstructPixaPixPipeline(**lowercase     )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      self.get_dummy_inputs(lowercase     )
				_snake_case          =      [inputs['prompt']] * 2

				_snake_case          =      np.array(inputs['image']     ).astype(np.floataa     ) / 255.0
				_snake_case          =      torch.from_numpy(lowercase     ).unsqueeze(0     ).to(lowercase     )
				_snake_case          =      image / 2 + 0.5
				_snake_case          =      image.permute(0      ,  3      ,  1      ,  2     )
				_snake_case          =      image.repeat(2      ,  1      ,  1      ,  1     )

				_snake_case          =      sd_pipe(**lowercase     ).images
				_snake_case          =      image[-1, -3:, -3:, -1]

				assert image.shape == (2, 32, 32, 3)
				_snake_case          =      np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3



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


				'''simple docstring'''


				_snake_case          =      'cpu'  # ensure determinism for the device-dependent torch.Generator
				_snake_case          =      self.get_dummy_components()
				_snake_case          =      EulerAncestralDiscreteScheduler(
				    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule='scaled_linear'     )
				_snake_case          =      StableDiffusionInstructPixaPixPipeline(**lowercase     )
				_snake_case          =      sd_pipe.to(lowercase     )
				sd_pipe.set_progress_bar_config(disable=lowercase     )

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

				_snake_case          =      [round(lowercase      ,  4     ) for x in image_slice.flatten().tolist()]
				print(','.join([str(lowercase     ) for x in slice]     )     )

				assert image.shape == (1, 32, 32, 3)
				_snake_case          =      np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-3



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


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





	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_components()
				_snake_case          =      StableDiffusionInstructPixaPixPipeline(**lowercase     )
				_snake_case          =      VaeImageProcessor(do_resize=lowercase      ,  do_normalize=lowercase     )
				_snake_case          =      pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )

				_snake_case          =      pipe(**self.get_dummy_inputs_by_type(lowercase      ,  input_image_type='pt'     )     )[0]

				_snake_case          =      components['vae']
				_snake_case          =      self.get_dummy_inputs_by_type(lowercase      ,  input_image_type='pt'     )

				for image_param in self.image_latents_params:
							if image_param in inputs.keys():
										_snake_case          =      vae.encode(inputs[image_param]     ).latent_dist.mode()

				_snake_case          =      pipe(**lowercase     )[0]

				_snake_case          =      np.abs(out - out_latents_inputs     ).max()
				self.assertLess(lowercase      ,  1E-4      ,  'passing latents as image input generate different result from passing image'     )




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





	'''simple docstring'''



	def 			A							(  self     :   str     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Any      ,  lowercase     :   Tuple=0     ):


				'''simple docstring'''


				_snake_case          =      torch.manual_seed(lowercase     )
				_snake_case          =      load_image(
				    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'     )
				_snake_case          =      {
				    'prompt': 'turn him into a cyborg',
				    'image': image,
				    'generator': generator,
				    'num_inference_steps': 3,
				    'guidance_scale': 7.5,
				    'image_guidance_scale': 1.0,
				    'output_type': 'numpy',
				}
				return inputs



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


				'''simple docstring'''


				_snake_case          =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    'timbrooks/instruct-pix2pix'      ,  safety_checker=lowercase     )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]     )

				assert np.abs(expected_slice - image_slice     ).max() < 1E-3



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    'timbrooks/instruct-pix2pix'      ,  safety_checker=lowercase     )
				_snake_case          =      LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]     )

				assert np.abs(expected_slice - image_slice     ).max() < 1E-3



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    'timbrooks/instruct-pix2pix'      ,  safety_checker=lowercase     )
				_snake_case          =      DDIMScheduler.from_config(pipe.scheduler.config     )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

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

				assert image.shape == (1, 512, 512, 3)
				_snake_case          =      np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]     )

				assert np.abs(expected_slice - image_slice     ).max() < 1E-3



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      0

				def callback_fn(lowercase     :   int      ,  lowercase     :   int      ,  lowercase     :   torch.FloatTensor     ) -> None:
							_snake_case          =      True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
										_snake_case          =      latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										_snake_case          =      latents[0, -3:, -3:, -1]
										_snake_case          =      np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]     )

										assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2
							elif step == 2:
										_snake_case          =      latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										_snake_case          =      latents[0, -3:, -3:, -1]
										_snake_case          =      np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]     )

										assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5E-2

				_snake_case          =      False

				_snake_case          =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    'timbrooks/instruct-pix2pix'      ,  safety_checker=lowercase      ,  torch_dtype=torch.floataa     )
				_snake_case          =      pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

				_snake_case          =      self.get_inputs()
				pipe(**lowercase      ,  callback=lowercase      ,  callback_steps=1     )
				assert callback_fn.has_been_called
				assert number_of_steps == 3



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				_snake_case          =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    'timbrooks/instruct-pix2pix'      ,  safety_checker=lowercase      ,  torch_dtype=torch.floataa     )
				_snake_case          =      pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing(1     )
				pipe.enable_sequential_cpu_offload()

				_snake_case          =      self.get_inputs()
				_snake_case          =      pipe(**lowercase     )

				_snake_case          =      torch.cuda.max_memory_allocated()
				# make sure that less than 2.2 GB is allocated
				assert mem_bytes < 2.2 * 10**9





	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_inputs()
				# resize to resolution that is divisible by 8 but not 16 or 32
				_snake_case          =      inputs['image'].resize((504, 504)     )

				_snake_case          =      'timbrooks/instruct-pix2pix'
				_snake_case          =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
				    lowercase      ,  safety_checker=lowercase      ,  )
				pipe.to(lowercase     )
				pipe.set_progress_bar_config(disable=lowercase     )
				pipe.enable_attention_slicing()

				_snake_case          =      pipe(**lowercase     )
				_snake_case          =      output.images[0]

				_snake_case          =      image[255:258, 383:386, -1]

				assert image.shape == (504, 504, 3)
				_snake_case          =      np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]     )

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

def 	a_		(	__lowercase       :     int = 50_000_000			)			->  int:
			_snake_case          =      set()
			_snake_case          =      int((limit - 24) ** (1 / 2)			)

			_snake_case          =      set(range(3	, prime_square_limit + 1	, 2			)			)
			primes.add(2			)
			for p in range(3	, prime_square_limit + 1	, 2			):
						if p not in primes:
									continue
						primes.difference_update(set(range(p * p	, prime_square_limit + 1	, __lowercase			)			)			)

			for primea in primes:
						_snake_case          =      primea * primea
						for primea in primes:
									_snake_case          =      primea * primea * primea
									if square + cube >= limit - 16:
												break
									for primea in primes:
												_snake_case          =      primea * primea * primea * primea
												_snake_case          =      square + cube + tetr
												if total >= limit:
															break
												ret.add(__lowercase			)

			return len(__lowercase			)


if __name__ == "__main__":
			print(F'{solution() = }')