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 __snake_case				(						_UpperCamelCase   ,		_UpperCamelCase   ,		_UpperCamelCase   ,		unittest.TestCase		):
   lowerCAmelCase_														=							StableDiffusionInstructPixaPixPipeline
   lowerCAmelCase_														=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
   lowerCAmelCase_														=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   lowerCAmelCase_														=							IMAGE_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase_														=							IMAGE_TO_IMAGE_IMAGE_PARAMS




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






     torch.manual_seed(0      )
     SCREAMING_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       ,	)
     SCREAMING_SNAKE_CASE__										=      PNDMScheduler(skip_prk_steps=_UpperCAmelCase      )
     torch.manual_seed(0      )
     SCREAMING_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      )
     SCREAMING_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=10_00       ,	)
     SCREAMING_SNAKE_CASE__										=      CLIPTextModel(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )

     SCREAMING_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			:				Optional[Any]       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				str=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      floats_tensor((1, 3, 32, 32)       ,	rng=random.Random(_UpperCAmelCase      )      ).to(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      image.cpu().permute(0       ,	2       ,	3       ,	1      )[0]
     SCREAMING_SNAKE_CASE__										=      Image.fromarray(np.uinta(_UpperCAmelCase      )      ).convert("""RGB"""      )
     if str(_UpperCAmelCase      ).startswith("""mps"""      ):
       SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_UpperCAmelCase      )
     else:
       SCREAMING_SNAKE_CASE__										=      torch.Generator(device=_UpperCAmelCase      ).manual_seed(_UpperCAmelCase      )
     SCREAMING_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			:				str      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      sd_pipe(**_UpperCAmelCase      ).images
     SCREAMING_SNAKE_CASE__										=      image[0, -3:, -3:, -1]
     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]      )

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




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






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

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

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]      )

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




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



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      [inputs['prompt']] * 2

     SCREAMING_SNAKE_CASE__										=      np.array(inputs["""image"""]      ).astype(np.floataa      ) / 2_55.0
     SCREAMING_SNAKE_CASE__										=      torch.from_numpy(_UpperCAmelCase      ).unsqueeze(0      ).to(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      image / 2 + 0.5
     SCREAMING_SNAKE_CASE__										=      image.permute(0       ,	3       ,	1       ,	2      )
     SCREAMING_SNAKE_CASE__										=      image.repeat(2       ,	1       ,	1       ,	1      )

     SCREAMING_SNAKE_CASE__										=      sd_pipe(**_UpperCAmelCase      ).images
     SCREAMING_SNAKE_CASE__										=      image[-1, -3:, -3:, -1]

     assert image.shape == (2, 32, 32, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]      )

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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      'cpu'  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__										=      self.get_dummy_components()
     SCREAMING_SNAKE_CASE__										=      EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85       ,	beta_end=0.0_12       ,	beta_schedule="""scaled_linear"""      )
     SCREAMING_SNAKE_CASE__										=      StableDiffusionInstructPixaPixPipeline(**_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      sd_pipe.to(_UpperCAmelCase      )
     sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase      )

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

     SCREAMING_SNAKE_CASE__										=      [round(_UpperCAmelCase       ,	4      ) for x in image_slice.flatten().tolist()]
     print(""",""".join([str(_UpperCAmelCase      ) for x in slice]      )      )

     assert image.shape == (1, 32, 32, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]      )

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




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      pipe(**self.get_dummy_inputs_by_type(_UpperCAmelCase       ,	input_image_type="""pt"""      )      )[0]

     SCREAMING_SNAKE_CASE__										=      components['vae']
     SCREAMING_SNAKE_CASE__										=      self.get_dummy_inputs_by_type(_UpperCAmelCase       ,	input_image_type="""pt"""      )

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

     SCREAMING_SNAKE_CASE__										=      pipe(**_UpperCAmelCase      )[0]

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







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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int       ,	_lowercase			:				Dict=0      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      torch.manual_seed(_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      load_image(
         """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""      )
     SCREAMING_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			:				List[str]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""       ,	safety_checker=_UpperCAmelCase      )
     pipe.to(_UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_12, 5_12, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]      )

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




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



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""       ,	safety_checker=_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
     pipe.to(_UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_12, 5_12, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]      )

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




   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""       ,	safety_checker=_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      DDIMScheduler.from_config(pipe.scheduler.config      )
     pipe.to(_UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
     pipe.enable_attention_slicing()

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

     assert image.shape == (1, 5_12, 5_12, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]      )

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




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      0

     def callback_fn(_lowercase			:				int       ,	_lowercase			:				int       ,	_lowercase			:				torch.FloatTensor      ) -> None:
       SCREAMING_SNAKE_CASE__										=      True
       nonlocal number_of_steps
       number_of_steps += 1
       if step == 1:
         SCREAMING_SNAKE_CASE__										=      latents.detach().cpu().numpy()
         assert latents.shape == (1, 4, 64, 64)
         SCREAMING_SNAKE_CASE__										=      latents[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE__										=      np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]      )

         assert np.abs(latents_slice.flatten() - expected_slice      ).max() < 5E-2
       elif step == 2:
         SCREAMING_SNAKE_CASE__										=      latents.detach().cpu().numpy()
         assert latents.shape == (1, 4, 64, 64)
         SCREAMING_SNAKE_CASE__										=      latents[0, -3:, -3:, -1]
         SCREAMING_SNAKE_CASE__										=      np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]      )

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

     SCREAMING_SNAKE_CASE__										=      False

     SCREAMING_SNAKE_CASE__										=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""       ,	safety_checker=_UpperCAmelCase       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
     pipe.enable_attention_slicing()

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




   def 	__a						(    self			:				List[str]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      StableDiffusionInstructPixaPixPipeline.from_pretrained(
         """timbrooks/instruct-pix2pix"""       ,	safety_checker=_UpperCAmelCase       ,	torch_dtype=torch.floataa      )
     SCREAMING_SNAKE_CASE__										=      pipe.to(_UpperCAmelCase      )
     pipe.set_progress_bar_config(disable=_UpperCAmelCase      )
     pipe.enable_attention_slicing(1      )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__										=      self.get_inputs()
     SCREAMING_SNAKE_CASE__										=      pipe(**_UpperCAmelCase      )

     SCREAMING_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			:				str      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.get_inputs()
     # resize to resolution that is divisible by 8 but not 16 or 32
     SCREAMING_SNAKE_CASE__										=      inputs['image'].resize((5_04, 5_04)      )

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

     SCREAMING_SNAKE_CASE__										=      pipe(**_UpperCAmelCase      )
     SCREAMING_SNAKE_CASE__										=      output.images[0]

     SCREAMING_SNAKE_CASE__										=      image[2_55:2_58, 3_83:3_86, -1]

     assert image.shape == (5_04, 5_04, 3)
     SCREAMING_SNAKE_CASE__										=      np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]      )

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





'''simple docstring'''

def      __lowerCamelCase     (      )			->      Tuple:
		for n in range(1						,			1000000  ):
				yield n * (n + 1) // 2



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      List[Any]:
		_a     :      Any        =							1
		_a     :      Tuple        =							2
		while i * i <= n:
				_a     :      Tuple        =							0
				while n % i == 0:
						n //= i
						multiplicity += 1
				divisors_count *= multiplicity + 1
				i += 1
		if n > 1:
				divisors_count *= 2
		return divisors_count



def      __lowerCamelCase     (      )			->      str:
		return next(i for i in triangle_number_generator() if count_divisors(lowerCAmelCase_  ) > 500  )


if __name__ == "__main__":
							print(solution())




