




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 						_A (  SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__      ,       unittest.TestCase				):
  _UpperCamelCase       :      Union[str, Any]			=      StableDiffusionInstructPixaPixPipeline
  _UpperCamelCase       :      Optional[int]			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
  _UpperCamelCase       :      Tuple			=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  _UpperCamelCase       :      Optional[int]			=      IMAGE_TO_IMAGE_IMAGE_PARAMS
  _UpperCamelCase       :      int			=      IMAGE_TO_IMAGE_IMAGE_PARAMS






  def  __a      (  self		:     str				)		->   str:






       """simple docstring"""







       torch.manual_seed(0				)
       lowercase :       int       =			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				,						)
       lowercase :       int       =			PNDMScheduler(skip_prk_steps=_A				)
       torch.manual_seed(0				)
       lowercase :       Union[str, Any]       =			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				)
       lowercase :       Union[str, 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=1_000				,						)
       lowercase :       Optional[int]       =			CLIPTextModel(_A				)
       lowercase :       str       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

       lowercase :       Union[str, Any]       =			{
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'safety_checker': None,
           'feature_extractor': None,
       }
       return components






  def  __a      (  self		:     int				,						_A		:     str				,						_A		:     Tuple=0				)		->   Tuple:






       """simple docstring"""







       lowercase :       Optional[int]       =			floats_tensor((1, 3, 32, 32)				,						rng=random.Random(_A				)				).to(_A				)
       lowercase :       Dict       =			image.cpu().permute(0				,						2				,						3				,						1				)[0]
       lowercase :       Any       =			Image.fromarray(np.uinta(_A				)				).convert('''RGB'''				)
       if str(_A				).startswith('''mps'''				):
            lowercase :       str       =			torch.manual_seed(_A				)
       else:
            lowercase :       Optional[Any]       =			torch.Generator(device=_A				).manual_seed(_A				)
       lowercase :       Union[str, Any]       =			{
           '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		:     Tuple				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       str       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase :       int       =			self.get_dummy_components()
       lowercase :       str       =			StableDiffusionInstructPixaPixPipeline(**_A				)
       lowercase :       Union[str, Any]       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

       lowercase :       Union[str, Any]       =			self.get_dummy_inputs(_A				)
       lowercase :       Union[str, Any]       =			sd_pipe(**_A				).images
       lowercase :       str       =			image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       lowercase :       str       =			np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815]				)

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






  def  __a      (  self		:     Optional[Any]				)		->   int:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase :       Union[str, Any]       =			self.get_dummy_components()
       lowercase :       Optional[int]       =			StableDiffusionInstructPixaPixPipeline(**_A				)
       lowercase :       Union[str, Any]       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

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

       assert image.shape == (1, 32, 32, 3)
       lowercase :       Dict       =			np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831]				)

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






  def  __a      (  self		:     Optional[int]				)		->   str:






       """simple docstring"""







       lowercase :       List[Any]       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase :       str       =			self.get_dummy_components()
       lowercase :       Optional[int]       =			StableDiffusionInstructPixaPixPipeline(**_A				)
       lowercase :       str       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

       lowercase :       Any       =			self.get_dummy_inputs(_A				)
       lowercase :       int       =			[inputs['prompt']] * 2

       lowercase :       Dict       =			np.array(inputs['''image''']				).astype(np.floataa				) / 255.0
       lowercase :       Optional[int]       =			torch.from_numpy(_A				).unsqueeze(0				).to(_A				)
       lowercase :       List[Any]       =			image / 2 + 0.5
       lowercase :       List[str]       =			image.permute(0				,						3				,						1				,						2				)
       lowercase :       List[str]       =			image.repeat(2				,						1				,						1				,						1				)

       lowercase :       Dict       =			sd_pipe(**_A				).images
       lowercase :       Union[str, Any]       =			image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       lowercase :       Optional[int]       =			np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579]				)

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






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






       """simple docstring"""







       lowercase :       str       =			'cpu'  # ensure determinism for the device-dependent torch.Generator
       lowercase :       Optional[int]       =			self.get_dummy_components()
       lowercase :       str       =			EulerAncestralDiscreteScheduler(
           beta_start=0.00_085				,						beta_end=0.012				,						beta_schedule='''scaled_linear'''				)
       lowercase :       Tuple       =			StableDiffusionInstructPixaPixPipeline(**_A				)
       lowercase :       str       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

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

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

       assert image.shape == (1, 32, 32, 3)
       lowercase :       List[str]       =			np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986]				)

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






  def  __a      (  self		:     List[str]				)		->   Dict:






       """simple docstring"""







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






  def  __a      (  self		:     int				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       List[Any]       =			self.get_dummy_components()
       lowercase :       Optional[Any]       =			StableDiffusionInstructPixaPixPipeline(**_A				)
       lowercase :       List[str]       =			VaeImageProcessor(do_resize=_A				,						do_normalize=_A				)
       lowercase :       int       =			pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)

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

       lowercase :       Optional[Any]       =			components['vae']
       lowercase :       str       =			self.get_dummy_inputs_by_type(_A				,						input_image_type='''pt'''				)

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

       lowercase :       Optional[int]       =			pipe(**_A				)[0]

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


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






  def  __a      (  self		:     List[str]				)		->   Dict:






       """simple docstring"""







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






  def  __a      (  self		:     List[str]				,						_A		:     int=0				)		->   str:






       """simple docstring"""







       lowercase :       Dict       =			torch.manual_seed(_A				)
       lowercase :       Optional[Any]       =			load_image(
           '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''				)
       lowercase :       int       =			{
           '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		:     int				)		->   str:






       """simple docstring"""







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

       lowercase :       Optional[Any]       =			self.get_inputs()
       lowercase :       List[Any]       =			pipe(**_A				).images
       lowercase :       Tuple       =			image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowercase :       List[str]       =			np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555]				)

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






  def  __a      (  self		:     Optional[Any]				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       Any       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''				,						safety_checker=_A				)
       lowercase :       List[Any]       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config				)
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing()

       lowercase :       List[Any]       =			self.get_inputs()
       lowercase :       Tuple       =			pipe(**_A				).images
       lowercase :       Dict       =			image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowercase :       str       =			np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301]				)

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






  def  __a      (  self		:     Optional[Any]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       int       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''				,						safety_checker=_A				)
       lowercase :       List[Any]       =			DDIMScheduler.from_config(pipe.scheduler.config				)
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing()

       lowercase :       Any       =			self.get_inputs()
       lowercase :       Union[str, Any]       =			pipe(**_A				).images
       lowercase :       List[Any]       =			image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 512, 3)
       lowercase :       int       =			np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753]				)

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






  def  __a      (  self		:     Tuple				)		->   Dict:






       """simple docstring"""







       lowercase :       Dict       =			0

       def callback_fn(_A		:     int				,						_A		:     int				,						_A		:     torch.FloatTensor				) -> None:
            lowercase :       Optional[Any]       =			True
            nonlocal number_of_steps
            number_of_steps += 1
            if step == 1:
                 lowercase :       str       =			latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 lowercase :       Union[str, Any]       =			latents[0, -3:, -3:, -1]
                 lowercase :       List[str]       =			np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983]				)

                 assert np.abs(latents_slice.flatten() - expected_slice				).max() < 5E-2
            elif step == 2:
                 lowercase :       Optional[Any]       =			latents.detach().cpu().numpy()
                 assert latents.shape == (1, 4, 64, 64)
                 lowercase :       Union[str, Any]       =			latents[0, -3:, -3:, -1]
                 lowercase :       str       =			np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115]				)

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

       lowercase :       Union[str, Any]       =			False

       lowercase :       List[str]       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''				,						safety_checker=_A				,						torch_dtype=torch.floataa				)
       lowercase :       Optional[int]       =			pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing()

       lowercase :       Union[str, Any]       =			self.get_inputs()
       pipe(**_A				,						callback=_A				,						callback_steps=1				)
       assert callback_fn.has_been_called
       assert number_of_steps == 3






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






       """simple docstring"""







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

       lowercase :       Optional[int]       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
           '''timbrooks/instruct-pix2pix'''				,						safety_checker=_A				,						torch_dtype=torch.floataa				)
       lowercase :       Dict       =			pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing(1				)
       pipe.enable_sequential_cpu_offload()

       lowercase :       Dict       =			self.get_inputs()
       lowercase :       List[Any]       =			pipe(**_A				)

       lowercase :       Any       =			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		:     Union[str, Any]				)		->   Tuple:






       """simple docstring"""







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

       lowercase :       Union[str, Any]       =			'timbrooks/instruct-pix2pix'
       lowercase :       Any       =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
           _A				,						safety_checker=_A				,						)
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing()

       lowercase :       str       =			pipe(**_A				)
       lowercase :       int       =			output.images[0]

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

       assert image.shape == (504, 504, 3)
       lowercase :       Union[str, Any]       =			np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590]				)

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





def  snake_case(    __magic_name__			,		__magic_name__   )			->							bool:





     '''simple docstring'''


     lowercase :       List[Any]       =			len(__magic_name__   )
     lowercase :       str       =			[[False] * (required_sum + 1) for _ in range(arr_len + 1   )]

     # for each arr value, a sum of zero(0) can be formed by not taking any element
     # hence True/1
     for i in range(arr_len + 1   ):
          lowercase :       List[str]       =			True

     # sum is not zero and set is empty then false
     for i in range(1			,		required_sum + 1   ):
          lowercase :       str       =			False

     for i in range(1			,		arr_len + 1   ):
          for j in range(1			,		required_sum + 1   ):
               if arr[i - 1] > j:
                    lowercase :       Optional[Any]       =			subset[i - 1][j]
               if arr[i - 1] <= j:
                    lowercase :       Dict       =			subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

     return subset[arr_len][required_sum]


if __name__ == "__main__":
 import doctest

 doctest.testmod()