

"""simple docstring"""




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 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		StableDiffusionInstructPixaPixPipeline
       UpperCamelCase_   =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
       UpperCamelCase_   =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase_   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase_   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         torch.manual_seed(0       )
         _lowercase:			List[Any]       =     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:			Union[str, Any]       =     PNDMScheduler(skip_prk_steps=A_       )
         torch.manual_seed(0       )
         _lowercase:			List[str]       =     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:			Optional[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 ,				)
         _lowercase:			Optional[Any]       =     CLIPTextModel(A_       )
         _lowercase:			Optional[Any]       =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

         _lowercase:			Dict       =     {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
       def 							lowercase_							(     self ,				A_ ,				A_=0       )     ->	str:





         """simple docstring"""




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





         """simple docstring"""




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

         _lowercase:			Optional[int]       =     self.get_dummy_inputs(A_       )
         _lowercase:			str       =     sd_pipe(**A_       ).images
         _lowercase:			str       =     image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         _lowercase:			List[Any]       =     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 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowercase:			List[str]       =     self.get_dummy_components()
         _lowercase:			List[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:			List[str]       =     '''french fries'''
         _lowercase:			Optional[Any]       =     sd_pipe(**A_ ,				negative_prompt=A_       )
         _lowercase:			Dict       =     output.images
         _lowercase:			List[str]       =     image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _lowercase:			Tuple       =     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 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




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

         _lowercase:			List[Any]       =     self.get_dummy_inputs(A_       )
         _lowercase:			Optional[int]       =     [inputs['''prompt''']] * 2

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

         _lowercase:			Union[str, Any]       =     sd_pipe(**A_       ).images
         _lowercase:			Any       =     image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         _lowercase:			Dict       =     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 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowercase:			str       =     self.get_dummy_components()
         _lowercase:			Optional[int]       =     EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85 ,				beta_end=0.0_12 ,				beta_schedule='''scaled_linear'''       )
         _lowercase:			Optional[int]       =     StableDiffusionInstructPixaPixPipeline(**A_       )
         _lowercase:			Optional[int]       =     sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			List[Any]       =     self.get_dummy_inputs(A_       )
         _lowercase:			Optional[Any]       =     sd_pipe(**A_       ).images
         _lowercase:			List[Any]       =     image[0, -3:, -3:, -1]

         _lowercase:			List[str]       =     [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:			str       =     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 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




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



       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




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

         _lowercase:			int       =     pipe(**self.get_dummy_inputs_by_type(A_ ,				input_image_type='''pt'''       )       )[0]

         _lowercase:			Dict       =     components['''vae''']
         _lowercase:			Optional[Any]       =     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:			int       =     pipe(**A_       )[0]

         _lowercase:			Union[str, Any]       =     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 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
       def 							lowercase_							(     self ,				A_=0       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			List[str]       =     torch.manual_seed(A_       )
         _lowercase:			Optional[int]       =     load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''       )
         _lowercase:			Dict       =     {
             '''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 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Any       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			str       =     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:			Any       =     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 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Any       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_       )
         _lowercase:			Optional[int]       =     LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			Union[str, Any]       =     self.get_inputs()
         _lowercase:			Dict       =     pipe(**A_       ).images
         _lowercase:			Any       =     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			int       =     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 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Dict       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_       )
         _lowercase:			Optional[int]       =     DDIMScheduler.from_config(pipe.scheduler.config       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			Optional[Any]       =     self.get_inputs()
         _lowercase:			Any       =     pipe(**A_       ).images
         _lowercase:			List[str]       =     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			str       =     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 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[Any]       =     0

         def callback_fn(A_ ,				A_ ,				A_       ) -> None:
           _lowercase:			Dict       =     True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
             _lowercase:			List[Any]       =     latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             _lowercase:			Any       =     latents[0, -3:, -3:, -1]
             _lowercase:			Optional[int]       =     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:
             _lowercase:			Dict       =     latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             _lowercase:			Any       =     latents[0, -3:, -3:, -1]
             _lowercase:			List[str]       =     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

         _lowercase:			Dict       =     False

         _lowercase:			Union[str, Any]       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_ ,				torch_dtype=torch.floataa       )
         _lowercase:			List[str]       =     pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			List[Any]       =     self.get_inputs()
         pipe(**A_ ,				callback=A_ ,				callback_steps=1       )
         assert callback_fn.has_been_called
         assert number_of_steps == 3
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




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

         _lowercase:			List[str]       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_ ,				torch_dtype=torch.floataa       )
         _lowercase:			Optional[Any]       =     pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         _lowercase:			Optional[int]       =     self.get_inputs()
         _lowercase:			Dict       =     pipe(**A_       )

         _lowercase:			Optional[int]       =     torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




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

         _lowercase:			Tuple       =     '''timbrooks/instruct-pix2pix'''
         _lowercase:			int       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             A_ ,				safety_checker=A_ ,				)
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			Union[str, Any]       =     pipe(**A_       )
         _lowercase:			Dict       =     output.images[0]

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

         assert image.shape == (504, 504, 3)
         _lowercase:			Tuple       =     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"""




from __future__ import annotations

import numpy as np




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  _lowercase ,				_lowercase:			List[str]       =     np.shape(_UpperCamelCase						)
  if rows != columns:
    _lowercase:			int       =     (
        '''\'table\' has to be of square shaped array but got a '''
        f'''{rows}x{columns} array:\n{table}'''
    )
    raise ValueError(_UpperCamelCase						)

  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  _lowercase:			Tuple       =     np.zeros((rows, columns)						)
  for i in range(_UpperCamelCase						):
    for j in range(_UpperCamelCase						):
      _lowercase:			List[Any]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      if upper[j][j] == 0:
        raise ArithmeticError('''No LU decomposition exists'''						)
      _lowercase:			Any       =     (table[i][j] - total) / upper[j][j]
    _lowercase:			Any       =     1
    for j in range(_UpperCamelCase ,				_UpperCamelCase						):
      _lowercase:			Optional[int]       =     sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase						)						)
      _lowercase:			List[str]       =     table[i][j] - total
  return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()
