




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(						lowerCamelCase_							,      lowerCamelCase_							,      lowerCamelCase_							,      unittest.TestCase   ):



 '''simple docstring'''


 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 				a__ (				self					:  Any  )   ->	Optional[int]:

       """simple docstring"""






       torch.manual_seed(0  )
       lowerCamelCase_		     =    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						,							)
       lowerCamelCase_		     =    PNDMScheduler(skip_prk_steps=lowerCAmelCase__  )
       torch.manual_seed(0  )
       lowerCamelCase_		     =    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  )
       lowerCamelCase_		     =    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						,							)
       lowerCamelCase_		     =    CLIPTextModel(lowerCAmelCase__  )
       lowerCamelCase_		     =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

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

 def 				a__ (				self					:  List[str]						,							A_					:  Optional[Any]						,							A_					:  Tuple=0  )   ->	List[str]:

       """simple docstring"""






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

       """simple docstring"""






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

       lowerCamelCase_		     =    self.get_dummy_inputs(lowerCAmelCase__  )
       lowerCamelCase_		     =    sd_pipe(**lowerCAmelCase__  ).images
       lowerCamelCase_		     =    image[0, -3:, -3:, -1]
       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_		     =    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					:  Dict  )   ->	int:

       """simple docstring"""






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

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

       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_		     =    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					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






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

       lowerCamelCase_		     =    self.get_dummy_inputs(lowerCAmelCase__  )
       lowerCamelCase_		     =    [inputs['prompt']] * 2

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

       lowerCamelCase_		     =    sd_pipe(**lowerCAmelCase__  ).images
       lowerCamelCase_		     =    image[-1, -3:, -3:, -1]

       assert image.shape == (2, 32, 32, 3)
       lowerCamelCase_		     =    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]  )   ->	List[str]:

       """simple docstring"""






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

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

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

       assert image.shape == (1, 32, 32, 3)
       lowerCamelCase_		     =    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					:  Union[str, Any]  )   ->	Dict:

       """simple docstring"""






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





 def 				a__ (				self					:  str  )   ->	List[str]:

       """simple docstring"""






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

       lowerCamelCase_		     =    pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase__						,							input_image_type='pt'  )  )[0]

       lowerCamelCase_		     =    components['vae']
       lowerCamelCase_		     =    self.get_dummy_inputs_by_type(lowerCAmelCase__						,							input_image_type='pt'  )

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

       lowerCamelCase_		     =    pipe(**lowerCAmelCase__  )[0]

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







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



 '''simple docstring'''


 def 				a__ (				self					:  List[str]  )   ->	str:

       """simple docstring"""






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

 def 				a__ (				self					:  Optional[int]						,							A_					:  List[Any]=0  )   ->	Any:

       """simple docstring"""






       lowerCamelCase_		     =    torch.manual_seed(lowerCAmelCase__  )
       lowerCamelCase_		     =    load_image(
           'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'  )
       lowerCamelCase_		     =    {
           '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]  )   ->	Union[str, Any]:

       """simple docstring"""






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

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    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  )   ->	Optional[Any]:

       """simple docstring"""






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

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    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					:  List[str]  )   ->	Optional[Any]:

       """simple docstring"""






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

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

       assert image.shape == (1, 512, 512, 3)
       lowerCamelCase_		     =    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					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






       lowerCamelCase_		     =    0

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

       lowerCamelCase_		     =    False

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

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

 def 				a__ (				self					:  Dict  )   ->	Optional[int]:

       """simple docstring"""






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

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

       lowerCamelCase_		     =    self.get_inputs()
       lowerCamelCase_		     =    pipe(**lowerCAmelCase__  )

       lowerCamelCase_		     =    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					:  Any  )   ->	Dict:

       """simple docstring"""






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

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

       lowerCamelCase_		     =    pipe(**lowerCAmelCase__  )
       lowerCamelCase_		     =    output.images[0]

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

       assert image.shape == (504, 504, 3)
       lowerCamelCase_		     =    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







"""simple docstring"""




def     lowercase			(SCREAMING_SNAKE_CASE_		:				int  )   ->						str:
						SCREAMING_SNAKE_CASE													=					int(SCREAMING_SNAKE_CASE_  )
						if decimal in (0, 1):  # Exit cases for the recursion
												return str(SCREAMING_SNAKE_CASE_  )
						SCREAMING_SNAKE_CASE							,				SCREAMING_SNAKE_CASE													=					divmod(SCREAMING_SNAKE_CASE_ ,			2  )
						return binary_recursive(SCREAMING_SNAKE_CASE_  ) + str(SCREAMING_SNAKE_CASE_  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				str  )   ->						str:
						SCREAMING_SNAKE_CASE													=					str(SCREAMING_SNAKE_CASE_  ).strip()
						if not number:
												raise ValueError('No input value was provided'  )
						SCREAMING_SNAKE_CASE													=					'-' if number.startswith('-'  ) else ''
						SCREAMING_SNAKE_CASE													=					number.lstrip('-'  )
						if not number.isnumeric():
												raise ValueError('Input value is not an integer'  )
						return F'{negative}0b{binary_recursive(int(SCREAMING_SNAKE_CASE_  )  )}'


if __name__ == "__main__":
	from doctest import testmod

	testmod()


