


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__	(     __lowerCamelCase					,				__lowerCamelCase					,				__lowerCamelCase					,				unittest.TestCase):
     A:		Any			=      StableDiffusionInstructPixaPixPipeline
     A:		List[Any]			=      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
     A:		str			=      TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     A:		int			=      IMAGE_TO_IMAGE_IMAGE_PARAMS
     A:		int			=      IMAGE_TO_IMAGE_IMAGE_PARAMS


     def      UpperCAmelCase__	(      self							:     int			)  ->      Any:





      '''simple docstring'''






      torch.manual_seed(0			)
      UpperCamelCase__					:    Dict          =   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							,	)
      UpperCamelCase__					:    List[Any]          =   PNDMScheduler(skip_prk_steps=UpperCamelCase_			)
      torch.manual_seed(0			)
      UpperCamelCase__					:    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			)
      UpperCamelCase__					:    Optional[int]          =   CLIPTextConfig(
          bos_token_id=0							,	eos_token_id=2							,	hidden_size=32							,	intermediate_size=37							,	layer_norm_eps=1E-0_5							,	num_attention_heads=4							,	num_hidden_layers=5							,	pad_token_id=1							,	vocab_size=1000							,	)
      UpperCamelCase__					:    Union[str, Any]          =   CLIPTextModel(UpperCamelCase_			)
      UpperCamelCase__					:    Optional[Any]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      UpperCamelCase__					:    Optional[Any]          =   {
          '''unet''': unet,
          '''scheduler''': scheduler,
          '''vae''': vae,
          '''text_encoder''': text_encoder,
          '''tokenizer''': tokenizer,
          '''safety_checker''': None,
          '''feature_extractor''': None,
      }
      return components


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[str]=0			)  ->      Any:





      '''simple docstring'''






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





      '''simple docstring'''






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

      UpperCamelCase__					:    List[str]          =   self.get_dummy_inputs(UpperCamelCase_			)
      UpperCamelCase__					:    Tuple          =   sd_pipe(**UpperCamelCase_			).images
      UpperCamelCase__					:    Any          =   image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      UpperCamelCase__					:    List[str]          =   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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      List[str]:





      '''simple docstring'''






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

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

      assert image.shape == (1, 32, 32, 3)
      UpperCamelCase__					:    Any          =   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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Tuple:





      '''simple docstring'''






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

      UpperCamelCase__					:    Optional[Any]          =   self.get_dummy_inputs(UpperCamelCase_			)
      UpperCamelCase__					:    Optional[Any]          =   [inputs['''prompt''']] * 2

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

      UpperCamelCase__					:    List[Any]          =   sd_pipe(**UpperCamelCase_			).images
      UpperCamelCase__					:    Optional[int]          =   image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      UpperCamelCase__					:    Tuple          =   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      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
      UpperCamelCase__					:    Dict          =   self.get_dummy_components()
      UpperCamelCase__					:    int          =   EulerAncestralDiscreteScheduler(
          beta_start=0.0_0085							,	beta_end=0.012							,	beta_schedule='''scaled_linear'''			)
      UpperCamelCase__					:    Tuple          =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_			)
      UpperCamelCase__					:    int          =   sd_pipe.to(UpperCamelCase_			)
      sd_pipe.set_progress_bar_config(disable=UpperCamelCase_			)

      UpperCamelCase__					:    str          =   self.get_dummy_inputs(UpperCamelCase_			)
      UpperCamelCase__					:    Union[str, Any]          =   sd_pipe(**UpperCamelCase_			).images
      UpperCamelCase__					:    List[str]          =   image[0, -3:, -3:, -1]

      UpperCamelCase__					:    Optional[int]          =   [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)
      UpperCamelCase__					:    Tuple          =   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      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[Any]:





      '''simple docstring'''






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


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





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.get_dummy_components()
      UpperCamelCase__					:    Dict          =   StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_			)
      UpperCamelCase__					:    str          =   VaeImageProcessor(do_resize=UpperCamelCase_							,	do_normalize=UpperCamelCase_			)
      UpperCamelCase__					:    int          =   pipe.to(UpperCamelCase_			)
      pipe.set_progress_bar_config(disable=UpperCamelCase_			)

      UpperCamelCase__					:    Optional[Any]          =   pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_							,	input_image_type='''pt'''			)			)[0]

      UpperCamelCase__					:    Union[str, Any]          =   components['''vae''']
      UpperCamelCase__					:    int          =   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():
        UpperCamelCase__					:    Tuple          =   vae.encode(inputs[image_param]			).latent_dist.mode()

      UpperCamelCase__					:    Union[str, Any]          =   pipe(**UpperCamelCase_			)[0]

      UpperCamelCase__					:    Optional[Any]          =   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       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     str=0			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   torch.manual_seed(UpperCamelCase_			)
      UpperCamelCase__					:    Dict          =   load_image(
          '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''			)
      UpperCamelCase__					:    Union[str, Any]          =   {
          '''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      UpperCAmelCase__	(      self							:     str			)  ->      int:





      '''simple docstring'''






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

      UpperCamelCase__					:    Any          =   self.get_inputs()
      UpperCamelCase__					:    Any          =   pipe(**UpperCamelCase_			).images
      UpperCamelCase__					:    Tuple          =   image[0, -3:, -3:, -1].flatten()

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





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
          '''timbrooks/instruct-pix2pix'''							,	safety_checker=UpperCamelCase_			)
      UpperCamelCase__					:    str          =   LMSDiscreteScheduler.from_config(pipe.scheduler.config			)
      pipe.to(UpperCamelCase_			)
      pipe.set_progress_bar_config(disable=UpperCamelCase_			)
      pipe.enable_attention_slicing()

      UpperCamelCase__					:    Union[str, Any]          =   self.get_inputs()
      UpperCamelCase__					:    Tuple          =   pipe(**UpperCamelCase_			).images
      UpperCamelCase__					:    Optional[int]          =   image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      UpperCamelCase__					:    int          =   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      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      Tuple:





      '''simple docstring'''






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

      UpperCamelCase__					:    Optional[int]          =   self.get_inputs()
      UpperCamelCase__					:    Optional[int]          =   pipe(**UpperCamelCase_			).images
      UpperCamelCase__					:    Union[str, Any]          =   image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 512, 512, 3)
      UpperCamelCase__					:    str          =   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      UpperCAmelCase__	(      self							:     List[str]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   0

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

      UpperCamelCase__					:    List[str]          =   False

      UpperCamelCase__					:    List[Any]          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
          '''timbrooks/instruct-pix2pix'''							,	safety_checker=UpperCamelCase_							,	torch_dtype=torch.floataa			)
      UpperCamelCase__					:    Tuple          =   pipe.to(UpperCamelCase_			)
      pipe.set_progress_bar_config(disable=UpperCamelCase_			)
      pipe.enable_attention_slicing()

      UpperCamelCase__					:    List[str]          =   self.get_inputs()
      pipe(**UpperCamelCase_							,	callback=UpperCamelCase_							,	callback_steps=1			)
      assert callback_fn.has_been_called
      assert number_of_steps == 3


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      List[Any]:





      '''simple docstring'''






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

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

      UpperCamelCase__					:    Optional[int]          =   self.get_inputs()
      UpperCamelCase__					:    Any          =   pipe(**UpperCamelCase_			)

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


     def      UpperCAmelCase__	(      self							:     str			)  ->      Dict:





      '''simple docstring'''






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

      UpperCamelCase__					:    Optional[int]          =   '''timbrooks/instruct-pix2pix'''
      UpperCamelCase__					:    Any          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
          UpperCamelCase_							,	safety_checker=UpperCamelCase_							,	)
      pipe.to(UpperCamelCase_			)
      pipe.set_progress_bar_config(disable=UpperCamelCase_			)
      pipe.enable_attention_slicing()

      UpperCamelCase__					:    Optional[int]          =   pipe(**UpperCamelCase_			)
      UpperCamelCase__					:    int          =   output.images[0]

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

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






from typing import TYPE_CHECKING

from ..utils import _LazyModule


__UpperCamelCase       :       int						       =				{
    "config": [
        "EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
        "OnnxConfig",
        "OnnxConfigWithPast",
        "OnnxSeq2SeqConfigWithPast",
        "PatchingSpec",
    ],
    "convert": ["export", "validate_model_outputs"],
    "features": ["FeaturesManager"],
    "utils": ["ParameterFormat", "compute_serialized_parameters_size"],
}


if TYPE_CHECKING:
       from .config import (
           EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
           OnnxConfig,
           OnnxConfigWithPast,
           OnnxSeqaSeqConfigWithPast,
           PatchingSpec,
       )
       from .convert import export, validate_model_outputs
       from .features import FeaturesManager
       from .utils import ParameterFormat, compute_serialized_parameters_size

else:
       import sys

       __UpperCamelCase       :       List[str]						       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


