






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_		(    _a			, _a			, _a			, unittest.TestCase       ):
  lowercase  	=   StableDiffusionInstructPixaPixPipeline
  lowercase  	=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
  lowercase  	=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  lowercase  	=   IMAGE_TO_IMAGE_IMAGE_PARAMS
  lowercase  	=   IMAGE_TO_IMAGE_IMAGE_PARAMS

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



        """simple docstring"""






        torch.manual_seed(0   )
        UpperCamelCase       						=      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       						=      PNDMScheduler(skip_prk_steps=snake_case_   )
        torch.manual_seed(0   )
        UpperCamelCase       						=      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       						=      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    ,	)
        UpperCamelCase       						=      CLIPTextModel(snake_case_   )
        UpperCamelCase       						=      CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

        UpperCamelCase       						=      {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0   )						->   Union[str, Any]:



        """simple docstring"""






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



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase       						=      self.get_dummy_components()
        UpperCamelCase       						=      StableDiffusionInstructPixaPixPipeline(**snake_case_   )
        UpperCamelCase       						=      sd_pipe.to(snake_case_   )
        sd_pipe.set_progress_bar_config(disable=snake_case_   )

        UpperCamelCase       						=      self.get_dummy_inputs(snake_case_   )
        UpperCamelCase       						=      sd_pipe(**snake_case_   ).images
        UpperCamelCase       						=      image[0, -3:, -3:, -1]
        assert image.shape == (1, 32, 32, 3)
        UpperCamelCase       						=      np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]   )

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

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



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase       						=      self.get_dummy_components()
        UpperCamelCase       						=      StableDiffusionInstructPixaPixPipeline(**snake_case_   )
        UpperCamelCase       						=      sd_pipe.to(snake_case_   )
        sd_pipe.set_progress_bar_config(disable=snake_case_   )

        UpperCamelCase       						=      self.get_dummy_inputs(snake_case_   )
        UpperCamelCase       						=      """french fries"""
        UpperCamelCase       						=      sd_pipe(**snake_case_    ,	negative_prompt=snake_case_   )
        UpperCamelCase       						=      output.images
        UpperCamelCase       						=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        UpperCamelCase       						=      np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]   )

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

  def 						A__		(   self   )						->   int:



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase       						=      self.get_dummy_components()
        UpperCamelCase       						=      StableDiffusionInstructPixaPixPipeline(**snake_case_   )
        UpperCamelCase       						=      sd_pipe.to(snake_case_   )
        sd_pipe.set_progress_bar_config(disable=snake_case_   )

        UpperCamelCase       						=      self.get_dummy_inputs(snake_case_   )
        UpperCamelCase       						=      [inputs["""prompt"""]] * 2

        UpperCamelCase       						=      np.array(inputs["""image"""]   ).astype(np.floataa   ) / 2_5_5.0
        UpperCamelCase       						=      torch.from_numpy(snake_case_   ).unsqueeze(0   ).to(snake_case_   )
        UpperCamelCase       						=      image / 2 + 0.5
        UpperCamelCase       						=      image.permute(0    ,	3    ,	1    ,	2   )
        UpperCamelCase       						=      image.repeat(2    ,	1    ,	1    ,	1   )

        UpperCamelCase       						=      sd_pipe(**snake_case_   ).images
        UpperCamelCase       						=      image[-1, -3:, -3:, -1]

        assert image.shape == (2, 32, 32, 3)
        UpperCamelCase       						=      np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]   )

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

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



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""  # ensure determinism for the device-dependent torch.Generator
        UpperCamelCase       						=      self.get_dummy_components()
        UpperCamelCase       						=      EulerAncestralDiscreteScheduler(
            beta_start=0.0_0_0_8_5    ,	beta_end=0.0_1_2    ,	beta_schedule="""scaled_linear"""   )
        UpperCamelCase       						=      StableDiffusionInstructPixaPixPipeline(**snake_case_   )
        UpperCamelCase       						=      sd_pipe.to(snake_case_   )
        sd_pipe.set_progress_bar_config(disable=snake_case_   )

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

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

        assert image.shape == (1, 32, 32, 3)
        UpperCamelCase       						=      np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]   )

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

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



        """simple docstring"""






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


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



        """simple docstring"""






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

        UpperCamelCase       						=      pipe(**self.get_dummy_inputs_by_type(snake_case_    ,	input_image_type="""pt"""   )   )[0]

        UpperCamelCase       						=      components["""vae"""]
        UpperCamelCase       						=      self.get_dummy_inputs_by_type(snake_case_    ,	input_image_type="""pt"""   )

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

        UpperCamelCase       						=      pipe(**snake_case_   )[0]

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






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

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



        """simple docstring"""






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

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE=0   )						->   Tuple:



        """simple docstring"""






        UpperCamelCase       						=      torch.manual_seed(snake_case_   )
        UpperCamelCase       						=      load_image(
            """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""   )
        UpperCamelCase       						=      {
            """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   )						->   str:



        """simple docstring"""






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

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

        assert image.shape == (1, 512, 512, 3)
        UpperCamelCase       						=      np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]   )

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

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



        """simple docstring"""






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

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

        assert image.shape == (1, 512, 512, 3)
        UpperCamelCase       						=      np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]   )

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

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



        """simple docstring"""






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

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

        assert image.shape == (1, 512, 512, 3)
        UpperCamelCase       						=      np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]   )

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

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



        """simple docstring"""






        UpperCamelCase       						=      0

        def callback_fn(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ) -> None:
              UpperCamelCase       						=      True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                    UpperCamelCase       						=      latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 64)
                    UpperCamelCase       						=      latents[0, -3:, -3:, -1]
                    UpperCamelCase       						=      np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]   )

                    assert np.abs(latents_slice.flatten() - expected_slice   ).max() < 5e-2
              elif step == 2:
                    UpperCamelCase       						=      latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 64)
                    UpperCamelCase       						=      latents[0, -3:, -3:, -1]
                    UpperCamelCase       						=      np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]   )

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

        UpperCamelCase       						=      False

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

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

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



        """simple docstring"""






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

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

        UpperCamelCase       						=      self.get_inputs()
        UpperCamelCase       						=      pipe(**snake_case_   )

        UpperCamelCase       						=      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   )						->   Dict:



        """simple docstring"""






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

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

        UpperCamelCase       						=      pipe(**snake_case_   )
        UpperCamelCase       						=      output.images[0]

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

        assert image.shape == (504, 504, 3)
        UpperCamelCase       						=      np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]   )

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








'''simple docstring'''

from __future__ import annotations







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase					)->				dict[str, float]:
      if (voltage, current, resistance).count(0					) != 1:
            raise ValueError("""One and only one argument must be 0"""					)
      if resistance < 0:
            raise ValueError("""Resistance cannot be negative"""					)
      if voltage == 0:
            return {"voltage": float(current * resistance					)}
      elif current == 0:
            return {"current": voltage / resistance}
      elif resistance == 0:
            return {"resistance": voltage / current}
      else:
            raise ValueError("""Exactly one argument must be 0"""					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
