


'''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        __snake_case	(       _SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,_SCREAMING_SNAKE_CASE     ,unittest.TestCase):







    """simple docstring"""



    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 __lowercase       (       self   :  Tuple		)       ->  List[str]:
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Union[str, 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   ,						)
      lowerCAmelCase_      :				Any      =       PNDMScheduler(skip_prk_steps=lowerCamelCase		)
      torch.manual_seed(0		)
      lowerCAmelCase_      :				Optional[int]      =       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_      :				List[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=10_00   ,						)
      lowerCAmelCase_      :				str      =       CLIPTextModel(lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

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

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  List[Any]=0		)       ->  Dict:
      lowerCAmelCase_      :				Tuple      =       floats_tensor((1, 3, 32, 32)   ,						rng=random.Random(lowerCamelCase		)		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       image.cpu().permute(0   ,						2   ,						3   ,						1		)[0]
      lowerCAmelCase_      :				Optional[Any]      =       Image.fromarray(np.uinta(lowerCamelCase		)		).convert("""RGB"""		)
      if str(lowerCamelCase		).startswith("""mps"""		):
        lowerCAmelCase_      :				Tuple      =       torch.manual_seed(lowerCamelCase		)
      else:
        lowerCAmelCase_      :				int      =       torch.Generator(device=lowerCamelCase		).manual_seed(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       {
          """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   :  Tuple		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_      :				List[Any]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Union[str, Any]      =       StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
      lowerCAmelCase_      :				List[str]      =       sd_pipe.to(lowerCamelCase		)
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Optional[int]      =       self.get_dummy_inputs(lowerCamelCase		)
      lowerCAmelCase_      :				int      =       sd_pipe(**lowerCamelCase		).images
      lowerCAmelCase_      :				Tuple      =       image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				int      =       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 __lowercase       (       self   :  List[Any]		)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[Any]      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_      :				Optional[int]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Dict      =       StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
      lowerCAmelCase_      :				Optional[int]      =       sd_pipe.to(lowerCamelCase		)
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				List[str]      =       self.get_dummy_inputs(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       """french fries"""
      lowerCAmelCase_      :				Any      =       sd_pipe(**lowerCamelCase   ,						negative_prompt=lowerCamelCase		)
      lowerCAmelCase_      :				str      =       output.images
      lowerCAmelCase_      :				List[Any]      =       image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_      :				Tuple      =       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 __lowercase       (       self   :  Union[str, Any]		)       ->  Tuple:
      lowerCAmelCase_      :				Optional[Any]      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_      :				Union[str, Any]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Optional[int]      =       StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
      lowerCAmelCase_      :				Union[str, Any]      =       sd_pipe.to(lowerCamelCase		)
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       self.get_dummy_inputs(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       [inputs["""prompt"""]] * 2

      lowerCAmelCase_      :				Union[str, Any]      =       np.array(inputs["""image"""]		).astype(np.floataa		) / 255.0
      lowerCAmelCase_      :				int      =       torch.from_numpy(lowerCamelCase		).unsqueeze(0		).to(lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       image / 2 + 0.5
      lowerCAmelCase_      :				Dict      =       image.permute(0   ,						3   ,						1   ,						2		)
      lowerCAmelCase_      :				Union[str, Any]      =       image.repeat(2   ,						1   ,						1   ,						1		)

      lowerCAmelCase_      :				Optional[int]      =       sd_pipe(**lowerCamelCase		).images
      lowerCAmelCase_      :				Dict      =       image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      lowerCAmelCase_      :				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 __lowercase       (       self   :  Optional[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Optional[int]      =       """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_      :				List[str]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Dict      =       EulerAncestralDiscreteScheduler(
          beta_start=0.00_085   ,						beta_end=0.012   ,						beta_schedule="""scaled_linear"""		)
      lowerCAmelCase_      :				Any      =       StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
      lowerCAmelCase_      :				str      =       sd_pipe.to(lowerCamelCase		)
      sd_pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       self.get_dummy_inputs(lowerCamelCase		)
      lowerCAmelCase_      :				Tuple      =       sd_pipe(**lowerCamelCase		).images
      lowerCAmelCase_      :				List[str]      =       image[0, -3:, -3:, -1]

      lowerCAmelCase_      :				Any      =       [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_      :				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 __lowercase       (       self   :  Dict		)       ->  List[Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3		)





    def __lowercase       (       self   :  List[str]		)       ->  List[str]:
      lowerCAmelCase_      :				Union[str, Any]      =       self.get_dummy_components()
      lowerCAmelCase_      :				Optional[int]      =       StableDiffusionInstructPixaPixPipeline(**lowerCamelCase		)
      lowerCAmelCase_      :				int      =       VaeImageProcessor(do_resize=lowerCamelCase   ,						do_normalize=lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       pipe(**self.get_dummy_inputs_by_type(lowerCamelCase   ,						input_image_type="""pt"""		)		)[0]

      lowerCAmelCase_      :				Optional[int]      =       components["""vae"""]
      lowerCAmelCase_      :				Dict      =       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_      :				Any      =       vae.encode(inputs[image_param]		).latent_dist.mode()

      lowerCAmelCase_      :				List[str]      =       pipe(**lowerCamelCase		)[0]

      lowerCAmelCase_      :				Union[str, Any]      =       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        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    def __lowercase       (       self   :  List[str]		)       ->  str:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

    def __lowercase       (       self   :  str   ,						lowerCamelCase   :  List[str]=0		)       ->  Any:
      lowerCAmelCase_      :				str      =       torch.manual_seed(lowerCamelCase		)
      lowerCAmelCase_      :				str      =       load_image(
          """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""		)
      lowerCAmelCase_      :				List[str]      =       {
          """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   :  Union[str, Any]		)       ->  int:
      lowerCAmelCase_      :				Optional[int]      =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,						safety_checker=lowerCamelCase		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 5_12, 5_12, 3)
      lowerCAmelCase_      :				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 __lowercase       (       self   :  Union[str, Any]		)       ->  Tuple:
      lowerCAmelCase_      :				Union[str, Any]      =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,						safety_checker=lowerCamelCase		)
      lowerCAmelCase_      :				str      =       LMSDiscreteScheduler.from_config(pipe.scheduler.config		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)
      pipe.enable_attention_slicing()

      lowerCAmelCase_      :				Optional[int]      =       self.get_inputs()
      lowerCAmelCase_      :				int      =       pipe(**lowerCamelCase		).images
      lowerCAmelCase_      :				Any      =       image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 5_12, 5_12, 3)
      lowerCAmelCase_      :				Union[str, Any]      =       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 __lowercase       (       self   :  Tuple		)       ->  Optional[int]:
      lowerCAmelCase_      :				int      =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,						safety_checker=lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       DDIMScheduler.from_config(pipe.scheduler.config		)
      pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)
      pipe.enable_attention_slicing()

      lowerCAmelCase_      :				Optional[int]      =       self.get_inputs()
      lowerCAmelCase_      :				List[str]      =       pipe(**lowerCamelCase		).images
      lowerCAmelCase_      :				str      =       image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 5_12, 5_12, 3)
      lowerCAmelCase_      :				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 __lowercase       (       self   :  List[Any]		)       ->  int:
      lowerCAmelCase_      :				int      =       0

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

      lowerCAmelCase_      :				List[Any]      =       False

      lowerCAmelCase_      :				Optional[int]      =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,						safety_checker=lowerCamelCase   ,						torch_dtype=torch.floataa		)
      lowerCAmelCase_      :				Dict      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)
      pipe.enable_attention_slicing()

      lowerCAmelCase_      :				Dict      =       self.get_inputs()
      pipe(**lowerCamelCase   ,						callback=lowerCamelCase   ,						callback_steps=1		)
      assert callback_fn.has_been_called
      assert number_of_steps == 3

    def __lowercase       (       self   :  str		)       ->  Any:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      lowerCAmelCase_      :				Union[str, Any]      =       StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,						safety_checker=lowerCamelCase   ,						torch_dtype=torch.floataa		)
      lowerCAmelCase_      :				Optional[Any]      =       pipe.to(lowerCamelCase		)
      pipe.set_progress_bar_config(disable=lowerCamelCase		)
      pipe.enable_attention_slicing(1		)
      pipe.enable_sequential_cpu_offload()

      lowerCAmelCase_      :				Tuple      =       self.get_inputs()
      lowerCAmelCase_      :				Union[str, Any]      =       pipe(**lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       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   :  int		)       ->  Any:
      lowerCAmelCase_      :				str      =       self.get_inputs()
      # resize to resolution that is divisible by 8 but not 16 or 32
      lowerCAmelCase_      :				Tuple      =       inputs["""image"""].resize((5_04, 5_04)		)

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

      lowerCAmelCase_      :				Dict      =       pipe(**lowerCamelCase		)
      lowerCAmelCase_      :				str      =       output.images[0]

      lowerCAmelCase_      :				str      =       image[2_55:2_58, 3_83:3_86, -1]

      assert image.shape == (5_04, 5_04, 3)
      lowerCAmelCase_      :				Optional[int]      =       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







'''simple docstring'''






import requests







def     UpperCamelCase_      (   A__ :       str							,   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       {"""Content-Type""": """application/json"""}
  lowerCAmelCase_      :				Union[str, Any]      =       requests.post(A__							,   json={"""text""": message_body}							,   headers=A__ )
  if response.status_code != 2_00:
    lowerCAmelCase_      :				Dict      =       (
        """Request to slack returned an error """
        f'{response.status_code}, the response is:\n{response.text}'
    )
    raise ValueError(A__ )


if __name__ == "__main__":
  # Set the slack url to the one provided by Slack when you create the webhook at
  # https://my.slack.com/services/new/incoming-webhook/
  send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")



