"""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        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  str     							=							StableDiffusionInstructPixaPixPipeline
    _snake_case     :  List[Any]     							=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
    _snake_case     :  str     							=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    _snake_case     :  Tuple     							=							IMAGE_TO_IMAGE_IMAGE_PARAMS
    _snake_case     :  Any     							=							IMAGE_TO_IMAGE_IMAGE_PARAMS


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      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=__lowerCAmelCase					)
      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-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)
      _UpperCAmelCase						 =      CLIPTextModel(__lowerCAmelCase					)
      _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      lowerCAmelCase_  (					self	:  List[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[Any]=0					):
      _UpperCAmelCase						 =      floats_tensor((1, 3, 32, 32)   ,				rng=random.Random(__lowerCAmelCase					)					).to(__lowerCAmelCase					)
      _UpperCAmelCase						 =      image.cpu().permute(0   ,				2   ,				3   ,				1					)[0]
      _UpperCAmelCase						 =      Image.fromarray(np.uinta(__lowerCAmelCase					)					).convert("""RGB"""					)
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
      _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      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      _UpperCAmelCase						 =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      _UpperCAmelCase						 =      self.get_dummy_components()
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      sd_pipe.to(__lowerCAmelCase					)
      sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

      _UpperCAmelCase						 =      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase						 =      sd_pipe(**__lowerCAmelCase					).images
      _UpperCAmelCase						 =      image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      _UpperCAmelCase						 =      self.get_dummy_components()
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      sd_pipe.to(__lowerCAmelCase					)
      sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

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

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

      _UpperCAmelCase						 =      self.get_dummy_inputs(__lowerCAmelCase					)
      _UpperCAmelCase						 =      [inputs["""prompt"""]] * 2

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

      _UpperCAmelCase						 =      sd_pipe(**__lowerCAmelCase					).images
      _UpperCAmelCase						 =      image[-1, -3:, -3:, -1]

      assert image.shape == (2, 32, 32, 3)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      """cpu"""  # ensure determinism for the device-dependent torch.Generator
      _UpperCAmelCase						 =      self.get_dummy_components()
      _UpperCAmelCase						 =      EulerAncestralDiscreteScheduler(
          beta_start=0.00_085   ,				beta_end=0.012   ,				beta_schedule="""scaled_linear"""					)
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      sd_pipe.to(__lowerCAmelCase					)
      sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

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

      _UpperCAmelCase						 =      [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)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Optional[int]					):
      super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      self.get_dummy_components()
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      VaeImageProcessor(do_resize=__lowerCAmelCase   ,				do_normalize=__lowerCAmelCase					)
      _UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)

      _UpperCAmelCase						 =      pipe(**self.get_dummy_inputs_by_type(__lowerCAmelCase   ,				input_image_type="""pt"""					)					)[0]

      _UpperCAmelCase						 =      components["""vae"""]
      _UpperCAmelCase						 =      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():
          _UpperCAmelCase						 =      vae.encode(inputs[image_param]					).latent_dist.mode()

      _UpperCAmelCase						 =      pipe(**__lowerCAmelCase					)[0]

      _UpperCAmelCase						 =      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   ):


    def      lowerCAmelCase_  (					self	:  Dict					):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


    def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]=0					):
      _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      _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      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,				safety_checker=__lowerCAmelCase					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,				safety_checker=__lowerCAmelCase					)
      _UpperCAmelCase						 =      LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      StableDiffusionInstructPixaPixPipeline.from_pretrained(
          """timbrooks/instruct-pix2pix"""   ,				safety_checker=__lowerCAmelCase					)
      _UpperCAmelCase						 =      DDIMScheduler.from_config(pipe.scheduler.config					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()

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

      assert image.shape == (1, 512, 512, 3)
      _UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      0

      def callback_fn(__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  torch.FloatTensor					) -> 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_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:
          _UpperCAmelCase						 =      latents.detach().cpu().numpy()
          assert latents.shape == (1, 4, 64, 64)
          _UpperCAmelCase						 =      latents[0, -3:, -3:, -1]
          _UpperCAmelCase						 =      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

      _UpperCAmelCase						 =      False

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

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


    def      lowerCAmelCase_  (					self	:  str					):
      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=__lowerCAmelCase   ,				torch_dtype=torch.floataa					)
      _UpperCAmelCase						 =      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing(1					)
      pipe.enable_sequential_cpu_offload()

      _UpperCAmelCase						 =      self.get_inputs()
      _UpperCAmelCase						 =      pipe(**__lowerCAmelCase					)

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


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _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(
          __lowerCAmelCase   ,				safety_checker=__lowerCAmelCase   ,				)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      pipe.enable_attention_slicing()

      _UpperCAmelCase						 =      pipe(**__lowerCAmelCase					)
      _UpperCAmelCase						 =      output.images[0]

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

      assert image.shape == (504, 504, 3)
      _UpperCAmelCase						 =      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 unittest

from knapsack import knapsack as k


class        a				(		unittest.TestCase   ):


    def      lowerCAmelCase_  (					self	:  List[Any]					):
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      [0]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)

      _UpperCAmelCase						 =      [60]
      _UpperCAmelCase						 =      [10]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				0					)


    def      lowerCAmelCase_  (					self	:  Optional[int]					):
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      [1, 2, 3]
      _UpperCAmelCase						 =      [3, 2, 1]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				5					)


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      50
      _UpperCAmelCase						 =      [60, 100, 120]
      _UpperCAmelCase						 =      [10, 20, 30]
      _UpperCAmelCase						 =      len(__lowerCAmelCase					)
      self.assertEqual(k.knapsack(__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase   ,				__lowerCAmelCase					)   ,				220					)


if __name__ == "__main__":
    unittest.main()
