


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

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

      lowerCAmelCase_     : List[Any]     =  self.get_dummy_inputs(lowerCamelCase						)
      lowerCAmelCase_     : List[Any]     =  sd_pipe(**lowerCamelCase						).images
      lowerCAmelCase_     : Union[str, Any]     =  image[0, -3:, -3:, -1]
      assert image.shape == (1, 32, 32, 3)
      lowerCAmelCase_     : Optional[Any]     =  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       :    Optional[int]						)  ->    Optional[Any]:
      lowerCAmelCase_     : Optional[int]     =  """cpu"""  # ensure determinism for the device-dependent torch.Generator
      lowerCAmelCase_     : Tuple     =  self.get_dummy_components()
      lowerCAmelCase_     : Any     =  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_     : List[Any]     =  """french fries"""
      lowerCAmelCase_     : Optional[Any]     =  sd_pipe(**lowerCamelCase       ,		negative_prompt=lowerCamelCase						)
      lowerCAmelCase_     : Optional[Any]     =  output.images
      lowerCAmelCase_     : List[str]     =  image[0, -3:, -3:, -1]

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

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

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

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

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

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

      lowerCAmelCase_     : List[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_     : Optional[int]     =  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       :    List[Any]						)  ->    Optional[Any]:
      super().test_inference_batch_single_identical(expected_max_diff=3E-3						)





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

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

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

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

      lowerCAmelCase_     : 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       :    Optional[Any]						)  ->    str:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()

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

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

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

      lowerCAmelCase_     : List[Any]     =  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_     : List[str]     =  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       :    Tuple						)  ->    List[Any]:
      lowerCAmelCase_     : Dict     =  0

      def callback_fn(lowerCamelCase       :    int       ,		lowerCamelCase       :    int       ,		lowerCamelCase       :    torch.FloatTensor						) -> None:
        lowerCAmelCase_     : Optional[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_     : Any     =  latents[0, -3:, -3:, -1]
          lowerCAmelCase_     : Optional[int]     =  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_     : Union[str, Any]     =  latents.detach().cpu().numpy()
          assert latents.shape == (1, 4, 64, 64)
          lowerCAmelCase_     : List[Any]     =  latents[0, -3:, -3:, -1]
          lowerCAmelCase_     : int     =  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_     : int     =  False

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

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

    def __lowercase  (  self       :    Tuple						)  ->    Dict:
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

      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(1						)
      pipe.enable_sequential_cpu_offload()

      lowerCAmelCase_     : str     =  self.get_inputs()
      lowerCAmelCase_     : Any     =  pipe(**lowerCamelCase						)

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

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

      lowerCAmelCase_     : int     =  pipe(**lowerCamelCase						)
      lowerCAmelCase_     : Optional[int]     =  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'''






from __future__ import annotations







def 							UpperCamelCase_     (       A__   :  list				,       A__   :  int				,       A__   :  int				,       A__   :  int   ):
  '''simple docstring'''





  lowerCAmelCase_     : int     =  []
  lowerCAmelCase_,				lowerCAmelCase_     : Any     =  input_list[low:mid], input_list[mid : high + 1]
  while left and right:
    result.append((left if left[0] <= right[0] else right).pop(0   )   )
  lowerCAmelCase_     : Dict     =  result + left + right
  return input_list







def 							UpperCamelCase_     (       A__   :  list   ):
  '''simple docstring'''





  if len(A__   ) <= 1:
    return input_list
  lowerCAmelCase_     : str     =  list(A__   )

  # iteration for two-way merging
  lowerCAmelCase_     : Dict     =  2
  while p <= len(A__   ):
    # getting low, high and middle value for merge-sort of single list
    for i in range(0				,       len(A__   )				,       A__   ):
      lowerCAmelCase_     : Tuple     =  i
      lowerCAmelCase_     : List[str]     =  i + p - 1
      lowerCAmelCase_     : Optional[Any]     =  (low + high + 1) // 2
      lowerCAmelCase_     : List[str]     =  merge(A__				,       A__				,       A__				,       A__   )
    # final merge of last two parts
    if p * 2 >= len(A__   ):
      lowerCAmelCase_     : Union[str, Any]     =  i
      lowerCAmelCase_     : Dict     =  merge(A__				,       0				,       A__				,       len(A__   ) - 1   )
      break
    p *= 2

  return input_list


if __name__ == "__main__":
  __A   :    int											= input("Enter numbers separated by a comma:\n").strip()
  if user_input == "":
    __A   :    str											= []
  else:
    __A   :    Optional[Any]											= [int(item.strip()) for item in user_input.split(",")]
  print(iter_merge_sort(unsorted))



