




"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class    lowercase__     (					__lowerCAmelCase       ,      unittest.TestCase   ):







       '''simple docstring'''


       UpperCamelCase		=  KandinskyVaaControlnetImgaImgPipeline
       UpperCamelCase		=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase		=  ['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
       UpperCamelCase		=  [
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       UpperCamelCase		=  False



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		int:


             '''simple docstring'''




             return 32



       @property
       def   lowercase__       (							self    :						List[Any]					)				->		Optional[Any]:


             '''simple docstring'''




             return 32



       @property
       def   lowercase__       (							self    :						Dict					)				->		int:


             '''simple docstring'''




             return self.time_input_dim



       @property
       def   lowercase__       (							self    :						Dict					)				->		Union[str, Any]:


             '''simple docstring'''




             return self.time_input_dim * 4



       @property
       def   lowercase__       (							self    :						Dict					)				->		str:


             '''simple docstring'''




             return 100



       @property
       def   lowercase__       (							self    :						Union[str, Any]					)				->		List[Any]:


             '''simple docstring'''




             torch.manual_seed(0					)

             UpperCAmelCase_    				=							{
                 """in_channels""": 8,
                 # Out channels is double in channels because predicts mean and variance
                 """out_channels""": 8,
                 """addition_embed_type""": """image_hint""",
                 """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
                 """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
                 """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
                 """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
                 """layers_per_block""": 1,
                 """encoder_hid_dim""": self.text_embedder_hidden_size,
                 """encoder_hid_dim_type""": """image_proj""",
                 """cross_attention_dim""": self.cross_attention_dim,
                 """attention_head_dim""": 4,
                 """resnet_time_scale_shift""": """scale_shift""",
                 """class_embed_type""": None,
             }

             UpperCAmelCase_    				=							UNetaDConditionModel(**_UpperCamelCase					)
             return model



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		List[Any]:


             '''simple docstring'''




             return {
                 "block_out_channels": [32, 32, 64, 64],
                 "down_block_types": [
                     "DownEncoderBlock2D",
                     "DownEncoderBlock2D",
                     "DownEncoderBlock2D",
                     "AttnDownEncoderBlock2D",
                 ],
                 "in_channels": 3,
                 "latent_channels": 4,
                 "layers_per_block": 1,
                 "norm_num_groups": 8,
                 "norm_type": "spatial",
                 "num_vq_embeddings": 12,
                 "out_channels": 3,
                 "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
                 "vq_embed_dim": 4,
             }



       @property
       def   lowercase__       (							self    :						Optional[int]					)				->		str:


             '''simple docstring'''




             torch.manual_seed(0					)
             UpperCAmelCase_    				=							VQModel(**self.dummy_movq_kwargs					)
             return model



       def   lowercase__       (							self    :						str					)				->		Tuple:


             '''simple docstring'''




             UpperCAmelCase_    				=							self.dummy_unet
             UpperCAmelCase_    				=							self.dummy_movq

             UpperCAmelCase_    				=							{
                 """num_train_timesteps""": 1000,
                 """beta_schedule""": """linear""",
                 """beta_start""": 0.0_0085,
                 """beta_end""": 0.012,
                 """clip_sample""": False,
                 """set_alpha_to_one""": False,
                 """steps_offset""": 0,
                 """prediction_type""": """epsilon""",
                 """thresholding""": False,
             }

             UpperCAmelCase_    				=							DDIMScheduler(**_UpperCamelCase					)

             UpperCAmelCase_    				=							{
                 """unet""": unet,
                 """scheduler""": scheduler,
                 """movq""": movq,
             }

             return components



       def   lowercase__       (							self    :						Tuple	,       _UpperCAmelCase    :						List[Any]	,       _UpperCAmelCase    :						Any=0					)				->		str:


             '''simple docstring'''




             UpperCAmelCase_    				=							floats_tensor((1, self.text_embedder_hidden_size)	,       rng=random.Random(_UpperCamelCase					)					).to(_UpperCamelCase					)
             UpperCAmelCase_    				=							floats_tensor((1, self.text_embedder_hidden_size)	,       rng=random.Random(seed + 1					)					).to(
                 _UpperCamelCase					)
             # create init_image
             UpperCAmelCase_    				=							floats_tensor((1, 3, 64, 64)	,       rng=random.Random(_UpperCamelCase					)					).to(_UpperCamelCase					)
             UpperCAmelCase_    				=							image.cpu().permute(0	,       2	,       3	,       1					)[0]
             UpperCAmelCase_    				=							Image.fromarray(np.uinta(_UpperCamelCase					)					).convert("RGB"					).resize((256, 256)					)
             # create hint
             UpperCAmelCase_    				=							floats_tensor((1, 3, 64, 64)	,       rng=random.Random(_UpperCamelCase					)					).to(_UpperCamelCase					)

             if str(_UpperCamelCase					).startswith("mps"					):
                   UpperCAmelCase_    				=							torch.manual_seed(_UpperCamelCase					)
             else:
                   UpperCAmelCase_    				=							torch.Generator(device=_UpperCamelCase					).manual_seed(_UpperCamelCase					)
             UpperCAmelCase_    				=							{
                 """image""": init_image,
                 """image_embeds""": image_embeds,
                 """negative_image_embeds""": negative_image_embeds,
                 """hint""": hint,
                 """generator""": generator,
                 """height""": 64,
                 """width""": 64,
                 """num_inference_steps""": 10,
                 """guidance_scale""": 7.0,
                 """strength""": 0.2,
                 """output_type""": """np""",
             }
             return inputs







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


             '''simple docstring'''




             UpperCAmelCase_    				=							"""cpu"""

             UpperCAmelCase_    				=							self.get_dummy_components()

             UpperCAmelCase_    				=							self.pipeline_class(**_UpperCamelCase					)
             UpperCAmelCase_    				=							pipe.to(_UpperCamelCase					)

             pipe.set_progress_bar_config(disable=_UpperCamelCase					)

             UpperCAmelCase_    				=							pipe(**self.get_dummy_inputs(_UpperCamelCase					)					)
             UpperCAmelCase_    				=							output.images

             UpperCAmelCase_    				=							pipe(
                 **self.get_dummy_inputs(_UpperCamelCase					)	,       return_dict=_UpperCamelCase	,       )[0]

             UpperCAmelCase_    				=							image[0, -3:, -3:, -1]
             UpperCAmelCase_    				=							image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)

             UpperCAmelCase_    				=							np.array(
                 [0.5498_5034, 0.5550_9365, 0.5256_1504, 0.557_0494, 0.559_3818, 0.526_3979, 0.5028_5643, 0.506_9846, 0.5119_6736]					)
             assert (
                 np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
             ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
             assert (
                 np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
             ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""



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







       '''simple docstring'''


       def   lowercase__       (							self    :						Any					)				->		Dict:


             '''simple docstring'''




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







       def   lowercase__       (							self    :						Optional[Any]					)				->		int:


             '''simple docstring'''




             UpperCAmelCase_    				=							load_numpy(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"					)

             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"					)
             UpperCAmelCase_    				=							init_image.resize((512, 512)					)

             UpperCAmelCase_    				=							load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/kandinskyv22/hint_image_cat.png"					)
             UpperCAmelCase_    				=							torch.from_numpy(np.array(_UpperCamelCase					)					).float() / 255.0
             UpperCAmelCase_    				=							hint.permute(2	,       0	,       1					).unsqueeze(0					)

             UpperCAmelCase_    				=							"""A robot, 4k photo"""

             UpperCAmelCase_    				=							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-prior"	,       torch_dtype=torch.floataa					)
             pipe_prior.to(_UpperCamelCase					)

             UpperCAmelCase_    				=							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
                 "kandinsky-community/kandinsky-2-2-controlnet-depth"	,       torch_dtype=torch.floataa					)
             UpperCAmelCase_    				=							pipeline.to(_UpperCamelCase					)

             pipeline.set_progress_bar_config(disable=_UpperCamelCase					)

             UpperCAmelCase_    				=							torch.Generator(device="cpu"					).manual_seed(0					)

             UpperCAmelCase_    				=							pipe_prior(
                 _UpperCamelCase	,       image=_UpperCamelCase	,       strength=0.85	,       generator=_UpperCamelCase	,       negative_prompt=""	,       ).to_tuple()

             UpperCAmelCase_    				=							pipeline(
                 image=_UpperCamelCase	,       image_embeds=_UpperCamelCase	,       negative_image_embeds=_UpperCamelCase	,       hint=_UpperCamelCase	,       generator=_UpperCamelCase	,       num_inference_steps=100	,       height=512	,       width=512	,       strength=0.5	,       output_type="np"	,       )

             UpperCAmelCase_    				=							output.images[0]

             assert image.shape == (512, 512, 3)

             assert_mean_pixel_difference(_UpperCamelCase	,       _UpperCamelCase					)









"""simple docstring"""





from __future__ import annotations
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							[]
      UpperCAmelCase_				,					UpperCAmelCase_    				=							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   )   )
      UpperCAmelCase_    				=							result + left + right
      return input_list
def      a__			(   lowerCAmelCase__   ):
      if len(lowerCAmelCase__   ) <= 1:
            return input_list
      UpperCAmelCase_    				=							list(lowerCAmelCase__   )

      # iteration for two-way merging
      UpperCAmelCase_    				=							2
      while p <= len(lowerCAmelCase__   ):
            # getting low, high and middle value for merge-sort of single list
            for i in range(0			,    len(lowerCAmelCase__   )			,    lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							i + p - 1
                  UpperCAmelCase_    				=							(low + high + 1) // 2
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )
            # final merge of last two parts
            if p * 2 >= len(lowerCAmelCase__   ):
                  UpperCAmelCase_    				=							i
                  UpperCAmelCase_    				=							merge(lowerCAmelCase__			,    0			,    lowerCAmelCase__			,    len(lowerCAmelCase__   ) - 1   )
                  break
            p *= 2

      return input_list


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




