





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	List[str]				      = KandinskyInpaintPipeline
   _UpperCamelCase					:	Optional[Any]				      = ['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   _UpperCamelCase					:	List[Any]				      = [
       'prompt',
       'negative_prompt',
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   _UpperCamelCase					:	int				      = [
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   _UpperCamelCase					:	str				      = False


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     Tuple:





     """simple docstring"""







     return 32


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





     """simple docstring"""







     return 32


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     Any:





     """simple docstring"""







     return self.time_input_dim


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





     """simple docstring"""







     return self.time_input_dim * 4


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Tuple )->     List[str]:





     """simple docstring"""







     return 100


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





     """simple docstring"""







     lowercase__			    =						XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base' )
     return tokenizer


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





     """simple docstring"""







     torch.manual_seed(0 )
     lowercase__			    =						MCLIPConfig(
         numDims=self.cross_attention_dim    ,  transformerDimensions=self.text_embedder_hidden_size    ,  hidden_size=self.text_embedder_hidden_size    ,  intermediate_size=37    ,  num_attention_heads=4    ,  num_hidden_layers=5    ,  vocab_size=1_005    ,  )

     lowercase__			    =						MultilingualCLIP(a )
     lowercase__			    =						text_encoder.eval()

     return text_encoder


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





     """simple docstring"""







     torch.manual_seed(0 )

     lowercase__			    =						{
         'in_channels': 9,
         # Out channels is double in channels because predicts mean and variance
         'out_channels': 8,
         'addition_embed_type': 'text_image',
         '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': 'text_image_proj',
         'cross_attention_dim': self.cross_attention_dim,
         'attention_head_dim': 4,
         'resnet_time_scale_shift': 'scale_shift',
         'class_embed_type': None,
     }

     lowercase__			    =						UNetaDConditionModel(**a )
     return model


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





     """simple docstring"""







     return {
         "block_out_channels": [32, 64],
         "down_block_types": ["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",
         ],
         "vq_embed_dim": 4,
     }


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





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     int:





     """simple docstring"""







     lowercase__			    =						self.dummy_text_encoder
     lowercase__			    =						self.dummy_tokenizer
     lowercase__			    =						self.dummy_unet
     lowercase__			    =						self.dummy_movq

     lowercase__			    =						DDIMScheduler(
         num_train_timesteps=1_000    ,  beta_schedule='linear'    ,  beta_start=0.00085    ,  beta_end=0.012    ,  clip_sample=a    ,  set_alpha_to_one=a    ,  steps_offset=1    ,  prediction_type='epsilon'    ,  thresholding=a    ,  )

     lowercase__			    =						{
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }

     return components


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int]    ,  a    :       Tuple    ,  a    :       Any=0 )->     Optional[Any]:





     """simple docstring"""







     lowercase__			    =						floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(a ) ).to(a )
     lowercase__			    =						floats_tensor((1, self.cross_attention_dim)    ,  rng=random.Random(seed + 1 ) ).to(a )
     # create init_image
     lowercase__			    =						floats_tensor((1, 3, 64, 64)    ,  rng=random.Random(a ) ).to(a )
     lowercase__			    =						image.cpu().permute(0    ,  2    ,  3    ,  1 )[0]
     lowercase__			    =						Image.fromarray(np.uinta(a ) ).convert('RGB' ).resize((256, 256) )
     # create mask
     lowercase__			    =						np.ones((64, 64)    ,  dtype=np.floataa )
     lowercase__			    =						0

     if str(a ).startswith('mps' ):
       lowercase__			    =						torch.manual_seed(a )
     else:
       lowercase__			    =						torch.Generator(device=a ).manual_seed(a )
     lowercase__			    =						{
         'prompt': 'horse',
         'image': init_image,
         'mask_image': mask,
         'image_embeds': image_embeds,
         'negative_image_embeds': negative_image_embeds,
         'generator': generator,
         'height': 64,
         'width': 64,
         'num_inference_steps': 2,
         'guidance_scale': 4.0,
         'output_type': 'np',
     }
     return inputs


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     str:





     """simple docstring"""







     lowercase__			    =						'cpu'

     lowercase__			    =						self.get_dummy_components()

     lowercase__			    =						self.pipeline_class(**a )
     lowercase__			    =						pipe.to(a )

     pipe.set_progress_bar_config(disable=a )

     lowercase__			    =						pipe(**self.get_dummy_inputs(a ) )
     lowercase__			    =						output.images

     lowercase__			    =						pipe(
         **self.get_dummy_inputs(a )    ,  return_dict=a    ,  )[0]

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

     print(f"""image.shape {image.shape}""" )

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

     lowercase__			    =						np.array(
         [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786] )

     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()}"""


   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     List[str]:





     """simple docstring"""







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






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


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





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[int] )->     Dict:





     """simple docstring"""







     lowercase__			    =						load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy' )

     lowercase__			    =						load_image(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
     lowercase__			    =						np.ones((768, 768)    ,  dtype=np.floataa )
     lowercase__			    =						0

     lowercase__			    =						'a hat'

     lowercase__			    =						KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,  torch_dtype=torch.floataa )
     pipe_prior.to(a )

     lowercase__			    =						KandinskyInpaintPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-inpaint'    ,  torch_dtype=torch.floataa )
     lowercase__			    =						pipeline.to(a )
     pipeline.set_progress_bar_config(disable=a )

     lowercase__			    =						torch.Generator(device='cpu' ).manual_seed(0 )
     lowercase__    ,					lowercase__			    =						pipe_prior(
         a    ,  generator=a    ,  num_inference_steps=5    ,  negative_prompt=''    ,  ).to_tuple()

     lowercase__			    =						pipeline(
         a    ,  image=a    ,  mask_image=a    ,  image_embeds=a    ,  negative_image_embeds=a    ,  generator=a    ,  num_inference_steps=100    ,  height=768    ,  width=768    ,  output_type='np'    ,  )

     lowercase__			    =						output.images[0]

     assert image.shape == (768, 768, 3)

     assert_mean_pixel_difference(a    ,  a )









def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[Any]:
  stooge(_SCREAMING_SNAKE_CASE	,		0	,		len(_SCREAMING_SNAKE_CASE	) - 1	)
  return arr




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     int:
  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    lowercase__    ,					lowercase__			    =						arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    lowercase__			    =						(int)((h - i + 1) / 3	)

    # Recursively sort first 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		(h - t)	)

    # Recursively sort last 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		i + t	,		(_SCREAMING_SNAKE_CASE)	)

    # Recursively sort first 2/3 elements
    stooge(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		(h - t)	)


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


