





import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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 				A_				(      UpperCAmelCase					,	unittest.TestCase	):







       """simple docstring"""





       SCREAMING_SNAKE_CASE_      : List[Any]      =  KandinskyImgaImgPipeline
       SCREAMING_SNAKE_CASE_      : Union[str, Any]      =  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''']
       SCREAMING_SNAKE_CASE_      : Tuple      =  [
           '''prompt''',
           '''negative_prompt''',
           '''image_embeds''',
           '''negative_image_embeds''',
           '''image''',
       ]
       SCREAMING_SNAKE_CASE_      : Any      =  [
           '''generator''',
           '''height''',
           '''width''',
           '''strength''',
           '''guidance_scale''',
           '''negative_prompt''',
           '''num_inference_steps''',
           '''return_dict''',
           '''guidance_scale''',
           '''num_images_per_prompt''',
           '''output_type''',
           '''return_dict''',
       ]
       SCREAMING_SNAKE_CASE_      : Tuple      =  False







       @property
       def    __UpperCAmelCase (    self	:	Any  )						->					str:
         return 32







       @property
       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[Any]:
         return 32







       @property
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Optional[Any]:
         return self.time_input_dim







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					List[Any]:
         return self.time_input_dim * 4







       @property
       def    __UpperCAmelCase (    self	:	List[str]  )						->					Dict:
         return 100







       @property
       def    __UpperCAmelCase (    self	:	Tuple  )						->					Dict:
         _lowercase							= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
         return tokenizer







       @property
       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Any:
         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=1005						,)

         _lowercase							= MultilingualCLIP(__A  )
         _lowercase							= text_encoder.eval()

         return text_encoder







       @property
       def    __UpperCAmelCase (    self	:	Any  )						->					Tuple:
         torch.manual_seed(0  )

         _lowercase							= {
             'in_channels': 4,
             # 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    __UpperCAmelCase (    self	:	Optional[int]  )						->					Tuple:
         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    __UpperCAmelCase (    self	:	int  )						->					Union[str, Any]:
         torch.manual_seed(0  )
         _lowercase							= VQModel(**self.dummy_movq_kwargs  )
         return model







       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Optional[int]:
         _lowercase							= self.dummy_text_encoder
         _lowercase							= self.dummy_tokenizer
         _lowercase							= self.dummy_unet
         _lowercase							= self.dummy_movq

         _lowercase							= {
             'num_train_timesteps': 1000,
             'beta_schedule': 'linear',
             'beta_start': 0.00085,
             'beta_end': 0.012,
             'clip_sample': False,
             'set_alpha_to_one': False,
             'steps_offset': 0,
             'prediction_type': 'epsilon',
             'thresholding': False,
         }

         _lowercase							= DDIMScheduler(**__A  )

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

         return components







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	int						,__A	:	List[Any]=0  )						->					Dict:
         _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)  )

         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,
             'image_embeds': image_embeds,
             'negative_image_embeds': negative_image_embeds,
             'generator': generator,
             'height': 64,
             'width': 64,
             'num_inference_steps': 10,
             'guidance_scale': 7.0,
             'strength': 0.2,
             'output_type': 'np',
         }
         return inputs







       def    __UpperCAmelCase (    self	:	List[Any]  )						->					Tuple:
         _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]

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

         _lowercase							= np.array(
             [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]  )
         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 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Optional[int]  )						->					Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







       def    __UpperCAmelCase (    self	:	str  )						->					str:
         _lowercase							= load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_img2img_frog.npy'  )

         _lowercase							= load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'  )
         _lowercase							= 'A red cartoon frog, 4k'

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

         _lowercase							= KandinskyImgaImgPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1'						,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						,image_embeds=__A						,negative_image_embeds=__A						,generator=__A						,num_inference_steps=100						,height=768						,width=768						,strength=0.2						,output_type='np'						,)

         _lowercase							= output.images[0]

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

         assert_mean_pixel_difference(__A						,__A  )






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Dict:
  _lowercase							= len(snake_case__     )
  while cur > 1:
    # Find the maximum number in arr
    _lowercase							= arr.index(max(arr[0:cur]     )     )
    # Reverse from 0 to mi
    _lowercase							= arr[mi::-1] + arr[mi + 1 : len(snake_case__     )]
    # Reverse whole list
    _lowercase							= arr[cur - 1 :: -1] + arr[cur : len(snake_case__     )]
    cur -= 1
  return arr


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