





'''simple docstring'''

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase       :		Optional[Any]		=				{5_1_2: "DiT-XL-2-512x512.pt", 2_5_6: "DiT-XL-2-256x256.pt"}




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						List[str]         =  f'''pretrained_models/{model_name}'''
      if not os.path.isfile(_SCREAMING_SNAKE_CASE						):
            os.makedirs('''pretrained_models'''      ,   exist_ok=_SCREAMING_SNAKE_CASE						)
            _snake_case		:						Optional[int]         =  f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
            download_url(_SCREAMING_SNAKE_CASE      ,   '''pretrained_models'''						)
      _snake_case		:						Union[str, Any]         =  torch.load(_SCREAMING_SNAKE_CASE      ,   map_location=lambda lowerCAmelCase_      ,   lowerCAmelCase_						: storage						)
      return model




def  _a     (				lowerCAmelCase_						):





      """simple docstring"""



      _snake_case		:						int         =  download_model(pretrained_models[args.image_size]						)

      _snake_case		:						Optional[int]         =  state_dict['''x_embedder.proj.weight''']
      _snake_case		:						Optional[int]         =  state_dict['''x_embedder.proj.bias''']
      state_dict.pop('''x_embedder.proj.weight'''						)
      state_dict.pop('''x_embedder.proj.bias'''						)

      for depth in range(28						):
            _snake_case		:						List[str]         =  state_dict[
                '''t_embedder.mlp.0.weight'''
            ]
            _snake_case		:						Optional[Any]         =  state_dict[
                '''t_embedder.mlp.0.bias'''
            ]
            _snake_case		:						Dict         =  state_dict[
                '''t_embedder.mlp.2.weight'''
            ]
            _snake_case		:						Dict         =  state_dict[
                '''t_embedder.mlp.2.bias'''
            ]
            _snake_case		:						Optional[int]         =  state_dict[
                '''y_embedder.embedding_table.weight'''
            ]

            _snake_case		:						Optional[Any]         =  state_dict[
                f'''blocks.{depth}.adaLN_modulation.1.weight'''
            ]
            _snake_case		:						Dict         =  state_dict[
                f'''blocks.{depth}.adaLN_modulation.1.bias'''
            ]

            _snake_case  ,				_snake_case  ,				_snake_case		:						int         =  torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.weight''']      ,   3      ,   dim=0						)
            _snake_case  ,				_snake_case  ,				_snake_case		:						Any         =  torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.bias''']      ,   3      ,   dim=0						)

            _snake_case		:						Optional[int]         =  q
            _snake_case		:						List[str]         =  q_bias
            _snake_case		:						Tuple         =  k
            _snake_case		:						Dict         =  k_bias
            _snake_case		:						List[str]         =  v
            _snake_case		:						str         =  v_bias

            _snake_case		:						str         =  state_dict[
                f'''blocks.{depth}.attn.proj.weight'''
            ]
            _snake_case		:						Optional[int]         =  state_dict[f'''blocks.{depth}.attn.proj.bias''']

            _snake_case		:						Union[str, Any]         =  state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
            _snake_case		:						int         =  state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
            _snake_case		:						str         =  state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
            _snake_case		:						Dict         =  state_dict[f'''blocks.{depth}.mlp.fc2.bias''']

            state_dict.pop(f'''blocks.{depth}.attn.qkv.weight'''						)
            state_dict.pop(f'''blocks.{depth}.attn.qkv.bias'''						)
            state_dict.pop(f'''blocks.{depth}.attn.proj.weight'''						)
            state_dict.pop(f'''blocks.{depth}.attn.proj.bias'''						)
            state_dict.pop(f'''blocks.{depth}.mlp.fc1.weight'''						)
            state_dict.pop(f'''blocks.{depth}.mlp.fc1.bias'''						)
            state_dict.pop(f'''blocks.{depth}.mlp.fc2.weight'''						)
            state_dict.pop(f'''blocks.{depth}.mlp.fc2.bias'''						)
            state_dict.pop(f'''blocks.{depth}.adaLN_modulation.1.weight'''						)
            state_dict.pop(f'''blocks.{depth}.adaLN_modulation.1.bias'''						)

      state_dict.pop('''t_embedder.mlp.0.weight'''						)
      state_dict.pop('''t_embedder.mlp.0.bias'''						)
      state_dict.pop('''t_embedder.mlp.2.weight'''						)
      state_dict.pop('''t_embedder.mlp.2.bias'''						)
      state_dict.pop('''y_embedder.embedding_table.weight'''						)

      _snake_case		:						Optional[int]         =  state_dict['''final_layer.adaLN_modulation.1.weight''']
      _snake_case		:						str         =  state_dict['''final_layer.adaLN_modulation.1.bias''']
      _snake_case		:						Optional[int]         =  state_dict['''final_layer.linear.weight''']
      _snake_case		:						Optional[Any]         =  state_dict['''final_layer.linear.bias''']

      state_dict.pop('''final_layer.linear.weight'''						)
      state_dict.pop('''final_layer.linear.bias'''						)
      state_dict.pop('''final_layer.adaLN_modulation.1.weight'''						)
      state_dict.pop('''final_layer.adaLN_modulation.1.bias'''						)

      # DiT XL/2
      _snake_case		:						Dict         =  TransformeraDModel(
          sample_size=args.image_size // 8      ,   num_layers=28      ,   attention_head_dim=72      ,   in_channels=4      ,   out_channels=8      ,   patch_size=2      ,   attention_bias=_SCREAMING_SNAKE_CASE      ,   num_attention_heads=16      ,   activation_fn='''gelu-approximate'''      ,   num_embeds_ada_norm=1_000      ,   norm_type='''ada_norm_zero'''      ,   norm_elementwise_affine=_SCREAMING_SNAKE_CASE      ,   )
      transformer.load_state_dict(_SCREAMING_SNAKE_CASE      ,   strict=_SCREAMING_SNAKE_CASE						)

      _snake_case		:						str         =  DDIMScheduler(
          num_train_timesteps=1_000      ,   beta_schedule='''linear'''      ,   prediction_type='''epsilon'''      ,   clip_sample=_SCREAMING_SNAKE_CASE      ,   )

      _snake_case		:						Any         =  AutoencoderKL.from_pretrained(args.vae_model						)

      _snake_case		:						List[Any]         =  DiTPipeline(transformer=_SCREAMING_SNAKE_CASE      ,   vae=_SCREAMING_SNAKE_CASE      ,   scheduler=_SCREAMING_SNAKE_CASE						)

      if args.save:
            pipeline.save_pretrained(args.checkpoint_path						)


if __name__ == "__main__":
   UpperCAmelCase       :		Optional[int]		=				argparse.ArgumentParser()

   parser.add_argument(
       '--image_size',
       default=2_5_6,
       type=int,
       required=False,
       help='Image size of pretrained model, either 256 or 512.',
   )
   parser.add_argument(
       '--vae_model',
       default='stabilityai/sd-vae-ft-ema',
       type=str,
       required=False,
       help='Path to pretrained VAE model, either stabilityai/sd-vae-ft-mse or stabilityai/sd-vae-ft-ema.',
   )
   parser.add_argument(
       '--save', default=True, type=bool, required=False, help='Whether to save the converted pipeline or not.'
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the output pipeline.'
   )

   UpperCAmelCase       :		List[Any]		=				parser.parse_args()
   main(args)





import random


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		List[Any]							):





    """simple docstring"""


    def is_sorted(_SCREAMING_SNAKE_CASE       :		List[Any]							):
        for i in range(len(_SCREAMING_SNAKE_CASE							) - 1							):
            if collection[i] > collection[i + 1]:
                return False
        return True

    while not is_sorted(_SCREAMING_SNAKE_CASE							):
        random.shuffle(_SCREAMING_SNAKE_CASE							)
    return collection


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


