import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_SCREAMING_SNAKE_CASE:						int    	=       {512: "DiT-XL-2-512x512.pt", 256: "DiT-XL-2-256x256.pt"}






def    UpperCAmelCase__(						lowerCamelCase_  ):



   """simple docstring"""





   _snake_case              =      f'pretrained_models/{model_name}'
   if not os.path.isfile(_UpperCamelCase  ):
      os.makedirs("pretrained_models"							,	exist_ok=_UpperCamelCase  )
      _snake_case              =      f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
      download_url(_UpperCamelCase							,	"pretrained_models"  )
   _snake_case              =      torch.load(_UpperCamelCase							,	map_location=lambda lowerCamelCase_							,	lowerCamelCase_  : storage  )
   return model






def    UpperCAmelCase__(						lowerCamelCase_  ):



   """simple docstring"""





   _snake_case              =      download_model(pretrained_models[args.image_size]  )

   _snake_case              =      state_dict["x_embedder.proj.weight"]
   _snake_case              =      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              =      state_dict[
          "t_embedder.mlp.0.weight"
      ]
      _snake_case              =      state_dict[
          "t_embedder.mlp.0.bias"
      ]
      _snake_case              =      state_dict[
          "t_embedder.mlp.2.weight"
      ]
      _snake_case              =      state_dict[
          "t_embedder.mlp.2.bias"
      ]
      _snake_case              =      state_dict[
          "y_embedder.embedding_table.weight"
      ]

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

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

      _snake_case              =      q
      _snake_case              =      q_bias
      _snake_case              =      k
      _snake_case              =      k_bias
      _snake_case              =      v
      _snake_case              =      v_bias

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

      _snake_case              =      state_dict[f'blocks.{depth}.mlp.fc1.weight']
      _snake_case              =      state_dict[f'blocks.{depth}.mlp.fc1.bias']
      _snake_case              =      state_dict[f'blocks.{depth}.mlp.fc2.weight']
      _snake_case              =      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              =      state_dict["final_layer.adaLN_modulation.1.weight"]
   _snake_case              =      state_dict["final_layer.adaLN_modulation.1.bias"]
   _snake_case              =      state_dict["final_layer.linear.weight"]
   _snake_case              =      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              =      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=_UpperCamelCase							,	num_attention_heads=16							,	activation_fn="gelu-approximate"							,	num_embeds_ada_norm=1000							,	norm_type="ada_norm_zero"							,	norm_elementwise_affine=_UpperCamelCase							,	)
   transformer.load_state_dict(_UpperCamelCase							,	strict=_UpperCamelCase  )

   _snake_case              =      DDIMScheduler(
       num_train_timesteps=1000							,	beta_schedule="linear"							,	prediction_type="epsilon"							,	clip_sample=_UpperCamelCase							,	)

   _snake_case              =      AutoencoderKL.from_pretrained(args.vae_model  )

   _snake_case              =      DiTPipeline(transformer=_UpperCamelCase							,	vae=_UpperCamelCase							,	scheduler=_UpperCamelCase  )

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


if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE:						Optional[Any]    	=       argparse.ArgumentParser()

   parser.add_argument(
       '''--image_size''',
       default=256,
       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.'''
   )

   _SCREAMING_SNAKE_CASE:						List[str]    	=       parser.parse_args()
   main(args)


from math import gcd






def    UpperCAmelCase__(						lowerCamelCase_ = 12000  ):



			"""simple docstring"""





			_snake_case              =      0
			for d in range(max_d + 1  ):
						for n in range(d // 3 + 1							,	(d + 1) // 2  ):
									if gcd(lowerCamelCase_							,	lowerCamelCase_  ) == 1:
												fractions_number += 1
			return fractions_number


if __name__ == "__main__":
			print(F"{solution() = }")

