


import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


__UpperCAmelCase      			=  {512: """DiT-XL-2-512x512.pt""", 256: """DiT-XL-2-256x256.pt"""}
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=f'''pretrained_models/{model_name}'''
   if not os.path.isfile(a__      ):
      os.makedirs("pretrained_models"							,  exist_ok=a__      )
      UpperCamelCase__ :			Union[str, Any] 						=f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
      download_url(a__							,  "pretrained_models"      )
   UpperCamelCase__ :			List[Any] 						=torch.load(a__							,  map_location=lambda A_							,  A_      : storage      )
   return model
def 	_lowerCamelCase  (		A_  :	List[Any]      )			->			Union[str, Any]:

   '''simple docstring'''






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

   UpperCamelCase__ :			Optional[int] 						=state_dict["x_embedder.proj.weight"]
   UpperCamelCase__ :			List[str] 						=state_dict["x_embedder.proj.bias"]
   state_dict.pop("x_embedder.proj.weight"      )
   state_dict.pop("x_embedder.proj.bias"      )

   for depth in range(2_8      ):
      UpperCamelCase__ :			Optional[int] 						=state_dict[
          "t_embedder.mlp.0.weight"
      ]
      UpperCamelCase__ :			Any 						=state_dict[
          "t_embedder.mlp.0.bias"
      ]
      UpperCamelCase__ :			Any 						=state_dict[
          "t_embedder.mlp.2.weight"
      ]
      UpperCamelCase__ :			List[str] 						=state_dict[
          "t_embedder.mlp.2.bias"
      ]
      UpperCamelCase__ :			Dict 						=state_dict[
          "y_embedder.embedding_table.weight"
      ]

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

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

      UpperCamelCase__ :			List[Any] 						=q
      UpperCamelCase__ :			Tuple 						=q_bias
      UpperCamelCase__ :			Union[str, Any] 						=k
      UpperCamelCase__ :			Union[str, Any] 						=k_bias
      UpperCamelCase__ :			Optional[Any] 						=v
      UpperCamelCase__ :			int 						=v_bias

      UpperCamelCase__ :			Any 						=state_dict[
          f'''blocks.{depth}.attn.proj.weight'''
      ]
      UpperCamelCase__ :			List[Any] 						=state_dict[f'''blocks.{depth}.attn.proj.bias''']

      UpperCamelCase__ :			Optional[int] 						=state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
      UpperCamelCase__ :			List[str] 						=state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
      UpperCamelCase__ :			Union[str, Any] 						=state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
      UpperCamelCase__ :			Optional[Any] 						=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"      )

   UpperCamelCase__ :			Union[str, Any] 						=state_dict["final_layer.adaLN_modulation.1.weight"]
   UpperCamelCase__ :			Tuple 						=state_dict["final_layer.adaLN_modulation.1.bias"]
   UpperCamelCase__ :			Tuple 						=state_dict["final_layer.linear.weight"]
   UpperCamelCase__ :			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
   UpperCamelCase__ :			Tuple 						=TransformeraDModel(
       sample_size=args.image_size // 8							,  num_layers=2_8							,  attention_head_dim=7_2							,  in_channels=4							,  out_channels=8							,  patch_size=2							,  attention_bias=a__							,  num_attention_heads=1_6							,  activation_fn="gelu-approximate"							,  num_embeds_ada_norm=1_0_0_0							,  norm_type="ada_norm_zero"							,  norm_elementwise_affine=a__							,  )
   transformer.load_state_dict(a__							,  strict=a__      )

   UpperCamelCase__ :			List[str] 						=DDIMScheduler(
       num_train_timesteps=1_0_0_0							,  beta_schedule="linear"							,  prediction_type="epsilon"							,  clip_sample=a__							,  )

   UpperCamelCase__ :			Optional[Any] 						=AutoencoderKL.from_pretrained(args.vae_model      )

   UpperCamelCase__ :			str 						=DiTPipeline(transformer=a__							,  vae=a__							,  scheduler=a__      )

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


if __name__ == "__main__":
     __UpperCAmelCase      			=  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."""
     )

     __UpperCAmelCase      			=  parser.parse_args()
     main(args)







'''simple docstring'''
from math import gcd
def 							snake_case_    (  a__						:      int = 1_20_00     ):



    """simple docstring"""




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


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




