






"""simple docstring"""






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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




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




def 						_UpperCamelCase			(  A			):
			UpperCamelCase_     =download_model(pretrained_models[args.image_size]			)

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

						UpperCamelCase_     =state_dict[
						    f"""blocks.{depth}.adaLN_modulation.1.weight"""
						]
						UpperCamelCase_     =state_dict[
						    f"""blocks.{depth}.adaLN_modulation.1.bias"""
						]

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

						UpperCamelCase_     =q
						UpperCamelCase_     =q_bias
						UpperCamelCase_     =k
						UpperCamelCase_     =k_bias
						UpperCamelCase_     =v
						UpperCamelCase_     =v_bias

						UpperCamelCase_     =state_dict[
						    f"""blocks.{depth}.attn.proj.weight"""
						]
						UpperCamelCase_     =state_dict[f"""blocks.{depth}.attn.proj.bias"""]

						UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
						UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
						UpperCamelCase_     =state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
						UpperCamelCase_     =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_     =state_dict["final_layer.adaLN_modulation.1.weight"]
			UpperCamelCase_     =state_dict["final_layer.adaLN_modulation.1.bias"]
			UpperCamelCase_     =state_dict["final_layer.linear.weight"]
			UpperCamelCase_     =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_     =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=A    ,       num_attention_heads=16    ,       activation_fn="gelu-approximate"    ,       num_embeds_ada_norm=1_000    ,       norm_type="ada_norm_zero"    ,       norm_elementwise_affine=A    ,       )
			transformer.load_state_dict(A    ,       strict=A			)

			UpperCamelCase_     =DDIMScheduler(
			    num_train_timesteps=1_000    ,       beta_schedule="linear"    ,       prediction_type="epsilon"    ,       clip_sample=A    ,       )

			UpperCamelCase_     =AutoencoderKL.from_pretrained(args.vae_model			)

			UpperCamelCase_     =DiTPipeline(transformer=A    ,       vae=A    ,       scheduler=A			)

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


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

				A_			  =    parser.parse_args()
				main(args)








"""simple docstring"""






from .hash_table import HashTable
from .number_theory.prime_numbers import is_prime, next_prime
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  def __init__(      self:		List[Any]				,			*UpperCamelCase_:		Dict				,			**UpperCamelCase_:		Optional[int]					):
     super().__init__(*UpperCamelCase_				,			**UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		Any				,			UpperCamelCase_:		Optional[int]				,			UpperCamelCase_:		Optional[int]					):
     UpperCamelCase_     =(
         next_prime(value % self.size_table					)
         if not is_prime(value % self.size_table					)
         else value % self.size_table
     )  # gt = bigger than
     return next_prime_gt - (data % next_prime_gt)
  def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		List[str]				,			UpperCamelCase_:		str				,			UpperCamelCase_:		Tuple					):
     return (increment * self.__hash_function_a(UpperCamelCase_				,			UpperCamelCase_					)) % self.size_table





  def 					UpperCamelCase__   (      self:		str				,			UpperCamelCase_:		int				,			UpperCamelCase_:		str=None					):
     UpperCamelCase_     =1
     UpperCamelCase_     =self.hash_function(UpperCamelCase_					)

     while self.values[new_key] is not None and self.values[new_key] != key:
        UpperCamelCase_     =(
            self.__hash_double_function(UpperCamelCase_				,			UpperCamelCase_				,			UpperCamelCase_					)
            if self.balanced_factor() >= self.lim_charge
            else None
        )
        if new_key is None:
           break
        else:
           i += 1

     return new_key
