import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCamelCase__       							=      {5_1_2: 'DiT-XL-2-512x512.pt', 2_5_6: 'DiT-XL-2-256x256.pt'}




def      lowerCAmelCase_				(  __A							)  ->       Dict:







      '''simple docstring'''


      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      lowerCAmelCase_				(  __A							)  ->       Union[str, Any]:







      '''simple docstring'''


      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__":
      UpperCamelCase__       							=      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__       							=      parser.parse_args()
      main(args)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__       							=      logging.get_logger(__name__)

UpperCamelCase__       							=      {
    'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/config.json'
    # See all CPMAnt models at https://huggingface.co/models?filter=cpmant
}





class   A							(       UpperCAmelCase_  ):
   __UpperCAmelCase				:				int          =    'cpmant'


   def __init__(self  :		Optional[Any] ,						__UpperCAmelCase  :		int = 3_0_7_2_0 ,						__UpperCAmelCase  :		int = 4_0_9_6 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 1_2_8 ,						__UpperCAmelCase  :		int = 1_0_2_4_0 ,						__UpperCAmelCase  :		int = 4_8 ,						__UpperCAmelCase  :		int = 0.0 ,						__UpperCAmelCase  :		int = 5_1_2 ,						__UpperCAmelCase  :		int = 2_0_4_8 ,						__UpperCAmelCase  :		int = 1E-6 ,						__UpperCAmelCase  :		float = 1.0 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		int = 3_2 ,						__UpperCAmelCase  :		bool = True ,						**__UpperCAmelCase  :		Dict ,						)		->       int:
         """simple docstring"""



         super().__init__(**__UpperCAmelCase	)
         UpperCAmelCase__			=						prompt_types
         UpperCAmelCase__			=						prompt_length
         UpperCAmelCase__			=						segment_types
         UpperCAmelCase__			=						hidden_size
         UpperCAmelCase__			=						num_attention_heads
         UpperCAmelCase__			=						dim_head
         UpperCAmelCase__			=						dim_ff
         UpperCAmelCase__			=						num_hidden_layers
         UpperCAmelCase__			=						position_bias_num_buckets
         UpperCAmelCase__			=						position_bias_max_distance
         UpperCAmelCase__			=						dropout_p
         UpperCAmelCase__			=						eps
         UpperCAmelCase__			=						use_cache
         UpperCAmelCase__			=						vocab_size
         UpperCAmelCase__			=						init_std



