



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





def 						a__     (						_UpperCamelCase     :	Dict							):
   __lowerCamelCase  							=      download_model(pretrained_models[args.image_size]							)

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

      __lowerCamelCase  							=      state_dict[
          F"""blocks.{depth}.adaLN_modulation.1.weight"""
      ]
      __lowerCamelCase  							=      state_dict[
          F"""blocks.{depth}.adaLN_modulation.1.bias"""
      ]

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

      __lowerCamelCase  							=      q
      __lowerCamelCase  							=      q_bias
      __lowerCamelCase  							=      k
      __lowerCamelCase  							=      k_bias
      __lowerCamelCase  							=      v
      __lowerCamelCase  							=      v_bias

      __lowerCamelCase  							=      state_dict[
          F"""blocks.{depth}.attn.proj.weight"""
      ]
      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.attn.proj.bias"""]

      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
      __lowerCamelCase  							=      state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
      __lowerCamelCase  							=      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'''							)

   __lowerCamelCase  							=      state_dict['''final_layer.adaLN_modulation.1.weight''']
   __lowerCamelCase  							=      state_dict['''final_layer.adaLN_modulation.1.bias''']
   __lowerCamelCase  							=      state_dict['''final_layer.linear.weight''']
   __lowerCamelCase  							=      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
   __lowerCamelCase  							=      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=10_00	,norm_type='''ada_norm_zero'''	,norm_elementwise_affine=_UpperCamelCase	,)
   transformer.load_state_dict(_UpperCamelCase	,strict=_UpperCamelCase							)

   __lowerCamelCase  							=      DDIMScheduler(
       num_train_timesteps=10_00	,beta_schedule='''linear'''	,prediction_type='''epsilon'''	,clip_sample=_UpperCamelCase	,)

   __lowerCamelCase  							=      AutoencoderKL.from_pretrained(args.vae_model							)

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

   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)











from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


a_								=   logging.get_logger(__name__)

a_								=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

a_								=   {
    """vocab_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/vocab.json"""},
    """merges_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/merges.txt"""},
    """tokenizer_file""": {"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/tokenizer.json"""},
}

a_								=   {
    """openai-gpt""": 512,
}



class 	__lowerCAmelCase       (		lowerCAmelCase__				):
 lowerCAmelCase__       =					VOCAB_FILES_NAMES
 lowerCAmelCase__       =					PRETRAINED_VOCAB_FILES_MAP
 lowerCAmelCase__       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowerCAmelCase__       =					["""input_ids""", """attention_mask"""]
 lowerCAmelCase__       =					OpenAIGPTTokenizer




 def __init__(  self		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase=None		,			__UpperCAmelCase="<unk>"		,			**__UpperCAmelCase			):



    '''simple docstring'''
    super().__init__(__UpperCAmelCase		,			__UpperCAmelCase		,			tokenizer_file=__UpperCAmelCase		,			unk_token=__UpperCAmelCase		,			**__UpperCAmelCase			)




 @property
 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return True




 def     lowerCamelCase (  self		,			__UpperCAmelCase		,			__UpperCAmelCase = None			):



    '''simple docstring'''
    __lowerCamelCase  							=      self._tokenizer.model.save(__UpperCAmelCase		,			name=__UpperCAmelCase			)
    return tuple(__UpperCAmelCase			)






