




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_lowerCamelCase    				=		{5_12: "DiT-XL-2-512x512.pt", 2_56: "DiT-XL-2-256x256.pt"}




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[int]   )						->							int:
    UpperCAmelCase_											=  f'pretrained_models/{model_name}'
    if not os.path.isfile(_UpperCamelCase   ):
        os.makedirs('''pretrained_models''' ,  exist_ok=_UpperCamelCase   )
        UpperCAmelCase_											=  f'https://dl.fbaipublicfiles.com/DiT/models/{model_name}'
        download_url(_UpperCamelCase ,  '''pretrained_models'''   )
    UpperCAmelCase_											=  torch.load(_UpperCamelCase ,  map_location=lambda __UpperCamelCase ,  __UpperCamelCase   : storage   )
    return model




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Tuple   )						->							Union[str, Any]:
    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=_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   )

    UpperCAmelCase_											=  DDIMScheduler(
        num_train_timesteps=1000 ,  beta_schedule='''linear''' ,  prediction_type='''epsilon''' ,  clip_sample=_UpperCamelCase ,  )

    UpperCAmelCase_											=  AutoencoderKL.from_pretrained(args.vae_model   )

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

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


if __name__ == "__main__":
       _lowerCamelCase    				=		argparse.ArgumentParser()

       parser.add_argument(
           '--image_size',
           default=2_56,
           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.'
       )

       _lowerCamelCase    				=		parser.parse_args()
       main(args)











import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_lowerCamelCase    				=		threading.Lock()
_lowerCamelCase    				=		None

_lowerCamelCase    				=		{
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

_lowerCamelCase    				=		logging.WARNING

_lowerCamelCase    				=		True




def       SCREAMING_SNAKE_CASE			( )						->							List[str]:
    UpperCAmelCase_											=  os.getenv('''DIFFUSERS_VERBOSITY''' ,  __UpperCamelCase   )
    if env_level_str:
        if env_level_str in log_levels:
            return log_levels[env_level_str]
        else:
            logging.getLogger().warning(
                f'Unknown option DIFFUSERS_VERBOSITY={env_level_str}, '
                f'has to be one of: { ", ".join(log_levels.keys()   ) }'   )
    return _default_log_level




def       SCREAMING_SNAKE_CASE			( )						->							str:
    return __name__.split('''.'''   )[0]




def       SCREAMING_SNAKE_CASE			( )						->							logging.Logger:
    return logging.getLogger(_get_library_name()   )




def       SCREAMING_SNAKE_CASE			( )						->							None:
    global _default_handler

    with _lock:
        if _default_handler:
            # This library has already configured the library root logger.
            return
        UpperCAmelCase_											=  logging.StreamHandler()  # Set sys.stderr as stream.
        UpperCAmelCase_											=  sys.stderr.flush

        # Apply our default configuration to the library root logger.
        UpperCAmelCase_											=  _get_library_root_logger()
        library_root_logger.addHandler(_default_handler   )
        library_root_logger.setLevel(_get_default_logging_level()   )
        UpperCAmelCase_											=  False




def       SCREAMING_SNAKE_CASE			( )						->							None:
    global _default_handler

    with _lock:
        if not _default_handler:
            return

        UpperCAmelCase_											=  _get_library_root_logger()
        library_root_logger.removeHandler(_default_handler   )
        library_root_logger.setLevel(logging.NOTSET   )
        UpperCAmelCase_											=  None




def       SCREAMING_SNAKE_CASE			( )						->							Dict:
    return log_levels




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		Optional[str] = None   )						->							logging.Logger:

    if name is None:
        UpperCAmelCase_											=  _get_library_name()

    _configure_library_root_logger()
    return logging.getLogger(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							int:
    _configure_library_root_logger()
    return _get_library_root_logger().getEffectiveLevel()




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		int   )						->							None:
    _configure_library_root_logger()
    _get_library_root_logger().setLevel(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							Optional[Any]:
    return set_verbosity(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							Union[str, Any]:
    return set_verbosity(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							Tuple:
    return set_verbosity(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							int:
    return set_verbosity(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							None:
    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().removeHandler(_default_handler   )




def       SCREAMING_SNAKE_CASE			( )						->							None:
    _configure_library_root_logger()

    assert _default_handler is not None
    _get_library_root_logger().addHandler(_default_handler   )




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		logging.Handler   )						->							None:
    _configure_library_root_logger()

    assert handler is not None
    _get_library_root_logger().addHandler(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( __UpperCamelCase    :		logging.Handler   )						->							None:
    _configure_library_root_logger()

    assert handler is not None and handler not in _get_library_root_logger().handlers
    _get_library_root_logger().removeHandler(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							None:
    _configure_library_root_logger()
    UpperCAmelCase_											=  False




def       SCREAMING_SNAKE_CASE			( )						->							None:
    _configure_library_root_logger()
    UpperCAmelCase_											=  True




def       SCREAMING_SNAKE_CASE			( )						->							None:
    UpperCAmelCase_											=  _get_library_root_logger().handlers

    for handler in handlers:
        UpperCAmelCase_											=  logging.Formatter('''[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s'''   )
        handler.setFormatter(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( )						->							None:
    UpperCAmelCase_											=  _get_library_root_logger().handlers

    for handler in handlers:
        handler.setFormatter(__UpperCamelCase   )




def       SCREAMING_SNAKE_CASE			( self    :		Tuple ,  *__UpperCamelCase    :		Any ,  **__UpperCamelCase    :		Any   )						->							Union[str, Any]:
    UpperCAmelCase_											=  os.getenv('''DIFFUSERS_NO_ADVISORY_WARNINGS''' ,  __UpperCamelCase   )
    if no_advisory_warnings:
        return
    self.warning(*__UpperCamelCase ,  **__UpperCamelCase   )


_lowerCamelCase    				=		warning_advice



class     a  :

     '''simple docstring'''





     def __init__(			self     :    List[Any]      ,					*__snake_case     :    Tuple      ,					**__snake_case     :    str       ):  # pylint: disable=unused-argument
         UpperCAmelCase_											=  args[0] if args else None
     def __iter__(			self     :    Union[str, Any]       ):
         return iter(self._iterator       )
     def __getattr__(			self     :    Tuple      ,					__snake_case     :    str       ):

         def empty_fn(*__snake_case     :    Optional[int]      ,					**__snake_case     :    int       ):  # pylint: disable=unused-argument
             return

         return empty_fn
     def __enter__(			self     :    Optional[Any]       ):
         return self




     def __exit__(			self     :    Union[str, Any]      ,					__snake_case     :    List[Any]      ,					__snake_case     :    List[str]      ,					__snake_case     :    Dict       ):
         return



class     a  :

     '''simple docstring'''





     def __call__(			self     :    Union[str, Any]      ,					*__snake_case     :    List[str]      ,					**__snake_case     :    Dict       ):
         if _tqdm_active:
             return tqdm_lib.tqdm(*__snake_case      ,					**__snake_case       )
         else:
             return EmptyTqdm(*__snake_case      ,					**__snake_case       )
     def 						lowerCamelCase_     (			self     :    int      ,					*__snake_case     :    Dict      ,					**__snake_case     :    List[str]       ):
         UpperCAmelCase_											=  None
         if _tqdm_active:
             return tqdm_lib.tqdm.set_lock(*__snake_case      ,					**__snake_case       )




     def 						lowerCamelCase_     (			self     :    str       ):
         if _tqdm_active:
             return tqdm_lib.tqdm.get_lock()


_lowerCamelCase    				=		_tqdm_cls()




def       SCREAMING_SNAKE_CASE			( )						->							bool:
    global _tqdm_active
    return bool(_tqdm_active   )




def       SCREAMING_SNAKE_CASE			( )						->							List[str]:
    global _tqdm_active
    UpperCAmelCase_											=  True




def       SCREAMING_SNAKE_CASE			( )						->							Optional[int]:
    global _tqdm_active
    UpperCAmelCase_											=  False





