


from typing import List, Union

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


UpperCamelCase			:					str         =							logging.get_logger(__name__)

UpperCamelCase			:					Tuple         =							{
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}

class      __snake_case				(   _a):
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			"led"
					SCREAMING_SNAKE_CASE_		:       Optional[Any]								=			{
					    "num_attention_heads": "encoder_attention_heads",
					    "hidden_size": "d_model",
					    "attention_probs_dropout_prob": "attention_dropout",
					    "initializer_range": "init_std",
					}
					def __init__(						self       :						Optional[Any]	,      _lowerCAmelCase       :						Tuple=5_0_2_6_5	,      _lowerCAmelCase       :						Any=1_6_3_8_4	,      _lowerCAmelCase       :						List[Any]=1_0_2_4	,      _lowerCAmelCase       :						List[Any]=1_2	,      _lowerCAmelCase       :						List[str]=4_0_9_6	,      _lowerCAmelCase       :						str=1_6	,      _lowerCAmelCase       :						Any=1_2	,      _lowerCAmelCase       :						Optional[int]=4_0_9_6	,      _lowerCAmelCase       :						Optional[Any]=1_6	,      _lowerCAmelCase       :						List[str]=0.0	,      _lowerCAmelCase       :						List[str]=0.0	,      _lowerCAmelCase       :						int=True	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						int="gelu"	,      _lowerCAmelCase       :						List[str]=1_0_2_4	,      _lowerCAmelCase       :						int=0.1	,      _lowerCAmelCase       :						str=0.0	,      _lowerCAmelCase       :						Optional[Any]=0.0	,      _lowerCAmelCase       :						Optional[int]=0.02	,      _lowerCAmelCase       :						Optional[int]=2	,      _lowerCAmelCase       :						int=0.0	,      _lowerCAmelCase       :						int=1	,      _lowerCAmelCase       :						Tuple=0	,      _lowerCAmelCase       :						str=2	,      _lowerCAmelCase       :						Union[List[int], int] = 5_1_2	,      **_lowerCAmelCase       :						Any	,      )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      vocab_size
											__magic_name__					=      max_encoder_position_embeddings
											__magic_name__					=      max_decoder_position_embeddings
											__magic_name__					=      d_model
											__magic_name__					=      encoder_ffn_dim
											__magic_name__					=      encoder_layers
											__magic_name__					=      encoder_attention_heads
											__magic_name__					=      decoder_ffn_dim
											__magic_name__					=      decoder_layers
											__magic_name__					=      decoder_attention_heads
											__magic_name__					=      dropout
											__magic_name__					=      attention_dropout
											__magic_name__					=      activation_dropout
											__magic_name__					=      activation_function
											__magic_name__					=      init_std
											__magic_name__					=      encoder_layerdrop
											__magic_name__					=      decoder_layerdrop
											__magic_name__					=      classifier_dropout
											__magic_name__					=      use_cache
											__magic_name__					=      encoder_layers
											__magic_name__					=      attention_window

											super().__init__(
											    pad_token_id=_lowerCAmelCase	,      bos_token_id=_lowerCAmelCase	,      eos_token_id=_lowerCAmelCase	,      is_encoder_decoder=_lowerCAmelCase	,      decoder_start_token_id=_lowerCAmelCase	,      **_lowerCAmelCase	,      )








import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Any):



  '''simple docstring'''






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


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple):



  '''simple docstring'''






  __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=__lowercase  ,				num_attention_heads=16  ,				activation_fn='''gelu-approximate'''  ,				num_embeds_ada_norm=10_00  ,				norm_type='''ada_norm_zero'''  ,				norm_elementwise_affine=__lowercase  ,				)
  transformer.load_state_dict(__lowercase  ,				strict=__lowercase)

  __lowerCAmelCase								=					DDIMScheduler(
      num_train_timesteps=10_00  ,				beta_schedule='''linear'''  ,				prediction_type='''epsilon'''  ,				clip_sample=__lowercase  ,				)

  __lowerCAmelCase								=					AutoencoderKL.from_pretrained(args.vae_model)

  __lowerCAmelCase								=					DiTPipeline(transformer=__lowercase  ,				vae=__lowercase  ,				scheduler=__lowercase)

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


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

      __lowercase				    =      parser.parse_args()
      main(args)
