

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	int:



    """simple docstring"""






    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 SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ : storage )
    return model




def  lowerCAmelCase(			SCREAMING_SNAKE_CASE_ )->	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(2_8 ):
        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=2_8 ,	attention_head_dim=7_2 ,	in_channels=4 ,	out_channels=8 ,	patch_size=2 ,	attention_bias=_UpperCamelCase ,	num_attention_heads=1_6 ,	activation_fn="gelu-approximate" ,	num_embeds_ada_norm=1_0_0_0 ,	norm_type="ada_norm_zero" ,	norm_elementwise_affine=_UpperCamelCase ,	)
    transformer.load_state_dict(_UpperCamelCase ,	strict=_UpperCamelCase )

    UpperCamelCase_      =	DDIMScheduler(
        num_train_timesteps=1_0_0_0 ,	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__":
 SCREAMING_SNAKE_CASE		:Optional[Any]				  =    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."""
 )

 SCREAMING_SNAKE_CASE		:List[str]				  =    parser.parse_args()
 main(args)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[Any]				  =    [
     """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """T5EncoderModel""",
     """T5ForConditionalGeneration""",
     """T5Model""",
     """T5PreTrainedModel""",
     """load_tf_weights_in_t5""",
     """T5ForQuestionAnswering""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """FlaxT5EncoderModel""",
     """FlaxT5ForConditionalGeneration""",
     """FlaxT5Model""",
     """FlaxT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
