



import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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





def 				A      (	_UpperCAmelCase							:  Any       )							->					List[Any]:







			'''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 _UpperCAmelCase				,  _UpperCAmelCase       : storage       )
			return model





def 				A      (	_UpperCAmelCase							:  Union[str, Any]       )							->					List[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=_UpperCAmelCase				,  num_attention_heads=16				,  activation_fn='gelu-approximate'				,  num_embeds_ada_norm=1_000				,  norm_type='ada_norm_zero'				,  norm_elementwise_affine=_UpperCAmelCase				,  )
			transformer.load_state_dict(_UpperCAmelCase				,  strict=_UpperCAmelCase       )

			_UpperCAmelCase									=  DDIMScheduler(
			    num_train_timesteps=1_000				,  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__":
							UpperCAmelCase__  =			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."
							)

							UpperCAmelCase__  =			parser.parse_args()
							main(args)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase__  =			{
    "configuration_align": [
        "ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "AlignConfig",
        "AlignTextConfig",
        "AlignVisionConfig",
    ],
    "processing_align": ["AlignProcessor"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__  =			[
							    "ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "AlignModel",
							    "AlignPreTrainedModel",
							    "AlignTextModel",
							    "AlignVisionModel",
							]

if TYPE_CHECKING:
							from .configuration_align import (
							    ALIGN_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    AlignConfig,
							    AlignTextConfig,
							    AlignVisionConfig,
							)
							from .processing_align import AlignProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_align import (
														    ALIGN_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AlignModel,
														    AlignPreTrainedModel,
														    AlignTextModel,
														    AlignVisionModel,
														)

else:
							import sys

							UpperCAmelCase__  =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
