






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   lowercase__   (  __snake_case					:							str						):


					'''simple docstring'''


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




def   lowercase__   (  __snake_case					:							List[str]						):


					'''simple docstring'''


					UpperCAmelCase_					:   Optional[Any]   =		download_model(pretrained_models[args.image_size]						)

					UpperCAmelCase_					:   Tuple   =		state_dict['x_embedder.proj.weight']
					UpperCAmelCase_					:   List[str]   =		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_					:   str   =		state_dict[
										    't_embedder.mlp.0.weight'
										]
										UpperCAmelCase_					:   Dict   =		state_dict[
										    't_embedder.mlp.0.bias'
										]
										UpperCAmelCase_					:   int   =		state_dict[
										    't_embedder.mlp.2.weight'
										]
										UpperCAmelCase_					:   Optional[int]   =		state_dict[
										    't_embedder.mlp.2.bias'
										]
										UpperCAmelCase_					:   str   =		state_dict[
										    'y_embedder.embedding_table.weight'
										]

										UpperCAmelCase_					:   List[Any]   =		state_dict[
										    F"blocks.{depth}.adaLN_modulation.1.weight"
										]
										UpperCAmelCase_					:   List[str]   =		state_dict[
										    F"blocks.{depth}.adaLN_modulation.1.bias"
										]

										UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[int]   =		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.weight"]  , 3  , dim=0						)
										UpperCAmelCase_ ,						UpperCAmelCase_ ,						UpperCAmelCase_					:   Optional[Any]   =		torch.chunk(state_dict[F"blocks.{depth}.attn.qkv.bias"]  , 3  , dim=0						)

										UpperCAmelCase_					:   Optional[Any]   =		q
										UpperCAmelCase_					:   Dict   =		q_bias
										UpperCAmelCase_					:   List[Any]   =		k
										UpperCAmelCase_					:   Tuple   =		k_bias
										UpperCAmelCase_					:   Optional[Any]   =		v
										UpperCAmelCase_					:   Union[str, Any]   =		v_bias

										UpperCAmelCase_					:   Tuple   =		state_dict[
										    F"blocks.{depth}.attn.proj.weight"
										]
										UpperCAmelCase_					:   Dict   =		state_dict[F"blocks.{depth}.attn.proj.bias"]

										UpperCAmelCase_					:   int   =		state_dict[F"blocks.{depth}.mlp.fc1.weight"]
										UpperCAmelCase_					:   int   =		state_dict[F"blocks.{depth}.mlp.fc1.bias"]
										UpperCAmelCase_					:   List[Any]   =		state_dict[F"blocks.{depth}.mlp.fc2.weight"]
										UpperCAmelCase_					:   Optional[int]   =		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_					:   int   =		state_dict['final_layer.adaLN_modulation.1.weight']
					UpperCAmelCase_					:   List[Any]   =		state_dict['final_layer.adaLN_modulation.1.bias']
					UpperCAmelCase_					:   List[Any]   =		state_dict['final_layer.linear.weight']
					UpperCAmelCase_					:   Dict   =		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_					:   str   =		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=1_000  , norm_type='ada_norm_zero'  , norm_elementwise_affine=lowercase__  , )
					transformer.load_state_dict(lowercase__  , strict=lowercase__						)

					UpperCAmelCase_					:   Tuple   =		DDIMScheduler(
					    num_train_timesteps=1_000  , beta_schedule='linear'  , prediction_type='epsilon'  , clip_sample=lowercase__  , )

					UpperCAmelCase_					:   int   =		AutoencoderKL.from_pretrained(args.vae_model						)

					UpperCAmelCase_					:   Dict   =		DiTPipeline(transformer=lowercase__  , vae=lowercase__  , scheduler=lowercase__						)

					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)














import torch.nn as nn

from .imports import is_fpa_available


if is_fpa_available():
		import transformer_engine.pytorch as te




def   lowercase__   (  __snake_case					:							List[str]  , __snake_case					:							List[Any]=True  , __snake_case					:							str=True  , __snake_case					:							Tuple=True						):


					'''simple docstring'''


					if not is_fpa_available():
										raise ImportError('Using `convert_model` requires transformer_engine to be installed.'						)
					for name, module in model.named_children():
										if isinstance(__snake_case  , nn.Linear						) and to_transformer_engine and _convert_linear:
															# Return early if the linear layer weights are not multiples of 16
															if any(p % 16 != 0 for p in module.weight.shape						):
																				return
															UpperCAmelCase_					:   Optional[int]   =		module.bias is not None
															UpperCAmelCase_					:   Optional[int]   =		te.Linear(
															    module.in_features  , module.out_features  , bias=__snake_case  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   List[str]   =		module.weight.data.clone()
															if has_bias:
																				UpperCAmelCase_					:   List[Any]   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , nn.LayerNorm						) and to_transformer_engine and _convert_ln:
															UpperCAmelCase_					:   int   =		te.LayerNorm(module.normalized_shape[0]  , eps=module.eps  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   Any   =		module.weight.data.clone()
															UpperCAmelCase_					:   Any   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , te.Linear						) and not to_transformer_engine and _convert_linear:
															UpperCAmelCase_					:   int   =		module.bias is not None
															UpperCAmelCase_					:   Tuple   =		nn.Linear(
															    module.in_features  , module.out_features  , bias=__snake_case  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   str   =		module.weight.data.clone()
															if has_bias:
																				UpperCAmelCase_					:   Dict   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										elif isinstance(__snake_case  , te.LayerNorm						) and not to_transformer_engine and _convert_ln:
															UpperCAmelCase_					:   List[Any]   =		nn.LayerNorm(module.normalized_shape[0]  , eps=module.eps  , params_dtype=module.weight.dtype						)
															UpperCAmelCase_					:   List[str]   =		module.weight.data.clone()
															UpperCAmelCase_					:   Tuple   =		module.bias.data.clone()

															setattr(__snake_case  , __snake_case  , __snake_case						)
										else:
															convert_model(
															    __snake_case  , to_transformer_engine=__snake_case  , _convert_linear=_convert_linear  , _convert_ln=_convert_ln  , )






def   lowercase__   (  __snake_case					:							List[Any]						):


					'''simple docstring'''


					if not is_fpa_available():
										raise ImportError('Using `has_transformer_engine_layers` requires transformer_engine to be installed.'						)
					for m in model.modules():
										if isinstance(__snake_case  , (te.LayerNorm, te.Linear)						):
															return True
					return False






