






import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


UpperCAmelCase_					:		List[Any]           = {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[Any]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      f"""pretrained_models/{model_name}"""
	if not os.path.isfile(UpperCAmelCase_   ):
		os.makedirs("""pretrained_models"""      ,     exist_ok=UpperCAmelCase_   )
		UpperCamelCase	:Tuple		=      f"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
		download_url(UpperCAmelCase_      ,     """pretrained_models"""   )
	UpperCamelCase	:List[str]		=      torch.load(UpperCAmelCase_      ,     map_location=lambda __magic_name__      ,     __magic_name__   : storage   )
	return model

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    List[str]   )						->     Tuple:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      download_model(pretrained_models[args.image_size]   )

	UpperCamelCase	:List[Any]		=      state_dict['x_embedder.proj.weight']
	UpperCamelCase	:Any		=      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	:Optional[Any]		=      state_dict[
		    't_embedder.mlp.0.weight'
		]
		UpperCamelCase	:Union[str, Any]		=      state_dict[
		    't_embedder.mlp.0.bias'
		]
		UpperCamelCase	:str		=      state_dict[
		    't_embedder.mlp.2.weight'
		]
		UpperCamelCase	:str		=      state_dict[
		    't_embedder.mlp.2.bias'
		]
		UpperCamelCase	:str		=      state_dict[
		    'y_embedder.embedding_table.weight'
		]

		UpperCamelCase	:Optional[Any]		=      state_dict[
		    f"""blocks.{depth}.adaLN_modulation.1.weight"""
		]
		UpperCamelCase	:Union[str, Any]		=      state_dict[
		    f"""blocks.{depth}.adaLN_modulation.1.bias"""
		]

		UpperCamelCase	:Dict		=      torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.weight"""]      ,     3      ,     dim=0   )
		UpperCamelCase	:List[str]		=      torch.chunk(state_dict[f"""blocks.{depth}.attn.qkv.bias"""]      ,     3      ,     dim=0   )

		UpperCamelCase	:List[str]		=      q
		UpperCamelCase	:List[Any]		=      q_bias
		UpperCamelCase	:int		=      k
		UpperCamelCase	:Optional[Any]		=      k_bias
		UpperCamelCase	:List[str]		=      v
		UpperCamelCase	:str		=      v_bias

		UpperCamelCase	:List[Any]		=      state_dict[
		    f"""blocks.{depth}.attn.proj.weight"""
		]
		UpperCamelCase	:Any		=      state_dict[f"""blocks.{depth}.attn.proj.bias"""]

		UpperCamelCase	:Tuple		=      state_dict[f"""blocks.{depth}.mlp.fc1.weight"""]
		UpperCamelCase	:Dict		=      state_dict[f"""blocks.{depth}.mlp.fc1.bias"""]
		UpperCamelCase	:List[str]		=      state_dict[f"""blocks.{depth}.mlp.fc2.weight"""]
		UpperCamelCase	:Any		=      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	:str		=      state_dict['final_layer.adaLN_modulation.1.weight']
	UpperCamelCase	:Optional[Any]		=      state_dict['final_layer.adaLN_modulation.1.bias']
	UpperCamelCase	:Union[str, Any]		=      state_dict['final_layer.linear.weight']
	UpperCamelCase	:Union[str, Any]		=      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	:Optional[int]		=      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	:str		=      DDIMScheduler(
	    num_train_timesteps=1000      ,     beta_schedule="""linear"""      ,     prediction_type="""epsilon"""      ,     clip_sample=UpperCAmelCase_      ,     )

	UpperCamelCase	:List[Any]		=      AutoencoderKL.from_pretrained(args.vae_model   )

	UpperCamelCase	:Tuple		=      DiTPipeline(transformer=UpperCAmelCase_      ,     vae=UpperCAmelCase_      ,     scheduler=UpperCAmelCase_   )

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


if __name__ == "__main__":
	UpperCAmelCase_					:		List[str]           = 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.'''
	)

	UpperCAmelCase_					:		Dict           = parser.parse_args()
	main(args)



import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Tuple						)    ->					str:




	'''simple docstring'''




	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		Tuple								=    pt_attn_layer.in_proj_weight.chunk(3				,							dim=0						)
	_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase :		str								=    pt_attn_layer.in_proj_bias.chunk(3				,							dim=0						)

	_UpperCamelCase :		Dict								=    pt_attn_layer.out_proj.weight
	_UpperCamelCase :		Tuple								=    pt_attn_layer.out_proj.bias

	_UpperCamelCase :		Tuple								=    q_proj
	_UpperCamelCase :		Dict								=    q_proj_bias

	_UpperCamelCase :		Dict								=    k_proj
	_UpperCamelCase :		str								=    k_proj_bias

	_UpperCamelCase :		Dict								=    v_proj
	_UpperCamelCase :		Optional[int]								=    v_proj_bias

	_UpperCamelCase :		Any								=    out_proj_weights
	_UpperCamelCase :		Optional[int]								=    out_proj_bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					List[Any]:




	'''simple docstring'''




	copy_linear(hf_mlp.fca				,							pt_mlp.c_fc						)
	copy_linear(hf_mlp.fca				,							pt_mlp.c_proj						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  List[str]				,							UpperCAmelCase_				:  List[str]						)    ->					List[Any]:




	'''simple docstring'''




	_UpperCamelCase :		int								=    pt_linear.weight
	_UpperCamelCase :		int								=    pt_linear.bias






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Union[str, Any]						)    ->					Dict:




	'''simple docstring'''




	copy_linear(hf_layer.layer_norma				,							pt_layer.ln_a						)
	copy_linear(hf_layer.layer_norma				,							pt_layer.ln_a						)

	# copy MLP
	copy_mlp(hf_layer.mlp				,							pt_layer.mlp						)

	# copy attn
	copy_attn_layer(hf_layer.self_attn				,							pt_layer.attn						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[Any]				,							UpperCAmelCase_				:  List[str]						)    ->					Dict:




	'''simple docstring'''




	for hf_layer, pt_layer in zip(UpperCAmelCase_				,							UpperCAmelCase_						):
		copy_layer(UpperCAmelCase_				,							UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  Any				,							UpperCAmelCase_				:  Optional[Any]						)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[int]								=    pt_model.token_embedding.weight
	_UpperCamelCase :		Tuple								=    pt_model.positional_embedding

	# copy layer norm
	copy_linear(hf_encoder.final_layer_norm				,							pt_model.ln_final						)

	# copy hidden layers
	copy_layers(hf_encoder.encoder.layers				,							pt_model.transformer.resblocks						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  int				,							UpperCAmelCase_				:  Tuple						)    ->					Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase :		Any								=    pt_model.text_projection.data.T

	# copy text encoder
	copy_encoder(hf_model.text_model				,							UpperCAmelCase_						)






def        lowerCamelCase_  (				UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  Dict						)    ->					Optional[int]:




	'''simple docstring'''




	_UpperCamelCase :		str								=    pt_model.visual.proj.data.T

	# copy layer norms
	copy_linear(hf_model.vision_model.pre_layrnorm				,							pt_model.visual.ln_pre						)
	copy_linear(hf_model.vision_model.post_layernorm				,							pt_model.visual.ln_post						)

	# copy embeds
	_UpperCamelCase :		Tuple								=    pt_model.visual.conva.weight.data
	_UpperCamelCase :		Dict								=    pt_model.visual.class_embedding
	_UpperCamelCase :		str								=    pt_model.visual.positional_embedding.data

	# copy encoder
	copy_layers(hf_model.vision_model.encoder.layers				,							pt_model.visual.transformer.resblocks						)






@torch.no_grad()
def        lowerCamelCase_  (				UpperCAmelCase_				:  Dict				,							UpperCAmelCase_				:  str				,							UpperCAmelCase_				:  List[str]=None						)    ->					Union[str, Any]:




	'''simple docstring'''




	if config_path is not None:
		_UpperCamelCase :		Dict								=    CLIPConfig.from_pretrained(UpperCAmelCase_						)
	else:
		_UpperCamelCase :		Tuple								=    CLIPConfig(projection_dim=5_1_2				,							text_config={}				,							vision_config={}						)

	_UpperCamelCase :		Tuple								=    CLIPModel(UpperCAmelCase_						).eval()

	_UpperCamelCase						,		_UpperCamelCase :		Optional[Any]								=    load(UpperCAmelCase_				,							device='cpu'				,							jit=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.eval()

	copy_text_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	copy_vison_model_and_projection(UpperCAmelCase_				,							UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    pt_model.logit_scale

	_UpperCamelCase :		Optional[int]								=    torch.arange(0				,							7_7						).unsqueeze(0						)
	_UpperCamelCase :		Dict								=    torch.randn(1				,							3				,							2_2_4				,							2_2_4						)

	_UpperCamelCase :		Optional[Any]								=    hf_model(input_ids=UpperCAmelCase_				,							pixel_values=UpperCAmelCase_				,							return_dict=UpperCAmelCase_						)
	_UpperCamelCase :		Optional[Any]								=    hf_outputs.logits_per_image
	_UpperCamelCase :		Optional[int]								=    hf_outputs.logits_per_text
	_UpperCamelCase						,		_UpperCamelCase :		int								=    pt_model(UpperCAmelCase_				,							UpperCAmelCase_						)

	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)
	assert torch.allclose(UpperCAmelCase_				,							UpperCAmelCase_				,							atol=1e-3						)

	hf_model.save_pretrained(UpperCAmelCase_						)


if __name__ == "__main__":
							lowerCAmelCase__             =		argparse.ArgumentParser()
							parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							lowerCAmelCase__             =		parser.parse_args()

							convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




