





"""simple docstring"""




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


_lowerCamelCase							=     {512: 'DiT-XL-2-512x512.pt', 256: 'DiT-XL-2-256x256.pt'}






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							str      ):
		SCREAMING_SNAKE_CASE__								= f'''pretrained_models/{model_name}'''
		if not os.path.isfile(SCREAMING_SNAKE_CASE_      ):
				os.makedirs("""pretrained_models"""   ,				exist_ok=SCREAMING_SNAKE_CASE_      )
				SCREAMING_SNAKE_CASE__								= f'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
				download_url(SCREAMING_SNAKE_CASE_   ,				"""pretrained_models"""      )
		SCREAMING_SNAKE_CASE__								= torch.load(SCREAMING_SNAKE_CASE_   ,				map_location=lambda UpperCamelCase__   ,				UpperCamelCase__      : storage      )
		return model






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Dict      ):
		SCREAMING_SNAKE_CASE__								= download_model(pretrained_models[args.image_size]      )

		SCREAMING_SNAKE_CASE__								= state_dict["x_embedder.proj.weight"]
		SCREAMING_SNAKE_CASE__								= 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      ):
				SCREAMING_SNAKE_CASE__								= state_dict[
				    "t_embedder.mlp.0.weight"
				]
				SCREAMING_SNAKE_CASE__								= state_dict[
				    "t_embedder.mlp.0.bias"
				]
				SCREAMING_SNAKE_CASE__								= state_dict[
				    "t_embedder.mlp.2.weight"
				]
				SCREAMING_SNAKE_CASE__								= state_dict[
				    "t_embedder.mlp.2.bias"
				]
				SCREAMING_SNAKE_CASE__								= state_dict[
				    "y_embedder.embedding_table.weight"
				]

				SCREAMING_SNAKE_CASE__								= state_dict[
				    f'''blocks.{depth}.adaLN_modulation.1.weight'''
				]
				SCREAMING_SNAKE_CASE__								= state_dict[
				    f'''blocks.{depth}.adaLN_modulation.1.bias'''
				]

				SCREAMING_SNAKE_CASE__								= torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.weight''']   ,				3   ,				dim=0      )
				SCREAMING_SNAKE_CASE__								= torch.chunk(state_dict[f'''blocks.{depth}.attn.qkv.bias''']   ,				3   ,				dim=0      )

				SCREAMING_SNAKE_CASE__								= q
				SCREAMING_SNAKE_CASE__								= q_bias
				SCREAMING_SNAKE_CASE__								= k
				SCREAMING_SNAKE_CASE__								= k_bias
				SCREAMING_SNAKE_CASE__								= v
				SCREAMING_SNAKE_CASE__								= v_bias

				SCREAMING_SNAKE_CASE__								= state_dict[
				    f'''blocks.{depth}.attn.proj.weight'''
				]
				SCREAMING_SNAKE_CASE__								= state_dict[f'''blocks.{depth}.attn.proj.bias''']

				SCREAMING_SNAKE_CASE__								= state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
				SCREAMING_SNAKE_CASE__								= state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
				SCREAMING_SNAKE_CASE__								= state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
				SCREAMING_SNAKE_CASE__								= 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"""      )

		SCREAMING_SNAKE_CASE__								= state_dict["final_layer.adaLN_modulation.1.weight"]
		SCREAMING_SNAKE_CASE__								= state_dict["final_layer.adaLN_modulation.1.bias"]
		SCREAMING_SNAKE_CASE__								= state_dict["final_layer.linear.weight"]
		SCREAMING_SNAKE_CASE__								= 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
		SCREAMING_SNAKE_CASE__								= 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=SCREAMING_SNAKE_CASE_   ,				num_attention_heads=16   ,				activation_fn="""gelu-approximate"""   ,				num_embeds_ada_norm=1_000   ,				norm_type="""ada_norm_zero"""   ,				norm_elementwise_affine=SCREAMING_SNAKE_CASE_   ,				)
		transformer.load_state_dict(SCREAMING_SNAKE_CASE_   ,				strict=SCREAMING_SNAKE_CASE_      )

		SCREAMING_SNAKE_CASE__								= DDIMScheduler(
		    num_train_timesteps=1_000   ,				beta_schedule="""linear"""   ,				prediction_type="""epsilon"""   ,				clip_sample=SCREAMING_SNAKE_CASE_   ,				)

		SCREAMING_SNAKE_CASE__								= AutoencoderKL.from_pretrained(args.vae_model      )

		SCREAMING_SNAKE_CASE__								= DiTPipeline(transformer=SCREAMING_SNAKE_CASE_   ,				vae=SCREAMING_SNAKE_CASE_   ,				scheduler=SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
	_lowerCamelCase							=     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.'
	)

	_lowerCamelCase							=     parser.parse_args()
	main(args)






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

		# Save pytorch-model
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE__								= pytorch_dump_folder_path + """/""" + CONFIG_NAME
		print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''      )
		torch.save(model.state_dict()   ,				UpperCamelCase__      )
		print(f'''Save configuration file to {pytorch_config_dump_path}'''      )
		with open(UpperCamelCase__   ,				"""w"""   ,				encoding="""utf-8"""      ) as f:
				f.write(config.to_json_string()      )


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)