





import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


lowerCAmelCase_												=       {5_1_2: "DiT-XL-2-512x512.pt", 2_5_6: "DiT-XL-2-256x256.pt"}






def   lowerCamelCase_		(       _UpperCamelCase				)	->		Optional[Any]:






				"""simple docstring"""


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






def   lowerCamelCase_		(       _UpperCamelCase				)	->		int:






				"""simple docstring"""


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

				snake_case_		:					int			    =						state_dict['''x_embedder.proj.weight''']
				snake_case_		:					Optional[int]			    =						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				):
								snake_case_		:					Optional[Any]			    =						state_dict[
								    '''t_embedder.mlp.0.weight'''
								]
								snake_case_		:					Optional[Any]			    =						state_dict[
								    '''t_embedder.mlp.0.bias'''
								]
								snake_case_		:					Dict			    =						state_dict[
								    '''t_embedder.mlp.2.weight'''
								]
								snake_case_		:					Optional[Any]			    =						state_dict[
								    '''t_embedder.mlp.2.bias'''
								]
								snake_case_		:					int			    =						state_dict[
								    '''y_embedder.embedding_table.weight'''
								]

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

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

								snake_case_		:					List[str]			    =						q
								snake_case_		:					str			    =						q_bias
								snake_case_		:					Union[str, Any]			    =						k
								snake_case_		:					int			    =						k_bias
								snake_case_		:					Optional[int]			    =						v
								snake_case_		:					str			    =						v_bias

								snake_case_		:					Dict			    =						state_dict[
								    f'''blocks.{depth}.attn.proj.weight'''
								]
								snake_case_		:					Tuple			    =						state_dict[f'''blocks.{depth}.attn.proj.bias''']

								snake_case_		:					Optional[int]			    =						state_dict[f'''blocks.{depth}.mlp.fc1.weight''']
								snake_case_		:					List[Any]			    =						state_dict[f'''blocks.{depth}.mlp.fc1.bias''']
								snake_case_		:					Union[str, Any]			    =						state_dict[f'''blocks.{depth}.mlp.fc2.weight''']
								snake_case_		:					List[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'''				)

				snake_case_		:					Dict			    =						state_dict['''final_layer.adaLN_modulation.1.weight''']
				snake_case_		:					List[str]			    =						state_dict['''final_layer.adaLN_modulation.1.bias''']
				snake_case_		:					int			    =						state_dict['''final_layer.linear.weight''']
				snake_case_		:					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
				snake_case_		:					List[Any]			    =						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				)

				snake_case_		:					Union[str, Any]			    =						DDIMScheduler(
				    num_train_timesteps=1_000				,       beta_schedule='''linear'''				,       prediction_type='''epsilon'''				,       clip_sample=_UpperCamelCase				,       )

				snake_case_		:					Any			    =						AutoencoderKL.from_pretrained(args.vae_model				)

				snake_case_		:					Dict			    =						DiTPipeline(transformer=_UpperCamelCase				,       vae=_UpperCamelCase				,       scheduler=_UpperCamelCase				)

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


if __name__ == "__main__":
	lowerCAmelCase_												=       argparse.ArgumentParser()

	parser.add_argument(
	    '''--image_size''',
	    default=2_5_6,
	    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)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowerCAmelCase_												=       {}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50Tokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       ['''MBart50TokenizerFast''']


if TYPE_CHECKING:
	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa import MBartaaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_mbartaa_fast import MBartaaTokenizerFast

else:
	import sys

	lowerCAmelCase_												=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
