




import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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





def 							A__   (snake_case      :		List[Any]							)    ->	Dict:
					__UpperCamelCase				:  Optional[int]								=			F'''pretrained_models/{model_name}'''
					if not os.path.isfile(snake_case							):
										os.makedirs("""pretrained_models"""		,  exist_ok=snake_case							)
										__UpperCamelCase				:  Optional[int]								=			F'''https://dl.fbaipublicfiles.com/DiT/models/{model_name}'''
										download_url(snake_case		,  """pretrained_models"""							)
					__UpperCamelCase				:  Union[str, Any]								=			torch.load(snake_case		,  map_location=lambda snake_case		,  snake_case							: storage							)
					return model





def 							A__   (snake_case      :		Optional[Any]							)    ->	Union[str, Any]:
					__UpperCamelCase				:  Any								=			download_model(pretrained_models[args.image_size]							)

					__UpperCamelCase				:  Optional[Any]								=			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				:  Tuple								=			state_dict[
										    """t_embedder.mlp.0.weight"""
										]
										__UpperCamelCase				:  Optional[Any]								=			state_dict[
										    """t_embedder.mlp.0.bias"""
										]
										__UpperCamelCase				:  Any								=			state_dict[
										    """t_embedder.mlp.2.weight"""
										]
										__UpperCamelCase				:  Optional[Any]								=			state_dict[
										    """t_embedder.mlp.2.bias"""
										]
										__UpperCamelCase				:  Optional[int]								=			state_dict[
										    """y_embedder.embedding_table.weight"""
										]

										__UpperCamelCase				:  List[str]								=			state_dict[
										    F'''blocks.{depth}.adaLN_modulation.1.weight'''
										]
										__UpperCamelCase				:  Union[str, Any]								=			state_dict[
										    F'''blocks.{depth}.adaLN_modulation.1.bias'''
										]

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

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

										__UpperCamelCase				:  Tuple								=			state_dict[
										    F'''blocks.{depth}.attn.proj.weight'''
										]
										__UpperCamelCase				:  Union[str, Any]								=			state_dict[F'''blocks.{depth}.attn.proj.bias''']

										__UpperCamelCase				:  List[Any]								=			state_dict[F'''blocks.{depth}.mlp.fc1.weight''']
										__UpperCamelCase				:  List[str]								=			state_dict[F'''blocks.{depth}.mlp.fc1.bias''']
										__UpperCamelCase				:  Optional[Any]								=			state_dict[F'''blocks.{depth}.mlp.fc2.weight''']
										__UpperCamelCase				:  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				:  str								=			state_dict["""final_layer.adaLN_modulation.1.weight"""]
					__UpperCamelCase				:  List[str]								=			state_dict["""final_layer.adaLN_modulation.1.bias"""]
					__UpperCamelCase				:  Union[str, Any]								=			state_dict["""final_layer.linear.weight"""]
					__UpperCamelCase				:  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[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=snake_case		,  num_attention_heads=16		,  activation_fn="""gelu-approximate"""		,  num_embeds_ada_norm=10_00		,  norm_type="""ada_norm_zero"""		,  norm_elementwise_affine=snake_case		,  )
					transformer.load_state_dict(snake_case		,  strict=snake_case							)

					__UpperCamelCase				:  Union[str, Any]								=			DDIMScheduler(
					    num_train_timesteps=10_00		,  beta_schedule="""linear"""		,  prediction_type="""epsilon"""		,  clip_sample=snake_case		,  )

					__UpperCamelCase				:  Optional[int]								=			AutoencoderKL.from_pretrained(args.vae_model							)

					__UpperCamelCase				:  str								=			DiTPipeline(transformer=snake_case		,  vae=snake_case		,  scheduler=snake_case							)

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


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

	a__		    =    parser.parse_args()
	main(args)












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a__		    =    logging.get_logger(__name__)

a__		    =    {
    '''apple/mobilevit-small''': '''https://huggingface.co/apple/mobilevit-small/resolve/main/config.json''',
    '''apple/mobilevit-x-small''': '''https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json''',
    '''apple/mobilevit-xx-small''': '''https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json''',
    '''apple/deeplabv3-mobilevit-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-x-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json'''
    ),
    '''apple/deeplabv3-mobilevit-xx-small''': (
        '''https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json'''
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}


class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Any			  =		'mobilevit'






	def __init__(				self		:	int				,    lowerCAmelCase		:	Dict=3				,    lowerCAmelCase		:	Dict=256				,    lowerCAmelCase		:	Any=2				,    lowerCAmelCase		:	Any=[144, 192, 240]				,    lowerCAmelCase		:	int=[16, 32, 64, 96, 128, 160, 640]				,    lowerCAmelCase		:	List[Any]=4				,    lowerCAmelCase		:	Tuple=2.0				,    lowerCAmelCase		:	Tuple=4.0				,    lowerCAmelCase		:	List[str]="silu"				,    lowerCAmelCase		:	Tuple=3				,    lowerCAmelCase		:	Tuple=32				,    lowerCAmelCase		:	Any=0.1				,    lowerCAmelCase		:	List[str]=0.0				,    lowerCAmelCase		:	Union[str, Any]=0.1				,    lowerCAmelCase		:	List[str]=0.02				,    lowerCAmelCase		:	Optional[Any]=1E-5				,    lowerCAmelCase		:	Union[str, Any]=True				,    lowerCAmelCase		:	List[str]=256				,    lowerCAmelCase		:	List[Any]=[6, 12, 18]				,    lowerCAmelCase		:	Tuple=0.1				,    lowerCAmelCase		:	List[str]=255				,    **lowerCAmelCase		:	Union[str, Any]				,    )  ->					Optional[Any]:







						"""simple docstring"""







						super().__init__(**lowerCAmelCase  )

						__UpperCamelCase				:  Tuple								=			num_channels
						__UpperCamelCase				:  Union[str, Any]								=			image_size
						__UpperCamelCase				:  List[Any]								=			patch_size
						__UpperCamelCase				:  Optional[int]								=			hidden_sizes
						__UpperCamelCase				:  Union[str, Any]								=			neck_hidden_sizes
						__UpperCamelCase				:  List[str]								=			num_attention_heads
						__UpperCamelCase				:  Union[str, Any]								=			mlp_ratio
						__UpperCamelCase				:  Tuple								=			expand_ratio
						__UpperCamelCase				:  Optional[Any]								=			hidden_act
						__UpperCamelCase				:  int								=			conv_kernel_size
						__UpperCamelCase				:  Any								=			output_stride
						__UpperCamelCase				:  int								=			hidden_dropout_prob
						__UpperCamelCase				:  Union[str, Any]								=			attention_probs_dropout_prob
						__UpperCamelCase				:  Dict								=			classifier_dropout_prob
						__UpperCamelCase				:  Any								=			initializer_range
						__UpperCamelCase				:  List[str]								=			layer_norm_eps
						__UpperCamelCase				:  Union[str, Any]								=			qkv_bias

						# decode head attributes for semantic segmentation
						__UpperCamelCase				:  Any								=			aspp_out_channels
						__UpperCamelCase				:  Union[str, Any]								=			atrous_rates
						__UpperCamelCase				:  Union[str, Any]								=			aspp_dropout_prob
						__UpperCamelCase				:  List[str]								=			semantic_loss_ignore_index






class       SCREAMING_SNAKE_CASE_							(					_UpperCamelCase      ):



	"""simple docstring"""


	__magic_name__      :					Optional[Any]			  =		version.parse('1.11'      )






	@property
	def 				lowerCamelCase__    (				self		:	Any  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						return OrderedDict([("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	List[Any]  )  ->					Mapping[str, Mapping[int, str]]:







						"""simple docstring"""







						if self.task == "image-classification":
											return OrderedDict([("""logits""", {0: """batch"""})]  )
						else:
											return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]  )






	@property
	def 				lowerCamelCase__    (				self		:	Optional[int]  )  ->					float:







						"""simple docstring"""







						return 1E-4






