
import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


snake_case__  							=							{5_12: """DiT-XL-2-512x512.pt""", 2_56: """DiT-XL-2-256x256.pt"""}

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

def        lowerCamelCase_      (			UpperCAmelCase_							:				Union[str, Any]       ):
						lowercase       :    List[Any]											=  download_model(pretrained_models[args.image_size]       )

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

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

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

												lowercase       :    Dict											=  q
												lowercase       :    List[Any]											=  q_bias
												lowercase       :    Tuple											=  k
												lowercase       :    Tuple											=  k_bias
												lowercase       :    Dict											=  v
												lowercase       :    Any											=  v_bias

												lowercase       :    int											=  state_dict[
												    f'''blocks.{depth}.attn.proj.weight'''
												]
												lowercase       :    Any											=  state_dict[f'''blocks.{depth}.attn.proj.bias''']

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

						lowercase       :    Optional[int]											=  state_dict['''final_layer.adaLN_modulation.1.weight''']
						lowercase       :    Union[str, Any]											=  state_dict['''final_layer.adaLN_modulation.1.bias''']
						lowercase       :    Optional[int]											=  state_dict['''final_layer.linear.weight''']
						lowercase       :    Optional[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
						lowercase       :    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=10_00							,			norm_type='''ada_norm_zero'''							,			norm_elementwise_affine=UpperCAmelCase_							,			)
						transformer.load_state_dict(UpperCAmelCase_							,			strict=UpperCAmelCase_       )

						lowercase       :    Any											=  DDIMScheduler(
						    num_train_timesteps=10_00							,			beta_schedule='''linear'''							,			prediction_type='''epsilon'''							,			clip_sample=UpperCAmelCase_							,			)

						lowercase       :    Union[str, Any]											=  AutoencoderKL.from_pretrained(args.vae_model       )

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

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


if __name__ == "__main__":
				snake_case__  							=							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."""
				)

				snake_case__  							=							parser.parse_args()
				main(args)




class 							UpperCAmelCase	:

					def __init__(			self      :					Any ,      lowerCAmelCase      :					int					):
											lowercase       :    Tuple											=  data
											lowercase       :    BinaryTreeNode | None											=  None
											lowercase       :    BinaryTreeNode | None											=  None





def        lowerCamelCase_      (			UpperCAmelCase_							:				BinaryTreeNode | None							,			UpperCAmelCase_							:				int       ):
						if node is None:
												lowercase       :    Union[str, Any]											=  BinaryTreeNode(UpperCAmelCase_       )
												return node

						# binary search tree is not empty,
						# so we will insert it into the tree
						# if new_value is less than value of data in node,
						#  add it to left subtree and proceed recursively
						if new_value < node.data:
												lowercase       :    List[Any]											=  insert(node.left_child							,			UpperCAmelCase_       )
						else:
												# if new_value is greater than value of data in node,
												#  add it to right subtree and proceed recursively
												lowercase       :    Dict											=  insert(node.right_child							,			UpperCAmelCase_       )
						return node

def        lowerCamelCase_      (			UpperCAmelCase_							:				None | BinaryTreeNode       ):  # if node is None,return
						if node:
												lowercase       :    Optional[Any]											=  inorder(node.left_child       )
												lowercase       :    Tuple											=  [*inorder_array, node.data]
												lowercase       :    Optional[Any]											=  inorder_array + inorder(node.right_child       )
						else:
												lowercase       :    Optional[int]											=  []
						return inorder_array

def        lowerCamelCase_      (			):
						lowercase       :    Any											=  insert(UpperCAmelCase_							,			15       )
						insert(UpperCAmelCase_							,			10       )
						insert(UpperCAmelCase_							,			25       )
						insert(UpperCAmelCase_							,			6       )
						insert(UpperCAmelCase_							,			14       )
						insert(UpperCAmelCase_							,			20       )
						insert(UpperCAmelCase_							,			60       )
						return root

def        lowerCamelCase_      (			):
						# main function
						lowercase       :    Any											=  make_tree()
						print('''Printing values of binary search tree in Inorder Traversal.'''       )
						inorder(UpperCAmelCase_       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()


