"""simple docstring"""

import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


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


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[Any]			):
							"""simple docstring"""


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


def    lowerCAmelCase			(__UpperCamelCase							:    Optional[int]			):
							"""simple docstring"""


							__UpperCamelCase							=download_model(pretrained_models[args.image_size]			)

							__UpperCamelCase							=state_dict['''x_embedder.proj.weight''']
							__UpperCamelCase							=state_dict['''x_embedder.proj.bias''']
							state_dict.pop('''x_embedder.proj.weight'''			)
							state_dict.pop('''x_embedder.proj.bias'''			)

							for depth in range(2_8			):
														__UpperCamelCase							=state_dict[
														    '''t_embedder.mlp.0.weight'''
														]
														__UpperCamelCase							=state_dict[
														    '''t_embedder.mlp.0.bias'''
														]
														__UpperCamelCase							=state_dict[
														    '''t_embedder.mlp.2.weight'''
														]
														__UpperCamelCase							=state_dict[
														    '''t_embedder.mlp.2.bias'''
														]
														__UpperCamelCase							=state_dict[
														    '''y_embedder.embedding_table.weight'''
														]

														__UpperCamelCase							=state_dict[
														    F"""blocks.{depth}.adaLN_modulation.1.weight"""
														]
														__UpperCamelCase							=state_dict[
														    F"""blocks.{depth}.adaLN_modulation.1.bias"""
														]

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

														__UpperCamelCase							=q
														__UpperCamelCase							=q_bias
														__UpperCamelCase							=k
														__UpperCamelCase							=k_bias
														__UpperCamelCase							=v
														__UpperCamelCase							=v_bias

														__UpperCamelCase							=state_dict[
														    F"""blocks.{depth}.attn.proj.weight"""
														]
														__UpperCamelCase							=state_dict[F"""blocks.{depth}.attn.proj.bias"""]

														__UpperCamelCase							=state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
														__UpperCamelCase							=state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
														__UpperCamelCase							=state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
														__UpperCamelCase							=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							=state_dict['''final_layer.adaLN_modulation.1.weight''']
							__UpperCamelCase							=state_dict['''final_layer.adaLN_modulation.1.bias''']
							__UpperCamelCase							=state_dict['''final_layer.linear.weight''']
							__UpperCamelCase							=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							=TransformeraDModel(
							    sample_size=args.image_size // 8							,					num_layers=2_8							,					attention_head_dim=7_2							,					in_channels=4							,					out_channels=8							,					patch_size=2							,					attention_bias=__UpperCamelCase							,					num_attention_heads=1_6							,					activation_fn='''gelu-approximate'''							,					num_embeds_ada_norm=1_0_0_0							,					norm_type='''ada_norm_zero'''							,					norm_elementwise_affine=__UpperCamelCase							,					)
							transformer.load_state_dict(__UpperCamelCase							,					strict=__UpperCamelCase			)

							__UpperCamelCase							=DDIMScheduler(
							    num_train_timesteps=1_0_0_0							,					beta_schedule='''linear'''							,					prediction_type='''epsilon'''							,					clip_sample=__UpperCamelCase							,					)

							__UpperCamelCase							=AutoencoderKL.from_pretrained(args.vae_model			)

							__UpperCamelCase							=DiTPipeline(transformer=__UpperCamelCase							,					vae=__UpperCamelCase							,					scheduler=__UpperCamelCase			)

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


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

				__lowercase    =				parser.parse_args()
				main(args)




"""simple docstring"""

class 		_lowercase :



						"""simple docstring"""




						def __init__(				self							:    List[str]     , UpperCamelCase__							:    int				)				->				None:
													'''simple docstring'''



													__UpperCamelCase							=data
													__UpperCamelCase							=None
													__UpperCamelCase							=None






def    lowerCAmelCase			(__UpperCamelCase							:    BinaryTreeNode | None							,					__UpperCamelCase							:    int			):
							"""simple docstring"""


							if node is None:
														__UpperCamelCase							=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:
														__UpperCamelCase							=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
														__UpperCamelCase							=insert(node.right_child							,					__UpperCamelCase			)
							return node


def    lowerCAmelCase			(__UpperCamelCase							:    None | BinaryTreeNode			):  # if node is None,return
							"""simple docstring"""


							if node:
														__UpperCamelCase							=inorder(node.left_child			)
														__UpperCamelCase							=[*inorder_array, node.data]
														__UpperCamelCase							=inorder_array + inorder(node.right_child			)
							else:
														__UpperCamelCase							=[]
							return inorder_array


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=insert(__UpperCamelCase							,					1_5			)
							insert(__UpperCamelCase							,					1_0			)
							insert(__UpperCamelCase							,					2_5			)
							insert(__UpperCamelCase							,					6			)
							insert(__UpperCamelCase							,					1_4			)
							insert(__UpperCamelCase							,					2_0			)
							insert(__UpperCamelCase							,					6_0			)
							return root


def    lowerCAmelCase			():
							"""simple docstring"""


							__UpperCamelCase							=make_tree()
							print('''Printing values of binary search tree in Inorder Traversal.'''			)
							inorder(__UpperCamelCase			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()



