




# This Python program implements an optimal binary search tree (abbreviated BST)
# building dynamic programming algorithm that delivers O(n^2) performance.
#
# The goal of the optimal BST problem is to build a low-cost BST for a
# given set of nodes, each with its own key and frequency. The frequency
# of the node is defined as how many time the node is being searched.
# The search cost of binary search tree is given by this formula:
#
# cost(1, n) = sum{i = 1 to n}((depth(node_i) + 1) * node_i_freq)
#
# where n is number of nodes in the BST. The characteristic of low-cost
# BSTs is having a faster overall search time than other implementations.
# The reason for their fast search time is that the nodes with high
# frequencies will be placed near the root of the tree while the nodes
# with low frequencies will be placed near the leaves of the tree thus
# reducing search time in the most frequent instances.
import sys
from random import randint


class       SCREAMING_SNAKE_CASE_							:



	"""simple docstring"""


	def __init__(				self		:	str				,    lowerCAmelCase		:	List[str]				,    lowerCAmelCase		:	Optional[int]  )  ->					Optional[int]:







						"""simple docstring"""







						__UpperCamelCase				:  List[str]								=			key
						__UpperCamelCase				:  Union[str, Any]								=			freq






	def __str__(				self		:	Union[str, Any]  )  ->					Optional[Any]:







						"""simple docstring"""







						return F'''Node(key={self.key}, freq={self.freq})'''






def 							A__   (snake_case      :		str		,  snake_case      :		List[Any]		,  snake_case      :		Tuple		,  snake_case      :		List[str]		,  snake_case      :		List[Any]		,  snake_case      :		Dict							)    ->	Tuple:
					if i > j or i < 0 or j > len(snake_case							) - 1:
										return

					__UpperCamelCase				:  Union[str, Any]								=			root[i][j]
					if parent == -1:  # root does not have a parent
										print(F'''{key[node]} is the root of the binary search tree.'''							)
					elif is_left:
										print(F'''{key[node]} is the left child of key {parent}.'''							)
					else:
										print(F'''{key[node]} is the right child of key {parent}.'''							)

					print_binary_search_tree(snake_case		,  snake_case		,  snake_case		,  node - 1		,  key[node]		,  snake_case							)
					print_binary_search_tree(snake_case		,  snake_case		,  node + 1		,  snake_case		,  key[node]		,  snake_case							)





def 							A__   (snake_case      :		List[Any]							)    ->	str:
					nodes.sort(key=lambda snake_case							: node.key							)

					__UpperCamelCase				:  str								=			len(snake_case							)

					__UpperCamelCase				:  Optional[int]								=			[nodes[i].key for i in range(snake_case							)]
					__UpperCamelCase				:  List[Any]								=			[nodes[i].freq for i in range(snake_case							)]

					# This 2D array stores the overall tree cost (which's as minimized as possible);
					# for a single key, cost is equal to frequency of the key.
					__UpperCamelCase				:  Any								=			[[freqs[i] if i == j else 0 for j in range(snake_case							)] for i in range(snake_case							)]
					# sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
					# array
					__UpperCamelCase				:  Union[str, Any]								=			[[freqs[i] if i == j else 0 for j in range(snake_case							)] for i in range(snake_case							)]
					# stores tree roots that will be used later for constructing binary search tree
					__UpperCamelCase				:  Optional[int]								=			[[i if i == j else 0 for j in range(snake_case							)] for i in range(snake_case							)]

					for interval_length in range(2		,  n + 1							):
										for i in range(n - interval_length + 1							):
															__UpperCamelCase				:  Any								=			i + interval_length - 1

															__UpperCamelCase				:  Tuple								=			sys.maxsize  # set the value to "infinity"
															__UpperCamelCase				:  Optional[Any]								=			total[i][j - 1] + freqs[j]

															# Apply Knuth's optimization
															# Loop without optimization: for r in range(i, j + 1):
															for r in range(root[i][j - 1]		,  root[i + 1][j] + 1							):  # r is a temporal root
																				__UpperCamelCase				:  str								=			dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
																				__UpperCamelCase				:  Dict								=			dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
																				__UpperCamelCase				:  Tuple								=			left + total[i][j] + right

																				if dp[i][j] > cost:
																									__UpperCamelCase				:  int								=			cost
																									__UpperCamelCase				:  Union[str, Any]								=			r

					print("""Binary search tree nodes:"""							)
					for node in nodes:
										print(snake_case							)

					print(F'''\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}.'''							)
					print_binary_search_tree(snake_case		,  snake_case		,  0		,  n - 1		,  -1		,  snake_case							)





def 							A__   ()    ->	Optional[Any]:
					# A sample binary search tree
					__UpperCamelCase				:  List[str]								=			[Node(snake_case		,  randint(1		,  50							)							) for i in range(10		,  0		,  -1							)]
					find_optimal_binary_search_tree(snake_case							)


if __name__ == "__main__":
	main()












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)






