




# 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 		__UpperCAmelCase:





     """simple docstring"""



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






         """simple docstring"""
         a__      =       key
         a__      =       freq






     def __str__(      self :       Dict    )			->			str:






         """simple docstring"""
         return f"""Node(key={self.key}, freq={self.freq})"""






def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_		):
    if i > j or i < 0 or j > len(UpperCAmelCase_		) - 1:
        return

    a__      =       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(UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    node - 1   ,    key[node]   ,    UpperCAmelCase_		)
    print_binary_search_tree(UpperCAmelCase_   ,    UpperCAmelCase_   ,    node + 1   ,    UpperCAmelCase_   ,    key[node]   ,    UpperCAmelCase_		)
def        __lowerCAmelCase (UpperCAmelCase_		):
    nodes.sort(key=lambda UpperCAmelCase_		: node.key		)

    a__      =       len(UpperCAmelCase_		)

    a__      =       [nodes[i].key for i in range(UpperCAmelCase_		)]
    a__      =       [nodes[i].freq for i in range(UpperCAmelCase_		)]

    # 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.
    a__      =       [[freqs[i] if i == j else 0 for j in range(UpperCAmelCase_		)] for i in range(UpperCAmelCase_		)]
    # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
    # array
    a__      =       [[freqs[i] if i == j else 0 for j in range(UpperCAmelCase_		)] for i in range(UpperCAmelCase_		)]
    # stores tree roots that will be used later for constructing binary search tree
    a__      =       [[i if i == j else 0 for j in range(UpperCAmelCase_		)] for i in range(UpperCAmelCase_		)]

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

            a__      =       sys.maxsize  # set the value to "infinity"
            a__      =       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
                a__      =       dp[i][r - 1] if r != i else 0  # optimal cost for left subtree
                a__      =       dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                a__      =       left + total[i][j] + right

                if dp[i][j] > cost:
                    a__      =       cost
                    a__      =       r

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

    print(F"""\nThe cost of optimal BST for given tree nodes is {dp[0][n - 1]}."""		)
    print_binary_search_tree(UpperCAmelCase_   ,    UpperCAmelCase_   ,    0   ,    n - 1   ,    -1   ,    UpperCAmelCase_		)
def        __lowerCAmelCase ():
    # A sample binary search tree
    a__      =       [Node(UpperCAmelCase_   ,    randint(1   ,    50		)		) for i in range(10   ,    0   ,    -1		)]
    find_optimal_binary_search_tree(UpperCAmelCase_		)


if __name__ == "__main__":
    main()












import argparse
import os

import torch
from torchvision.datasets.utils import download_url

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, TransformeraDModel


SCREAMING_SNAKE_CASE_						:					Tuple			   =       {5_12: '''DiT-XL-2-512x512.pt''', 2_56: '''DiT-XL-2-256x256.pt'''}
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       F"""pretrained_models/{model_name}"""
    if not os.path.isfile(UpperCAmelCase_		):
        os.makedirs("""pretrained_models"""   ,    exist_ok=UpperCAmelCase_		)
        a__      =       F"""https://dl.fbaipublicfiles.com/DiT/models/{model_name}"""
        download_url(UpperCAmelCase_   ,    """pretrained_models"""		)
    a__      =       torch.load(UpperCAmelCase_   ,    map_location=lambda UpperCAmelCase_   ,    UpperCAmelCase_		: storage		)
    return model
def        __lowerCAmelCase (UpperCAmelCase_		):
    a__      =       download_model(pretrained_models[args.image_size]		)

    a__      =       state_dict["""x_embedder.proj.weight"""]
    a__      =       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		):
        a__      =       state_dict[
            """t_embedder.mlp.0.weight"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.0.bias"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.2.weight"""
        ]
        a__      =       state_dict[
            """t_embedder.mlp.2.bias"""
        ]
        a__      =       state_dict[
            """y_embedder.embedding_table.weight"""
        ]

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

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

        a__      =       q
        a__      =       q_bias
        a__      =       k
        a__      =       k_bias
        a__      =       v
        a__      =       v_bias

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

        a__      =       state_dict[F"""blocks.{depth}.mlp.fc1.weight"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc1.bias"""]
        a__      =       state_dict[F"""blocks.{depth}.mlp.fc2.weight"""]
        a__      =       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"""		)

    a__      =       state_dict["""final_layer.adaLN_modulation.1.weight"""]
    a__      =       state_dict["""final_layer.adaLN_modulation.1.bias"""]
    a__      =       state_dict["""final_layer.linear.weight"""]
    a__      =       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
    a__      =       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_		)

    a__      =       DDIMScheduler(
        num_train_timesteps=1_000   ,    beta_schedule="""linear"""   ,    prediction_type="""epsilon"""   ,    clip_sample=UpperCAmelCase_   ,    )

    a__      =       AutoencoderKL.from_pretrained(args.vae_model		)

    a__      =       DiTPipeline(transformer=UpperCAmelCase_   ,    vae=UpperCAmelCase_   ,    scheduler=UpperCAmelCase_		)

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       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.'''
 )

 SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       parser.parse_args()
 main(args)






