

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





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    download_model(pretrained_models[args.image_size]						)

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

        SCREAMING_SNAKE_CASE__         =    state_dict[
            f"blocks.{depth}.adaLN_modulation.1.weight"
        ]
        SCREAMING_SNAKE_CASE__         =    state_dict[
            f"blocks.{depth}.adaLN_modulation.1.bias"
        ]

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

        SCREAMING_SNAKE_CASE__         =    q
        SCREAMING_SNAKE_CASE__         =    q_bias
        SCREAMING_SNAKE_CASE__         =    k
        SCREAMING_SNAKE_CASE__         =    k_bias
        SCREAMING_SNAKE_CASE__         =    v
        SCREAMING_SNAKE_CASE__         =    v_bias

        SCREAMING_SNAKE_CASE__         =    state_dict[
            f"blocks.{depth}.attn.proj.weight"
        ]
        SCREAMING_SNAKE_CASE__         =    state_dict[f"blocks.{depth}.attn.proj.bias"]

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

    SCREAMING_SNAKE_CASE__         =    state_dict["final_layer.adaLN_modulation.1.weight"]
    SCREAMING_SNAKE_CASE__         =    state_dict["final_layer.adaLN_modulation.1.bias"]
    SCREAMING_SNAKE_CASE__         =    state_dict["final_layer.linear.weight"]
    SCREAMING_SNAKE_CASE__         =    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
    SCREAMING_SNAKE_CASE__         =    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=__A			,							num_attention_heads=16			,							activation_fn="gelu-approximate"			,							num_embeds_ada_norm=1000			,							norm_type="ada_norm_zero"			,							norm_elementwise_affine=__A			,							)
    transformer.load_state_dict(__A			,							strict=__A						)

    SCREAMING_SNAKE_CASE__         =    DDIMScheduler(
        num_train_timesteps=1000			,							beta_schedule="linear"			,							prediction_type="epsilon"			,							clip_sample=__A			,							)

    SCREAMING_SNAKE_CASE__         =    AutoencoderKL.from_pretrained(args.vae_model						)

    SCREAMING_SNAKE_CASE__         =    DiTPipeline(transformer=__A			,							vae=__A			,							scheduler=__A						)

    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)





# 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     :					Any  ,	A_     :					Union[str, Any]  ,	A_     :					Optional[Any]						)       ->			Union[str, Any]:
      SCREAMING_SNAKE_CASE__         =    key
      SCREAMING_SNAKE_CASE__         =    freq







  def __str__(						self     :					Union[str, Any]						)       ->			List[str]:
      return f"Node(key={self.key}, freq={self.freq})"





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

    SCREAMING_SNAKE_CASE__         =    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(__A			,							__A			,							__A			,							node - 1			,							key[node]			,							__A						)
    print_binary_search_tree(__A			,							__A			,							node + 1			,							__A			,							key[node]			,							__A						)





def 				__a							(       __A						):
    nodes.sort(key=lambda __A						: node.key						)

    SCREAMING_SNAKE_CASE__         =    len(__A						)

    SCREAMING_SNAKE_CASE__         =    [nodes[i].key for i in range(__A						)]
    SCREAMING_SNAKE_CASE__         =    [nodes[i].freq for i in range(__A						)]

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

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

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

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

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

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





def 				__a							(       ):
    # A sample binary search tree
    SCREAMING_SNAKE_CASE__         =    [Node(__A			,							randint(1			,							50						)						) for i in range(10			,							0			,							-1						)]
    find_optimal_binary_search_tree(__A						)


if __name__ == "__main__":
  main()


