

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block







@dataclass
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			torch.FloatTensor







class  UpperCamelCase_      (	_snake_case		,  _snake_case      ):


  """simple docstring"""



  @register_to_config
  def __init__(						self     :					List[str]  ,	A_     :					Optional[Union[int, Tuple[int, int]]] = None  ,	A_     :					int = 3  ,	A_     :					int = 3  ,	A_     :					bool = False  ,	A_     :					str = "positional"  ,	A_     :					int = 0  ,	A_     :					bool = True  ,	A_     :					Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")  ,	A_     :					Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")  ,	A_     :					Tuple[int] = (224, 448, 672, 896)  ,	A_     :					int = 2  ,	A_     :					float = 1  ,	A_     :					int = 1  ,	A_     :					str = "conv"  ,	A_     :					str = "conv"  ,	A_     :					str = "silu"  ,	A_     :					Optional[int] = 8  ,	A_     :					int = 32  ,	A_     :					float = 1E-5  ,	A_     :					str = "default"  ,	A_     :					bool = True  ,	A_     :					Optional[str] = None  ,	A_     :					Optional[int] = None  ,	)       ->			Union[str, Any]:
      super().__init__()

      SCREAMING_SNAKE_CASE__         =    sample_size
      SCREAMING_SNAKE_CASE__         =    block_out_channels[0] * 4

      # Check inputs
      if len(A_						) != len(A_						):
          raise ValueError(
              f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."						)

      if len(A_						) != len(A_						):
          raise ValueError(
              f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."						)

      # input
      SCREAMING_SNAKE_CASE__         =    nn.Convad(A_  ,	block_out_channels[0]  ,	kernel_size=3  ,	padding=(1, 1)						)

      # time
      if time_embedding_type == "fourier":
          SCREAMING_SNAKE_CASE__         =    GaussianFourierProjection(embedding_size=block_out_channels[0]  ,	scale=16						)
          SCREAMING_SNAKE_CASE__         =    2 * block_out_channels[0]
      elif time_embedding_type == "positional":
          SCREAMING_SNAKE_CASE__         =    Timesteps(block_out_channels[0]  ,	A_  ,	A_						)
          SCREAMING_SNAKE_CASE__         =    block_out_channels[0]

      SCREAMING_SNAKE_CASE__         =    TimestepEmbedding(A_  ,	A_						)

      # class embedding
      if class_embed_type is None and num_class_embeds is not None:
          SCREAMING_SNAKE_CASE__         =    nn.Embedding(A_  ,	A_						)
      elif class_embed_type == "timestep":
          SCREAMING_SNAKE_CASE__         =    TimestepEmbedding(A_  ,	A_						)
      elif class_embed_type == "identity":
          SCREAMING_SNAKE_CASE__         =    nn.Identity(A_  ,	A_						)
      else:
          SCREAMING_SNAKE_CASE__         =    None

      SCREAMING_SNAKE_CASE__         =    nn.ModuleList([]						)
      SCREAMING_SNAKE_CASE__         =    None
      SCREAMING_SNAKE_CASE__         =    nn.ModuleList([]						)

      # down
      SCREAMING_SNAKE_CASE__         =    block_out_channels[0]
      for i, down_block_type in enumerate(A_						):
          SCREAMING_SNAKE_CASE__         =    output_channel
          SCREAMING_SNAKE_CASE__         =    block_out_channels[i]
          SCREAMING_SNAKE_CASE__         =    i == len(A_						) - 1

          SCREAMING_SNAKE_CASE__         =    get_down_block(
              A_  ,	num_layers=A_  ,	in_channels=A_  ,	out_channels=A_  ,	temb_channels=A_  ,	add_downsample=not is_final_block  ,	resnet_eps=A_  ,	resnet_act_fn=A_  ,	resnet_groups=A_  ,	attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel  ,	downsample_padding=A_  ,	resnet_time_scale_shift=A_  ,	downsample_type=A_  ,	)
          self.down_blocks.append(A_						)

      # mid
      SCREAMING_SNAKE_CASE__         =    UNetMidBlockaD(
          in_channels=block_out_channels[-1]  ,	temb_channels=A_  ,	resnet_eps=A_  ,	resnet_act_fn=A_  ,	output_scale_factor=A_  ,	resnet_time_scale_shift=A_  ,	attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]  ,	resnet_groups=A_  ,	add_attention=A_  ,	)

      # up
      SCREAMING_SNAKE_CASE__         =    list(reversed(A_						)						)
      SCREAMING_SNAKE_CASE__         =    reversed_block_out_channels[0]
      for i, up_block_type in enumerate(A_						):
          SCREAMING_SNAKE_CASE__         =    output_channel
          SCREAMING_SNAKE_CASE__         =    reversed_block_out_channels[i]
          SCREAMING_SNAKE_CASE__         =    reversed_block_out_channels[min(i + 1  ,	len(A_						) - 1						)]

          SCREAMING_SNAKE_CASE__         =    i == len(A_						) - 1

          SCREAMING_SNAKE_CASE__         =    get_up_block(
              A_  ,	num_layers=layers_per_block + 1  ,	in_channels=A_  ,	out_channels=A_  ,	prev_output_channel=A_  ,	temb_channels=A_  ,	add_upsample=not is_final_block  ,	resnet_eps=A_  ,	resnet_act_fn=A_  ,	resnet_groups=A_  ,	attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel  ,	resnet_time_scale_shift=A_  ,	upsample_type=A_  ,	)
          self.up_blocks.append(A_						)
          SCREAMING_SNAKE_CASE__         =    output_channel

      # out
      SCREAMING_SNAKE_CASE__         =    norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4  ,	32						)
      SCREAMING_SNAKE_CASE__         =    nn.GroupNorm(num_channels=block_out_channels[0]  ,	num_groups=A_  ,	eps=A_						)
      SCREAMING_SNAKE_CASE__         =    nn.SiLU()
      SCREAMING_SNAKE_CASE__         =    nn.Convad(block_out_channels[0]  ,	A_  ,	kernel_size=3  ,	padding=1						)







  def   _UpperCamelCase     (						self     :					Union[str, Any]  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[torch.Tensor, float, int]  ,	A_     :					Optional[torch.Tensor] = None  ,	A_     :					bool = True  ,	)       ->			Union[UNetaDOutput, Tuple]:
      # 0. center input if necessary
      if self.config.center_input_sample:
          SCREAMING_SNAKE_CASE__         =    2 * sample - 1.0

      # 1. time
      SCREAMING_SNAKE_CASE__         =    timestep
      if not torch.is_tensor(A_						):
          SCREAMING_SNAKE_CASE__         =    torch.tensor([timesteps]  ,	dtype=torch.long  ,	device=sample.device						)
      elif torch.is_tensor(A_						) and len(timesteps.shape						) == 0:
          SCREAMING_SNAKE_CASE__         =    timesteps[None].to(sample.device						)

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      SCREAMING_SNAKE_CASE__         =    timesteps * torch.ones(sample.shape[0]  ,	dtype=timesteps.dtype  ,	device=timesteps.device						)

      SCREAMING_SNAKE_CASE__         =    self.time_proj(A_						)

      # timesteps does not contain any weights and will always return f32 tensors
      # but time_embedding might actually be running in fp16. so we need to cast here.
      # there might be better ways to encapsulate this.
      SCREAMING_SNAKE_CASE__         =    t_emb.to(dtype=self.dtype						)
      SCREAMING_SNAKE_CASE__         =    self.time_embedding(A_						)

      if self.class_embedding is not None:
          if class_labels is None:
              raise ValueError("class_labels should be provided when doing class conditioning"						)

          if self.config.class_embed_type == "timestep":
              SCREAMING_SNAKE_CASE__         =    self.time_proj(A_						)

          SCREAMING_SNAKE_CASE__         =    self.class_embedding(A_						).to(dtype=self.dtype						)
          SCREAMING_SNAKE_CASE__         =    emb + class_emb

      # 2. pre-process
      SCREAMING_SNAKE_CASE__         =    sample
      SCREAMING_SNAKE_CASE__         =    self.conv_in(A_						)

      # 3. down
      SCREAMING_SNAKE_CASE__         =    (sample,)
      for downsample_block in self.down_blocks:
          if hasattr(A_  ,	"skip_conv"						):
              SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    downsample_block(
                  hidden_states=A_  ,	temb=A_  ,	skip_sample=A_						)
          else:
              SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    downsample_block(hidden_states=A_  ,	temb=A_						)

          down_block_res_samples += res_samples

      # 4. mid
      SCREAMING_SNAKE_CASE__         =    self.mid_block(A_  ,	A_						)

      # 5. up
      SCREAMING_SNAKE_CASE__         =    None
      for upsample_block in self.up_blocks:
          SCREAMING_SNAKE_CASE__         =    down_block_res_samples[-len(upsample_block.resnets						) :]
          SCREAMING_SNAKE_CASE__         =    down_block_res_samples[: -len(upsample_block.resnets						)]

          if hasattr(A_  ,	"skip_conv"						):
              SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    upsample_block(A_  ,	A_  ,	A_  ,	A_						)
          else:
              SCREAMING_SNAKE_CASE__         =    upsample_block(A_  ,	A_  ,	A_						)

        # 6. post-process
      SCREAMING_SNAKE_CASE__         =    self.conv_norm_out(A_						)
      SCREAMING_SNAKE_CASE__         =    self.conv_act(A_						)
      SCREAMING_SNAKE_CASE__         =    self.conv_out(A_						)

      if skip_sample is not None:
          sample += skip_sample

      if self.config.time_embedding_type == "fourier":
          SCREAMING_SNAKE_CASE__         =    timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]						)))						)
          SCREAMING_SNAKE_CASE__         =    sample / timesteps

      if not return_dict:
          return (sample,)

      return UNetaDOutput(sample=A_						)





# 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()


