'''simple docstring'''


import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


snake_case__	:     List[str]      =    get_logger(__name__)


def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[dict]      ,	lowerCamelCase_   :				list      ,	lowerCamelCase_   :				list      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				GradScaler = None      ,	):



     """simple docstring"""





     for i, state in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  Tuple  = F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
          UpperCAmelCase_				:  int  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          save(lowerCamelCase_      ,	lowerCamelCase_  )
          logger.info(F'''Model weights saved in {output_model_file}'''  )
     # Optimizer states
     for i, opt in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  str  = opt.state_dict()
          UpperCAmelCase_				:  Optional[Any]  = F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
          UpperCAmelCase_				:  List[str]  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          save(lowerCamelCase_      ,	lowerCamelCase_  )
          logger.info(F'''Optimizer state saved in {output_optimizer_file}'''  )
     # Scheduler states
     for i, scheduler in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  Tuple  = scheduler.state_dict()
          UpperCAmelCase_				:  Union[str, Any]  = F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
          UpperCAmelCase_				:  int  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          save(lowerCamelCase_      ,	lowerCamelCase_  )
          logger.info(F'''Scheduler state saved in {output_scheduler_file}'''  )
     # GradScaler state
     if scaler is not None:
          UpperCAmelCase_				:  Dict  = scaler.state_dict()
          UpperCAmelCase_				:  Dict  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          torch.save(lowerCamelCase_      ,	lowerCamelCase_  )
          logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''  )
     # Random number generator states
     UpperCAmelCase_				:  Dict  = {}
     UpperCAmelCase_				:  int  = F'''{RNG_STATE_NAME}_{process_index}.pkl'''
     UpperCAmelCase_				:  Dict  = random.getstate()
     UpperCAmelCase_				:  List[str]  = np.random.get_state()
     UpperCAmelCase_				:  Any  = torch.get_rng_state()
     if is_xpu_available():
          UpperCAmelCase_				:  Optional[int]  = torch.xpu.get_rng_state_all()
     else:
          UpperCAmelCase_				:  Dict  = torch.cuda.get_rng_state_all()
     if is_tpu_available():
          UpperCAmelCase_				:  int  = xm.get_rng_state()
     UpperCAmelCase_				:  Any  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
     torch.save(lowerCamelCase_      ,	lowerCamelCase_  )
     logger.info(F'''Random states saved in {output_states_file}'''  )
     return output_dir


def    _lowerCamelCase       ( lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				Any=None      ,	lowerCamelCase_   :				int=None      ,	**lowerCamelCase_   :				Dict      ,	):



     """simple docstring"""





     if map_location not in [None, "cpu", "on_device"]:
          raise TypeError(
              'Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'  )
     if map_location is None:
          UpperCAmelCase_				:  str  = 'cpu'
     elif map_location == "on_device":
          UpperCAmelCase_				:  Optional[int]  = PartialState().device
     # Model states
     for i, model in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  Union[str, Any]  = F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
          UpperCAmelCase_				:  Any  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          models[i].load_state_dict(torch.load(lowerCamelCase_      ,	map_location=lowerCamelCase_  )      ,	**lowerCamelCase_  )
     logger.info('All model weights loaded successfully'  )

     # Optimizer states
     for i, opt in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  List[Any]  = F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
          UpperCAmelCase_				:  List[Any]  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          UpperCAmelCase_				:  str  = torch.load(lowerCamelCase_      ,	map_location=lowerCamelCase_  )
          optimizers[i].load_state_dict(lowerCamelCase_  )
     logger.info('All optimizer states loaded successfully'  )

     # Scheduler states
     for i, scheduler in enumerate(lowerCamelCase_  ):
          UpperCAmelCase_				:  Any  = F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
          UpperCAmelCase_				:  List[Any]  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          scheduler.load_state_dict(torch.load(lowerCamelCase_  )  )
     logger.info('All scheduler states loaded successfully'  )

     # GradScaler state
     if scaler is not None:
          UpperCAmelCase_				:  Dict  = os.path.join(lowerCamelCase_      ,	lowerCamelCase_  )
          scaler.load_state_dict(torch.load(lowerCamelCase_  )  )
          logger.info('GradScaler state loaded successfully'  )

     # Random states
     try:
          UpperCAmelCase_				:  Union[str, Any]  = torch.load(os.path.join(lowerCamelCase_      ,	F'''{RNG_STATE_NAME}_{process_index}.pkl'''  )  )
          random.setstate(states['random_state']  )
          np.random.set_state(states['numpy_random_seed']  )
          torch.set_rng_state(states['torch_manual_seed']  )
          if is_xpu_available():
               torch.xpu.set_rng_state_all(states['torch_xpu_manual_seed']  )
          else:
               torch.cuda.set_rng_state_all(states['torch_cuda_manual_seed']  )
          if is_tpu_available():
               xm.set_rng_state(states['xm_seed']  )
          logger.info('All random states loaded successfully'  )
     except Exception:
          logger.info('Could not load random states'  )


def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				str      ,	lowerCamelCase_   :				int = 0  ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = Path(lowerCamelCase_  ) / F'''custom_checkpoint_{index}.pkl'''
     logger.info(F'''Saving the state of {get_pretty_name(lowerCamelCase_  )} to {save_location}'''  )
     torch.save(obj.state_dict()      ,	lowerCamelCase_  )





def    _lowerCamelCase       ( lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				int = 0  ):



     """simple docstring"""





     UpperCAmelCase_				:  Tuple  = F'''{path}/custom_checkpoint_{index}.pkl'''
     logger.info(F'''Loading the state of {get_pretty_name(lowerCamelCase_  )} from {load_location}'''  )
     obj.load_state_dict(torch.load(lowerCamelCase_      ,	map_location='cpu'  )  )







'''simple docstring'''


# 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 ,						snake_case_ ,						snake_case_		):




       '''simple docstring'''

       UpperCAmelCase_				:  Union[str, Any]  = key
       UpperCAmelCase_				:  Tuple  = freq






  def __str__(					self		):




       '''simple docstring'''

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







def    _lowerCamelCase       ( lowerCamelCase_   :				str      ,	lowerCamelCase_   :				List[str]      ,	lowerCamelCase_   :				Dict      ,	lowerCamelCase_   :				List[Any]      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     if i > j or i < 0 or j > len(lowerCamelCase_  ) - 1:
          return

     UpperCAmelCase_				:  Optional[int]  = 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(lowerCamelCase_      ,	lowerCamelCase_      ,	lowerCamelCase_      ,	node - 1      ,	key[node]      ,	lowerCamelCase_  )
     print_binary_search_tree(lowerCamelCase_      ,	lowerCamelCase_      ,	node + 1      ,	lowerCamelCase_      ,	key[node]      ,	lowerCamelCase_  )


def    _lowerCamelCase       ( lowerCamelCase_   :				Optional[int]  ):



     """simple docstring"""





     nodes.sort(key=lambda lowerCamelCase_  : node.key  )

     UpperCAmelCase_				:  int  = len(lowerCamelCase_  )

     UpperCAmelCase_				:  Dict  = [nodes[i].key for i in range(lowerCamelCase_  )]
     UpperCAmelCase_				:  Any  = [nodes[i].freq for i in range(lowerCamelCase_  )]

     # 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_				:  Optional[Any]  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # sum[i][j] stores the sum of key frequencies between i and j inclusive in nodes
     # array
     UpperCAmelCase_				:  Any  = [[freqs[i] if i == j else 0 for j in range(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]
     # 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(lowerCamelCase_  )] for i in range(lowerCamelCase_  )]

     for interval_length in range(2      ,	n + 1  ):
          for i in range(n - interval_length + 1  ):
               UpperCAmelCase_				:  Optional[Any]  = i + interval_length - 1

               UpperCAmelCase_				:  Tuple  = sys.maxsize  # set the value to "infinity"
               UpperCAmelCase_				:  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_				:  Optional[int]  = dp[r + 1][j] if r != j else 0  # optimal cost for right subtree
                    UpperCAmelCase_				:  Optional[int]  = left + total[i][j] + right

                    if dp[i][j] > cost:
                         UpperCAmelCase_				:  Dict  = cost
                         UpperCAmelCase_				:  Tuple  = r

     print('Binary search tree nodes:'  )
     for node in nodes:
          print(lowerCamelCase_  )

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


def    _lowerCamelCase       ( ):



     """simple docstring"""





     UpperCAmelCase_				:  Any  = [Node(lowerCamelCase_      ,	randint(1      ,	50  )  ) for i in range(10      ,	0      ,	-1  )]
     find_optimal_binary_search_tree(lowerCamelCase_  )


if __name__ == "__main__":
 main()






