




'''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


UpperCAmelCase_       :  str      =		get_logger(__name__)







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




     '''simple docstring'''





     for i, state in enumerate(_lowerCamelCase	):
          __snake_case    					=  f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
          __snake_case    					=  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	):
          __snake_case    					=  opt.state_dict()
          __snake_case    					=  f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
          __snake_case    					=  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	):
          __snake_case    					=  scheduler.state_dict()
          __snake_case    					=  f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
          __snake_case    					=  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:
          __snake_case    					=  scaler.state_dict()
          __snake_case    					=  os.path.join(_lowerCamelCase   ,	_lowerCamelCase	)
          torch.save(_lowerCamelCase   ,	_lowerCamelCase	)
          logger.info(f'''Gradient scaler state saved in {output_scaler_file}'''	)
     # Random number generator states
     __snake_case    					=  {}
     __snake_case    					=  f'''{RNG_STATE_NAME}_{process_index}.pkl'''
     __snake_case    					=  random.getstate()
     __snake_case    					=  np.random.get_state()
     __snake_case    					=  torch.get_rng_state()
     if is_xpu_available():
          __snake_case    					=  torch.xpu.get_rng_state_all()
     else:
          __snake_case    					=  torch.cuda.get_rng_state_all()
     if is_tpu_available():
          __snake_case    					=  xm.get_rng_state()
     __snake_case    					=  os.path.join(_lowerCamelCase   ,	_lowerCamelCase	)
     torch.save(_lowerCamelCase   ,	_lowerCamelCase	)
     logger.info(f'''Random states saved in {output_states_file}'''	)
     return output_dir







def 			_UpperCamelCase (_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     List[str]   ,	_lowerCamelCase							:     Optional[int]   ,	_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     Any   ,	_lowerCamelCase							:     Dict=None   ,	_lowerCamelCase							:     List[Any]=None   ,	**_lowerCamelCase							:     str   ,	)->   int:




     '''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:
          __snake_case    					=  '''cpu'''
     elif map_location == "on_device":
          __snake_case    					=  PartialState().device
     # Model states
     for i, model in enumerate(_lowerCamelCase	):
          __snake_case    					=  f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
          __snake_case    					=  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	):
          __snake_case    					=  f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
          __snake_case    					=  os.path.join(_lowerCamelCase   ,	_lowerCamelCase	)
          __snake_case    					=  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	):
          __snake_case    					=  f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
          __snake_case    					=  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:
          __snake_case    					=  os.path.join(_lowerCamelCase   ,	_lowerCamelCase	)
          scaler.load_state_dict(torch.load(_lowerCamelCase	)	)
          logger.info('''GradScaler state loaded successfully'''	)

     # Random states
     try:
          __snake_case    					=  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 			_UpperCamelCase (_lowerCamelCase							:     Optional[int]   ,	_lowerCamelCase							:     str   ,	_lowerCamelCase							:     int = 0	)->   str:




     '''simple docstring'''





     __snake_case    					=  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 			_UpperCamelCase (_lowerCamelCase							:     Tuple   ,	_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     int = 0	)->   Optional[int]:




     '''simple docstring'''





     __snake_case    					=  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     lowerCAmelCase   :


       def __init__(   self    ,  __SCREAMING_SNAKE_CASE    ,  __SCREAMING_SNAKE_CASE  )      ->  Optional[int]:



            '''simple docstring'''




            __snake_case    					=  key
            __snake_case    					=  freq




       def __str__(   self  )      ->  List[Any]:



            '''simple docstring'''




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







def 			_UpperCamelCase (_lowerCamelCase							:     str   ,	_lowerCamelCase							:     List[str]   ,	_lowerCamelCase							:     Union[str, Any]   ,	_lowerCamelCase							:     int   ,	_lowerCamelCase							:     List[Any]   ,	_lowerCamelCase							:     Tuple	)->   Any:




     '''simple docstring'''





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

     __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(_lowerCamelCase   ,	_lowerCamelCase   ,	_lowerCamelCase   ,	node - 1   ,	key[node]   ,	_lowerCamelCase	)
     print_binary_search_tree(_lowerCamelCase   ,	_lowerCamelCase   ,	node + 1   ,	_lowerCamelCase   ,	key[node]   ,	_lowerCamelCase	)







def 			_UpperCamelCase (_lowerCamelCase							:     List[Any]	)->   Union[str, Any]:




     '''simple docstring'''





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

     __snake_case    					=  len(_lowerCamelCase	)

     __snake_case    					=  [nodes[i].key for i in range(_lowerCamelCase	)]
     __snake_case    					=  [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.
     __snake_case    					=  [[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
     __snake_case    					=  [[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
     __snake_case    					=  [[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	):
               __snake_case    					=  i + interval_length - 1

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

                    if dp[i][j] > cost:
                         __snake_case    					=  cost
                         __snake_case    					=  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 			_UpperCamelCase ()->   Union[str, Any]:




     '''simple docstring'''





     __snake_case    					=  [Node(_lowerCamelCase   ,	randint(1   ,	50	)	) for i in range(10   ,	0   ,	-1	)]
     find_optimal_binary_search_tree(_lowerCamelCase	)


if __name__ == "__main__":
     main()






