




class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      Tuple	) ->				int:
           lowerCamelCase__					     = val
           # Number of nodes in left subtree
           lowerCamelCase__					     = 0
           lowerCamelCase__					     = None
           lowerCamelCase__					     = None
           lowerCamelCase__					     = None




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      List[Any]	) ->				Optional[int]:
           assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

           if self.val < other.val:
                 lowerCamelCase__					     = self.right
                 lowerCamelCase__					     = None
                 if self.right:
                       lowerCamelCase__					     = other
                 lowerCamelCase__					     = other
                 lowerCamelCase__					     = self.left_tree_size * 2 + 1
                 return self
           else:
                 lowerCamelCase__					     = other.right
                 lowerCamelCase__					     = None
                 if other.right:
                       lowerCamelCase__					     = self
                 lowerCamelCase__					     = self
                 lowerCamelCase__					     = other.left_tree_size * 2 + 1
                 return other






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      Optional[Any]=None		,     A__     :      Tuple=None		,     A__     :      int=0	) ->				Union[str, Any]:
           lowerCamelCase__					     = heap_size
           lowerCamelCase__					     = bottom_root
           lowerCamelCase__					     = min_node




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Tuple	) ->				Any:

           # Empty heaps corner cases
           if other.size == 0:
                 return None
           if self.size == 0:
                 lowerCamelCase__					     = other.size
                 lowerCamelCase__					     = other.bottom_root
                 lowerCamelCase__					     = other.min_node
                 return None
           # Update size
           lowerCamelCase__					     = self.size + other.size

           # Update min.node
           if self.min_node.val > other.min_node.val:
                 lowerCamelCase__					     = other.min_node
           # Merge

           # Order roots by left_subtree_size
           lowerCamelCase__					     = []
           lowerCamelCase__     ,						lowerCamelCase__					     = self.bottom_root, other.bottom_root
           while i or j:
                 if i and ((not j) or i.left_tree_size < j.left_tree_size):
                       combined_roots_list.append((i, True)	)
                       lowerCamelCase__					     = i.parent
                 else:
                       combined_roots_list.append((j, False)	)
                       lowerCamelCase__					     = j.parent
        # Insert links between them
           for i in range(len(A__	) - 1	):
                 if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                       lowerCamelCase__					     = combined_roots_list[i + 1][0]
                       lowerCamelCase__					     = combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
           lowerCamelCase__					     = combined_roots_list[0][0]
           while i.parent:
                 if (
                     (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
                 ) or (
                     i.left_tree_size == i.parent.left_tree_size
                     and i.left_tree_size != i.parent.parent.left_tree_size
                 ):
                       # Neighbouring Nodes
                       lowerCamelCase__					     = i.left
                       lowerCamelCase__					     = i.parent.parent

                       # Merging trees
                       lowerCamelCase__					     = i.merge_trees(i.parent	)

                       # Updating links
                       lowerCamelCase__					     = previous_node
                       lowerCamelCase__					     = next_node
                       if previous_node:
                             lowerCamelCase__					     = i
                       if next_node:
                             lowerCamelCase__					     = i
                 else:
                       lowerCamelCase__					     = i.parent
        # Updating self.bottom_root
           while i.left:
                 lowerCamelCase__					     = i.left
           lowerCamelCase__					     = i

           # Update other
           lowerCamelCase__					     = self.size
           lowerCamelCase__					     = self.bottom_root
           lowerCamelCase__					     = self.min_node

           # Return the merged heap
           return self




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      Union[str, Any]	) ->				List[Any]:
           if self.size == 0:
                 lowerCamelCase__					     = Node(A__	)
                 lowerCamelCase__					     = 1
                 lowerCamelCase__					     = self.bottom_root
           else:
                 # Create new node
                 lowerCamelCase__					     = Node(A__	)

                 # Update size
                 self.size += 1

                 # update min_node
                 if val < self.min_node.val:
                       lowerCamelCase__					     = new_node
                 # Put new_node as a bottom_root in heap
                 lowerCamelCase__					     = new_node
                 lowerCamelCase__					     = self.bottom_root
                 lowerCamelCase__					     = new_node

                 # Consecutively merge roots with same left_tree_size
                 while (
                     self.bottom_root.parent
                     and self.bottom_root.left_tree_size
                     == self.bottom_root.parent.left_tree_size
                 ):
                       # Next node
                       lowerCamelCase__					     = self.bottom_root.parent.parent

                       # Merge
                       lowerCamelCase__					     = self.bottom_root.merge_trees(self.bottom_root.parent	)

                       # Update Links
                       lowerCamelCase__					     = next_node
                       lowerCamelCase__					     = None
                       if next_node:
                             lowerCamelCase__					     = self.bottom_root




     def        __snake_case      (			self     :      List[Any]	) ->				int:
           return self.min_node.val




     def        __snake_case      (			self     :      Dict	) ->				int:
           return self.size == 0




     def        __snake_case      (			self     :      str	) ->				str:
           lowerCamelCase__					     = self.min_node.val

           # Last element in heap corner case
           if self.size == 1:
                 # Update size
                 lowerCamelCase__					     = 0

                 # Update bottom root
                 lowerCamelCase__					     = None

                 # Update min_node
                 lowerCamelCase__					     = None

                 return min_value
           # No right subtree corner case
           # The structure of the tree implies that this should be the bottom root
           # and there is at least one other root
           if self.min_node.right is None:
                 # Update size
                 self.size -= 1

                 # Update bottom root
                 lowerCamelCase__					     = self.bottom_root.parent
                 lowerCamelCase__					     = None

                 # Update min_node
                 lowerCamelCase__					     = self.bottom_root
                 lowerCamelCase__					     = self.bottom_root.parent
                 while i:
                       if i.val < self.min_node.val:
                             lowerCamelCase__					     = i
                       lowerCamelCase__					     = i.parent
                 return min_value
           # General case
           # Find the BinomialHeap of the right subtree of min_node
           lowerCamelCase__					     = self.min_node.right
           lowerCamelCase__					     = None
           lowerCamelCase__					     = bottom_of_new
           lowerCamelCase__					     = 1

           # Size, min_node and bottom_root
           while bottom_of_new.left:
                 lowerCamelCase__					     = size_of_new * 2 + 1
                 lowerCamelCase__					     = bottom_of_new.left
                 if bottom_of_new.val < min_of_new.val:
                       lowerCamelCase__					     = bottom_of_new
        # Corner case of single root on top left path
           if (not self.min_node.left) and (not self.min_node.parent):
                 lowerCamelCase__					     = size_of_new
                 lowerCamelCase__					     = bottom_of_new
                 lowerCamelCase__					     = min_of_new
                 # print("Single root, multiple nodes case")
                 return min_value
           # Remaining cases
           # Construct heap of right subtree
           lowerCamelCase__					     = BinomialHeap(
               bottom_root=A__		,     min_node=A__		,     heap_size=A__	)

           # Update size
           lowerCamelCase__					     = self.size - 1 - size_of_new

           # Neighbour nodes
           lowerCamelCase__					     = self.min_node.left
           lowerCamelCase__					     = self.min_node.parent

           # Initialize new bottom_root and min_node
           lowerCamelCase__					     = previous_node or next_node
           lowerCamelCase__					     = next_node

           # Update links of previous_node and search below for new min_node and
           # bottom_root
           if previous_node:
                 lowerCamelCase__					     = next_node

                 # Update bottom_root and search for min_node below
                 lowerCamelCase__					     = previous_node
                 lowerCamelCase__					     = previous_node
                 while self.bottom_root.left:
                       lowerCamelCase__					     = self.bottom_root.left
                       if self.bottom_root.val < self.min_node.val:
                             lowerCamelCase__					     = self.bottom_root
           if next_node:
                 lowerCamelCase__					     = previous_node

                 # Search for new min_node above min_node
                 lowerCamelCase__					     = next_node
                 while i:
                       if i.val < self.min_node.val:
                             lowerCamelCase__					     = i
                       lowerCamelCase__					     = i.parent
        # Merge heaps
           self.merge_heaps(A__	)

           return min_value




     def        __snake_case      (			self     :      Tuple	) ->				List[Any]:
           lowerCamelCase__					     = self.bottom_root
           while top_root.parent:
                 lowerCamelCase__					     = top_root.parent
           # preorder
           lowerCamelCase__					     = []
           self.__traversal(A__		,     A__	)
           return heap_pre_order




     def        __snake_case      (			self     :      List[Any]		,     A__     :      Union[str, Any]		,     A__     :      Tuple		,     A__     :      Dict=0	) ->				List[str]:
           if curr_node:
                 preorder.append((curr_node.val, level)	)
                 self.__traversal(curr_node.left		,     A__		,     level + 1	)
                 self.__traversal(curr_node.right		,     A__		,     level + 1	)
           else:
                 preorder.append(('#', level)	)




     def __str__(			self     :      Optional[Any]	) ->				Any:
           if self.is_empty():
                 return ""
           lowerCamelCase__					     = self.pre_order()

           return "\n".join(('-' * level + str(A__	)) for value, level in preorder_heap	)


# Unit Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()





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


__SCREAMING_SNAKE_CASE			:      Union[str, Any]      			=get_logger(__name__)

def 	lowerCAmelCase_	(     lowerCamelCase:				str   , lowerCamelCase:				List[dict]   , lowerCamelCase:				list   , lowerCamelCase:				list   , lowerCamelCase:				int   , lowerCamelCase:				GradScaler = None   , )    ->	Any:


      '''simple docstring'''




      for i, state in enumerate(lowerCamelCase  ):
            lowerCamelCase__					     = f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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  ):
            lowerCamelCase__					     = opt.state_dict()
            lowerCamelCase__					     = f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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  ):
            lowerCamelCase__					     = scheduler.state_dict()
            lowerCamelCase__					     = f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
            lowerCamelCase__					     = 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:
            lowerCamelCase__					     = scaler.state_dict()
            lowerCamelCase__					     = os.path.join(lowerCamelCase   , lowerCamelCase  )
            torch.save(lowerCamelCase   , lowerCamelCase  )
            logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""  )
      # Random number generator states
      lowerCamelCase__					     = {}
      lowerCamelCase__					     = f"""{RNG_STATE_NAME}_{process_index}.pkl"""
      lowerCamelCase__					     = random.getstate()
      lowerCamelCase__					     = np.random.get_state()
      lowerCamelCase__					     = torch.get_rng_state()
      if is_xpu_available():
            lowerCamelCase__					     = torch.xpu.get_rng_state_all()
      else:
            lowerCamelCase__					     = torch.cuda.get_rng_state_all()
      if is_tpu_available():
            lowerCamelCase__					     = xm.get_rng_state()
      lowerCamelCase__					     = 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:				int   , lowerCamelCase:				Dict   , lowerCamelCase:				Union[str, Any]   , lowerCamelCase:				Any   , lowerCamelCase:				Any   , lowerCamelCase:				Any=None   , lowerCamelCase:				Dict=None   , **lowerCamelCase:				Optional[Any]   , )    ->	Any:


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

      # Random states
      try:
            lowerCamelCase__					     = 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:				Dict   , lowerCamelCase:				Any   , lowerCamelCase:				int = 0  )    ->	Union[str, Any]:


      '''simple docstring'''




      lowerCamelCase__					     = 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:				int   , lowerCamelCase:				Optional[int]   , lowerCamelCase:				int = 0  )    ->	Any:


      '''simple docstring'''




      lowerCamelCase__					     = 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'  )  )