




'''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__      				=  get_logger(__name__)




def   snake_case__			(       lowerCamelCase__ :      str	,						lowerCamelCase__ :      int	,						lowerCamelCase__ :      List[Any]	,						lowerCamelCase__ :      Tuple	,						lowerCamelCase__ :      List[str]	,						lowerCamelCase__ :      Any = None	,						)      ->      List[Any]:






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




def   snake_case__			(       lowerCamelCase__ :      Tuple	,						lowerCamelCase__ :      Tuple	,						lowerCamelCase__ :      str	,						lowerCamelCase__ :      List[Any]	,						lowerCamelCase__ :      Dict	,						lowerCamelCase__ :      List[Any]=None	,						lowerCamelCase__ :      Optional[Any]=None	,						**lowerCamelCase__ :      List[str]	,						)      ->      List[Any]:






					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:
										A_       :   int       =							'''cpu'''
					elif map_location == "on_device":
										A_       :   Optional[int]       =							PartialState().device
					# Model states
					for i, model in enumerate(_snake_case	):
										A_       :   Dict       =							f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										A_       :   Tuple       =							os.path.join(_snake_case	,						_snake_case	)
										models[i].load_state_dict(torch.load(_snake_case	,						map_location=_snake_case	)	,						**_snake_case	)
					logger.info('''All model weights loaded successfully'''	)

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

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

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

					# Random states
					try:
										A_       :   str       =							torch.load(os.path.join(_snake_case	,						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   snake_case__			(       lowerCamelCase__ :      Optional[int]	,						lowerCamelCase__ :      List[Any]	,						lowerCamelCase__ :      Union[str, Any] = 0	)      ->      List[str]:
					A_       :   Optional[int]       =							Path(_snake_case	) / f'custom_checkpoint_{index}.pkl'
					logger.info(f'Saving the state of {get_pretty_name(_snake_case	)} to {save_location}'	)
					torch.save(obj.state_dict()	,						_snake_case	)





def   snake_case__			(       lowerCamelCase__ :      Tuple	,						lowerCamelCase__ :      List[Any]	,						lowerCamelCase__ :      List[Any] = 0	)      ->      Tuple:
					A_       :   Tuple       =							f'{path}/custom_checkpoint_{index}.pkl'
					logger.info(f'Loading the state of {get_pretty_name(_snake_case	)} from {load_location}'	)
					obj.load_state_dict(torch.load(_snake_case	,						map_location='''cpu'''	)	)











"""simple docstring"""







from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase     			=  TypeVar("""T""")







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self	,A					):
          UpperCAmelCase							=       data  # Assign data
          UpperCAmelCase							=       None  # Initialize next as null
          UpperCAmelCase							=       None  # Initialize prev as null







class 					lowerCamelCase__					(       Generic[T] ):






    def __init__( self					):
          UpperCAmelCase							=       None






    def 				_UpperCamelCase							( self	,A					):
          if self.head is None:
                UpperCAmelCase							=       Node(A					)
          else:
                UpperCAmelCase							=       Node(A					)
                UpperCAmelCase							=       new_node
                UpperCAmelCase							=       self.head
                UpperCAmelCase							=       None
                UpperCAmelCase							=       new_node






    def 				_UpperCamelCase							( self					):
          if self.head is None:
                return None
          else:
                assert self.head is not None
                UpperCAmelCase							=       self.head.data
                UpperCAmelCase							=       self.head.next
                if self.head is not None:
                      UpperCAmelCase							=       None
                return temp






    def 				_UpperCamelCase							( self					):
          return self.head.data if self.head is not None else None






    def __len__( self					):
          UpperCAmelCase							=       self.head
          UpperCAmelCase							=       0
          while temp is not None:
                count += 1
                UpperCAmelCase							=       temp.next
          return count






    def 				_UpperCamelCase							( self					):
          return self.head is None






    def 				_UpperCamelCase							( self					):
          print("""stack elements are:"""					)
          UpperCAmelCase							=       self.head
          while temp is not None:
                print(temp.data	,end="""->"""					)
                UpperCAmelCase							=       temp.next


# Code execution starts here
if __name__ == "__main__":
  # Start with the empty stack
  _UpperCamelCase     			=  Stack()

  # Insert 4 at the beginning. So stack becomes 4->None
  print("""Stack operations using Doubly LinkedList""")
  stack.push(4)

  # Insert 5 at the beginning. So stack becomes 4->5->None
  stack.push(5)

  # Insert 6 at the beginning. So stack becomes 4->5->6->None
  stack.push(6)

  # Insert 7 at the beginning. So stack becomes 4->5->6->7->None
  stack.push(7)

  # Print the stack
  stack.print_stack()

  # Print the top element
  print("""\nTop element is """, stack.top())

  # Print the stack size
  print("""Size of the stack is """, len(stack))

  # pop the top element
  stack.pop()

  # pop the top element
  stack.pop()

  # two elements have now been popped off
  stack.print_stack()

  # Print True if the stack is empty else False
  print("""\nstack is empty:""", stack.is_empty())



