'''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


__SCREAMING_SNAKE_CASE	:					Optional[Any]					=  get_logger(__name__)

def      UpperCamelCase_							( _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				List[dict]      ,     _UpperCAmelCase  :				list      ,     _UpperCAmelCase  :				list      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				GradScaler = None      ,     )   ->   Dict:


       """simple docstring"""







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

def      UpperCamelCase_							( _UpperCAmelCase  :				Optional[int]      ,     _UpperCAmelCase  :				Tuple      ,     _UpperCAmelCase  :				Optional[Any]      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				int      ,     _UpperCAmelCase  :				Dict=None      ,     _UpperCAmelCase  :				int=None      ,     **_UpperCAmelCase  :				Any      ,     )   ->   Tuple:


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

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

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

       # GradScaler state
       if scaler is not None:
              _UpperCAmelCase					:  Union[str, Any]											=   os.path.join(_UpperCAmelCase      ,     _UpperCAmelCase   )
              scaler.load_state_dict(torch.load(_UpperCAmelCase   )   )
              logger.info("GradScaler state loaded successfully"   )

       # Random states
       try:
              _UpperCAmelCase					:  Dict											=   torch.load(os.path.join(_UpperCAmelCase      ,     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_							( _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				str      ,     _UpperCAmelCase  :				int = 0   )   ->   int:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   Path(_UpperCAmelCase   ) / F"""custom_checkpoint_{index}.pkl"""
       logger.info(F"""Saving the state of {get_pretty_name(_UpperCAmelCase   )} to {save_location}"""   )
       torch.save(obj.state_dict()      ,     _UpperCAmelCase   )


def      UpperCamelCase_							( _UpperCAmelCase  :				List[str]      ,     _UpperCAmelCase  :				Dict      ,     _UpperCAmelCase  :				int = 0   )   ->   Tuple:


       """simple docstring"""







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


'''simple docstring'''




from __future__ import annotations

from collections.abc import Generator

def      UpperCamelCase_							( _UpperCAmelCase  :				int   )   ->   Generator[int, None, None]:


       """simple docstring"""







       if not isinstance(_UpperCAmelCase      ,     _UpperCAmelCase   ) or n < 1:
              raise Exception("Sequence only defined for positive integers"   )

       yield n
       while n != 1:
              if n % 2 == 0:
                     n //= 2
              else:
                     _UpperCAmelCase					:  Optional[Any]											=   3 * n + 1
              yield n

def      UpperCamelCase_							( )   ->   Any:


       """simple docstring"""







       _UpperCAmelCase					:  str											=   43
       _UpperCAmelCase					:  Optional[int]											=   tuple(collatz_sequence(_UpperCAmelCase   )   )
       print(_UpperCAmelCase   )
       print(F"""Collatz sequence from {n} took {len(_UpperCAmelCase   )} steps."""   )


if __name__ == "__main__":
  main()

