code: 
  
  
  
  
  
  
  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
  
  
  __A  							=  get_logger(__name__)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	str     ,			A_:	List[dict]     ,			A_:	list     ,			A_:	list     ,			A_:	int     ,			A_:	GradScaler = None     ,			)  ->							Any:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         for i, state in enumerate(A_		):
                __UpperCAmelCase    							=F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                save(A_     ,			A_		)
                logger.info(F'''Model weights saved in {output_model_file}'''		)
         # Optimizer states
         for i, opt in enumerate(A_		):
                __UpperCAmelCase    							=opt.state_dict()
                __UpperCAmelCase    							=F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                save(A_     ,			A_		)
                logger.info(F'''Optimizer state saved in {output_optimizer_file}'''		)
         # Scheduler states
         for i, scheduler in enumerate(A_		):
                __UpperCAmelCase    							=scheduler.state_dict()
                __UpperCAmelCase    							=F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                save(A_     ,			A_		)
                logger.info(F'''Scheduler state saved in {output_scheduler_file}'''		)
         # GradScaler state
         if scaler is not None:
                __UpperCAmelCase    							=scaler.state_dict()
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                torch.save(A_     ,			A_		)
                logger.info(F'''Gradient scaler state saved in {output_scaler_file}'''		)
         # Random number generator states
         __UpperCAmelCase    							={}
         __UpperCAmelCase    							=F'''{RNG_STATE_NAME}_{process_index}.pkl'''
         __UpperCAmelCase    							=random.getstate()
         __UpperCAmelCase    							=np.random.get_state()
         __UpperCAmelCase    							=torch.get_rng_state()
         if is_xpu_available():
                __UpperCAmelCase    							=torch.xpu.get_rng_state_all()
         else:
                __UpperCAmelCase    							=torch.cuda.get_rng_state_all()
         if is_tpu_available():
                __UpperCAmelCase    							=xm.get_rng_state()
         __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
         torch.save(A_     ,			A_		)
         logger.info(F'''Random states saved in {output_states_file}'''		)
         return output_dir
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	List[str]     ,			A_:	Union[str, Any]     ,			A_:	List[Any]     ,			A_:	Tuple     ,			A_:	Tuple=None     ,			A_:	List[Any]=None     ,			**A_:	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:
                __UpperCAmelCase    							="""cpu"""
         elif map_location == "on_device":
                __UpperCAmelCase    							=PartialState().device
         # Model states
         for i, model in enumerate(A_		):
                __UpperCAmelCase    							=F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                models[i].load_state_dict(torch.load(A_     ,			map_location=A_		)     ,			**A_		)
         logger.info("""All model weights loaded successfully"""		)
  
         # Optimizer states
         for i, opt in enumerate(A_		):
                __UpperCAmelCase    							=F'''{OPTIMIZER_NAME}.bin''' if i == 0 else F'''{OPTIMIZER_NAME}_{i}.bin'''
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                __UpperCAmelCase    							=torch.load(A_     ,			map_location=A_		)
                optimizers[i].load_state_dict(A_		)
         logger.info("""All optimizer states loaded successfully"""		)
  
         # Scheduler states
         for i, scheduler in enumerate(A_		):
                __UpperCAmelCase    							=F'''{SCHEDULER_NAME}.bin''' if i == 0 else F'''{SCHEDULER_NAME}_{i}.bin'''
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                scheduler.load_state_dict(torch.load(A_		)		)
         logger.info("""All scheduler states loaded successfully"""		)
  
         # GradScaler state
         if scaler is not None:
                __UpperCAmelCase    							=os.path.join(A_     ,			A_		)
                scaler.load_state_dict(torch.load(A_		)		)
                logger.info("""GradScaler state loaded successfully"""		)
  
         # Random states
         try:
                __UpperCAmelCase    							=torch.load(os.path.join(A_     ,			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 			lowercase__  (				A_:	Union[str, Any]     ,			A_:	Dict     ,			A_:	int = 0		)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=Path(A_		) / F'''custom_checkpoint_{index}.pkl'''
         logger.info(F'''Saving the state of {get_pretty_name(A_		)} to {save_location}'''		)
         torch.save(obj.state_dict()     ,			A_		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Dict     ,			A_:	Dict     ,			A_:	int = 0		)  ->							List[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=F'''{path}/custom_checkpoint_{index}.pkl'''
         logger.info(F'''Loading the state of {get_pretty_name(A_		)} from {load_location}'''		)
         obj.load_state_dict(torch.load(A_     ,			map_location="""cpu"""		)		)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from collections import namedtuple
  
  __A  							=  namedtuple("from_to", "from_ to")
  
  __A  							=  {
      "millimeter": "mm",
      "centimeter": "cm",
      "meter": "m",
      "kilometer": "km",
      "inch": "in",
      "inche": "in",  # Trailing 's' has been stripped off
      "feet": "ft",
      "foot": "ft",
      "yard": "yd",
      "mile": "mi",
  }
  
  __A  							=  {
      "mm": from_to(0.001, 10_00),
      "cm": from_to(0.01, 1_00),
      "m": from_to(1, 1),
      "km": from_to(10_00, 0.001),
      "in": from_to(0.0254, 39.3701),
      "ft": from_to(0.3048, 3.2_8084),
      "yd": from_to(0.9144, 1.0_9361),
      "mi": from_to(1609.34, 0.0_0062_1371),
  }
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	float     ,			A_:	str     ,			A_:	str		)  ->							float:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=from_type.lower().rstrip("""s"""		)
         __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
         __UpperCAmelCase    							=to_type.lower().rstrip("""s"""		)
         __UpperCAmelCase    							=TYPE_CONVERSION.get(A_     ,			A_		)
         if new_from not in METRIC_CONVERSION:
                __UpperCAmelCase    							=(
                    F'''Invalid \'from_type\' value: {from_type!r}.\n'''
                    F'''Conversion abbreviations are: {", ".join(A_		)}'''
                )
                raise ValueError(A_		)
         if new_to not in METRIC_CONVERSION:
                __UpperCAmelCase    							=(
                    F'''Invalid \'to_type\' value: {to_type!r}.\n'''
                    F'''Conversion abbreviations are: {", ".join(A_		)}'''
                )
                raise ValueError(A_		)
         return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  