code: 
  """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
  
  
  _a	 =							get_logger(__name__)
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = None,	):
     # Model states
     for i, state in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Union[str, Any]      =					f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Optional[Any]      =					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						):
        UpperCAmelCase_      :      Optional[int]      =					opt.state_dict()
        UpperCAmelCase_      :      Any      =					f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      List[Any]      =					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						):
        UpperCAmelCase_      :      Optional[int]      =					scheduler.state_dict()
        UpperCAmelCase_      :      Tuple      =					f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Optional[Any]      =					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:
        UpperCAmelCase_      :      Tuple      =					scaler.state_dict()
        UpperCAmelCase_      :      Dict      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        torch.save(__lowerCamelCase,	__lowerCamelCase						)
        logger.info(f"""Gradient scaler state saved in {output_scaler_file}"""						)
     # Random number generator states
     UpperCAmelCase_      :      List[Any]      =					{}
     UpperCAmelCase_      :      List[str]      =					f"""{RNG_STATE_NAME}_{process_index}.pkl"""
     UpperCAmelCase_      :      Any      =					random.getstate()
     UpperCAmelCase_      :      Optional[Any]      =					np.random.get_state()
     UpperCAmelCase_      :      Dict      =					torch.get_rng_state()
     if is_xpu_available():
        UpperCAmelCase_      :      Optional[int]      =					torch.xpu.get_rng_state_all()
     else:
        UpperCAmelCase_      :      int      =					torch.cuda.get_rng_state_all()
     if is_tpu_available():
        UpperCAmelCase_      :      List[str]      =					xm.get_rng_state()
     UpperCAmelCase_      :      Union[str, Any]      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
     torch.save(__lowerCamelCase,	__lowerCamelCase						)
     logger.info(f"""Random states saved in {output_states_file}"""						)
     return output_dir
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase=None,	__lowerCamelCase=None,	**__lowerCamelCase,	):
     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_      :      List[Any]      =					PartialState().device
     # Model states
     for i, model in enumerate(__lowerCamelCase						):
        UpperCAmelCase_      :      Optional[Any]      =					f"""{MODEL_NAME}.bin""" if i == 0 else f"""{MODEL_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Any      =					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						):
        UpperCAmelCase_      :      Tuple      =					f"""{OPTIMIZER_NAME}.bin""" if i == 0 else f"""{OPTIMIZER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      Tuple      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        UpperCAmelCase_      :      Union[str, Any]      =					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						):
        UpperCAmelCase_      :      List[str]      =					f"""{SCHEDULER_NAME}.bin""" if i == 0 else f"""{SCHEDULER_NAME}_{i}.bin"""
        UpperCAmelCase_      :      str      =					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:
        UpperCAmelCase_      :      str      =					os.path.join(__lowerCamelCase,	__lowerCamelCase						)
        scaler.load_state_dict(torch.load(__lowerCamelCase						)						)
        logger.info("GradScaler state loaded successfully"						)
  
     # Random states
     try:
        UpperCAmelCase_      :      Tuple      =					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 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = 0						):
     UpperCAmelCase_      :      Dict      =					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 			__a  (	__lowerCamelCase,	__lowerCamelCase,	__lowerCamelCase = 0						):
     UpperCAmelCase_      :      Any      =					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"						)						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from hashlib import mda, shaaaa
  
  _a	 =							(shaaaa, mda)
  
  
  class A_			:
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(	self       ,     lowercase_ = 8						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       UpperCAmelCase_      :      Union[str, Any]      =					size
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					self.hash_(lowercase_						)
       self.bitarray |= h
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					self.hash_(lowercase_						)
       return (h & self.bitarray) == h
  
  
  
  
  
  
  
    def __contains__(	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.exists(lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      str      =					bin(lowercase_						)[2:]
       return res.zfill(self.size						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       return self.format_bin(self.bitarray						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					0B0
       for func in HASH_FUNCTIONS:
          UpperCAmelCase_      :      str      =					(
              int.from_bytes(func(value.encode()						).digest()       ,     "little"						) % self.size
          )
          res |= 2**position
       return res
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self       ,     lowercase_						):
  
       """simple docstring"""
       return self.format_bin(self.hash_(lowercase_						)						)
  
  
  
  
  
  
  
    @property
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					bin(self.bitarray						).count("1"						)
       return (n_ones / self.size) ** len(lowercase_						)
  
  
  