


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


lowerCamelCase		=	get_logger(__name__)






def   lowerCamelCase_					( _a			,     _a			,     _a			,     _a			,     _a			,     _a = None			,     ):




  """simple docstring"""






  for i, state in enumerate(_a ):
    lowerCAmelCase__     :    int					  =      f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
    lowerCAmelCase__     :    Any					  =      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 ):
    lowerCAmelCase__     :    List[Any]					  =      opt.state_dict()
    lowerCAmelCase__     :    Optional[int]					  =      f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
    lowerCAmelCase__     :    Any					  =      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 ):
    lowerCAmelCase__     :    List[Any]					  =      scheduler.state_dict()
    lowerCAmelCase__     :    List[str]					  =      f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
    lowerCAmelCase__     :    Union[str, Any]					  =      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:
    lowerCAmelCase__     :    Tuple					  =      scaler.state_dict()
    lowerCAmelCase__     :    Optional[int]					  =      os.path.join(_a			,     _a )
    torch.save(_a			,     _a )
    logger.info(f'Gradient scaler state saved in {output_scaler_file}' )
  # Random number generator states
  lowerCAmelCase__     :    Optional[int]					  =      {}
  lowerCAmelCase__     :    Optional[Any]					  =      f'{RNG_STATE_NAME}_{process_index}.pkl'
  lowerCAmelCase__     :    List[str]					  =      random.getstate()
  lowerCAmelCase__     :    List[Any]					  =      np.random.get_state()
  lowerCAmelCase__     :    Optional[int]					  =      torch.get_rng_state()
  if is_xpu_available():
    lowerCAmelCase__     :    List[str]					  =      torch.xpu.get_rng_state_all()
  else:
    lowerCAmelCase__     :    Union[str, Any]					  =      torch.cuda.get_rng_state_all()
  if is_tpu_available():
    lowerCAmelCase__     :    Dict					  =      xm.get_rng_state()
  lowerCAmelCase__     :    int					  =      os.path.join(_a			,     _a )
  torch.save(_a			,     _a )
  logger.info(f'Random states saved in {output_states_file}' )
  return output_dir






def   lowerCamelCase_					( _a			,     _a			,     _a			,     _a			,     _a			,     _a=None			,     _a=None			,     **_a			,     ):




  """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__     :    Optional[int]					  =      '''cpu'''
  elif map_location == "on_device":
    lowerCAmelCase__     :    List[str]					  =      PartialState().device
  # Model states
  for i, model in enumerate(_a ):
    lowerCAmelCase__     :    Dict					  =      f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
    lowerCAmelCase__     :    int					  =      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 ):
    lowerCAmelCase__     :    List[Any]					  =      f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
    lowerCAmelCase__     :    List[str]					  =      os.path.join(_a			,     _a )
    lowerCAmelCase__     :    int					  =      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 ):
    lowerCAmelCase__     :    int					  =      f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
    lowerCAmelCase__     :    Dict					  =      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:
    lowerCAmelCase__     :    List[Any]					  =      os.path.join(_a			,     _a )
    scaler.load_state_dict(torch.load(_a ) )
    logger.info('''GradScaler state loaded successfully''' )

  # Random states
  try:
    lowerCAmelCase__     :    Optional[int]					  =      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   lowerCamelCase_					( _a			,     _a			,     _a = 0 ):




  """simple docstring"""






  lowerCAmelCase__     :    Any					  =      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   lowerCamelCase_					( _a			,     _a			,     _a = 0 ):




  """simple docstring"""






  lowerCAmelCase__     :    Optional[int]					  =      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''' ) )








from itertools import count
from math import asin, pi, sqrt






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		return (
		    (1 - 2 * point) * sqrt(point - point**2 ) + 2 * point + asin(sqrt(1 - point ) )
		) / 4






def   lowerCamelCase_					( _a ):




		"""simple docstring"""






		lowerCAmelCase__     :    Union[str, Any]					  =      (circles_number + 1 - sqrt(2 * circles_number )) / (
		    2 * (circles_number**2 + 1)
		)
		lowerCAmelCase__     :    Tuple					  =      circles_number * intersection_y

		lowerCAmelCase__     :    Any					  =      intersection_x * intersection_y / 2
		lowerCAmelCase__     :    Optional[Any]					  =      circle_bottom_arc_integral(
		    1 / 2 ) - circle_bottom_arc_integral(_a )

		return triangle_area + concave_region_area






def   lowerCamelCase_					( _a = 1 / 1_000 ):




		"""simple docstring"""






		lowerCAmelCase__     :    Any					  =      (1 - pi / 4) / 4

		for n in count(1 ):
				if concave_triangle_area(_a ) / l_section_area < fraction:
						return n

		return -1


if __name__ == "__main__":
		print(f'''{solution() = }''')




