"""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


lowerCamelCase      : Dict       =get_logger(__name__)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				List[dict]  ,       _SCREAMING_SNAKE_CASE				:				list  ,       _SCREAMING_SNAKE_CASE				:				list  ,       _SCREAMING_SNAKE_CASE				:				int  ,       _SCREAMING_SNAKE_CASE				:				GradScaler = None  ,       )    ->     List[str]:







     '''simple docstring'''


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




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				str  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				Any  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				Optional[Any]=None  ,       _SCREAMING_SNAKE_CASE				:				Optional[int]=None  ,       **_SCREAMING_SNAKE_CASE				:				Tuple  ,       )    ->     Dict:







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

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

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

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

     # Random states
     try:
          __A					:					Dict								=		torch.load(os.path.join(_SCREAMING_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 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				Optional[int]  ,       _SCREAMING_SNAKE_CASE				:				Tuple  ,       _SCREAMING_SNAKE_CASE				:				int = 0						)    ->     int:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		Path(_SCREAMING_SNAKE_CASE						) / F'custom_checkpoint_{index}.pkl'
     logger.info(F'Saving the state of {get_pretty_name(_SCREAMING_SNAKE_CASE						)} to {save_location}'						)
     torch.save(obj.state_dict()  ,       _SCREAMING_SNAKE_CASE						)




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				List[Any]  ,       _SCREAMING_SNAKE_CASE				:				List[str]  ,       _SCREAMING_SNAKE_CASE				:				int = 0						)    ->     List[str]:







     '''simple docstring'''


     __A					:					Union[str, Any]								=		F'{path}/custom_checkpoint_{index}.pkl'
     logger.info(F'Loading the state of {get_pretty_name(_SCREAMING_SNAKE_CASE						)} from {load_location}'						)
     obj.load_state_dict(torch.load(_SCREAMING_SNAKE_CASE  ,       map_location='cpu'						)						)




"""simple docstring"""







from typing import Dict, Optional, Tuple

import torch




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor						)    ->     torch.Tensor:







     '''simple docstring'''


     __A					:					List[str]								=		boundaries[1] - boundaries[0]
     __A					:					int								=		boundaries + step / 2
     __A					:					Union[str, Any]								=		torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1						)]  ,       dim=0						)
     return bin_centers




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       )    ->     Tuple[torch.Tensor, torch.Tensor]:







     '''simple docstring'''


     __A					:					str								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     return (
         torch.sum(aligned_distance_error_probs * bin_centers  ,       dim=-1						),
         bin_centers[-1],
     )




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       **_SCREAMING_SNAKE_CASE				:				Optional[Any]  ,       )    ->     Dict[str, torch.Tensor]:







     '''simple docstring'''


     __A					:					int								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Any								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)
     __A						,		__A					:					Tuple								=		_calculate_expected_aligned_error(
         alignment_confidence_breaks=_SCREAMING_SNAKE_CASE  ,       aligned_distance_error_probs=_SCREAMING_SNAKE_CASE  ,       )

     return {
         "aligned_confidence_probs": aligned_confidence_probs,
         "predicted_aligned_error": predicted_aligned_error,
         "max_predicted_aligned_error": max_predicted_aligned_error,
     }




def 						_lowercase				(  _SCREAMING_SNAKE_CASE				:				torch.Tensor  ,       _SCREAMING_SNAKE_CASE				:				Optional[torch.Tensor] = None  ,       _SCREAMING_SNAKE_CASE				:				int = 31  ,       _SCREAMING_SNAKE_CASE				:				int = 64  ,       _SCREAMING_SNAKE_CASE				:				float = 1E-8  ,       **_SCREAMING_SNAKE_CASE				:				Union[str, Any]  ,       )    ->     torch.Tensor:







     '''simple docstring'''


     if residue_weights is None:
          __A					:					Optional[int]								=		logits.new_ones(logits.shape[-2]						)

     __A					:					List[str]								=		torch.linspace(0  ,       _SCREAMING_SNAKE_CASE  ,       steps=(no_bins - 1)  ,       device=logits.device						)

     __A					:					Tuple								=		_calculate_bin_centers(_SCREAMING_SNAKE_CASE						)
     torch.sum(_SCREAMING_SNAKE_CASE						)
     __A					:					int								=		logits.shape[-2]
     __A					:					str								=		max(_SCREAMING_SNAKE_CASE  ,       19						)

     __A					:					Tuple								=		1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

     __A					:					Tuple								=		torch.nn.functional.softmax(_SCREAMING_SNAKE_CASE  ,       dim=-1						)

     __A					:					List[str]								=		1.0 / (1 + (bin_centers**2) / (da**2))
     __A					:					int								=		torch.sum(probs * tm_per_bin  ,       dim=-1						)

     __A					:					Any								=		residue_weights / (eps + residue_weights.sum())
     __A					:					List[Any]								=		torch.sum(predicted_tm_term * normed_residue_mask  ,       dim=-1						)

     __A					:					int								=		per_alignment * residue_weights

     __A					:					Optional[int]								=		(weighted == torch.max(_SCREAMING_SNAKE_CASE						)).nonzero()[0]
     return per_alignment[tuple(_SCREAMING_SNAKE_CASE						)]



