


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     :   Optional[Any]   				=      get_logger(__name__)




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase = None,    )    ->Tuple:







      """simple docstring"""

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




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase=None,    _UpperCamelCase=None,    **_UpperCamelCase,    )    ->List[str]:







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

      # Random states
      try:
            lowercase			:   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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase = 0     )    ->Optional[int]:







      """simple docstring"""

      lowercase			:   Union[str, Any]					   =		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 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase = 0     )    ->List[str]:







      """simple docstring"""

      lowercase			:   Union[str, 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'''     )     )





from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version('''>=''', '''4.27.0''')):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      AudioLDMPipeline,
  )
else:
  from .pipeline_audioldm import AudioLDMPipeline

