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
  
  
  lowerCamelCase     =      get_logger(__name__)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str			,   _A   :				List[dict]			,   _A   :				list			,   _A   :				list			,   _A   :				int			,   _A   :				GradScaler = None			,   ):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for i, state in enumerate(_A							):
      a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
      a__						=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							):
      a__						=opt.state_dict()
      a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
      a__						=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							):
      a__						=scheduler.state_dict()
      a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
      a__						=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:
      a__						=scaler.state_dict()
      a__						=os.path.join(_A			,   _A							)
      torch.save(_A			,   _A							)
      logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
    # Random number generator states
    a__						={}
    a__						=F"""{RNG_STATE_NAME}_{process_index}.pkl"""
    a__						=random.getstate()
    a__						=np.random.get_state()
    a__						=torch.get_rng_state()
    if is_xpu_available():
      a__						=torch.xpu.get_rng_state_all()
    else:
      a__						=torch.cuda.get_rng_state_all()
    if is_tpu_available():
      a__						=xm.get_rng_state()
    a__						=os.path.join(_A			,   _A							)
    torch.save(_A			,   _A							)
    logger.info(F"""Random states saved in {output_states_file}"""							)
    return output_dir
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Union[str, Any]			,   _A   :				int			,   _A   :				int			,   _A   :				Union[str, Any]			,   _A   :				Tuple			,   _A   :				Optional[int]=None			,   _A   :				List[Any]=None			,   **_A   :				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__						='''cpu'''
    elif map_location == "on_device":
      a__						=PartialState().device
    # Model states
    for i, model in enumerate(_A							):
      a__						=F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
      a__						=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							):
      a__						=F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
      a__						=os.path.join(_A			,   _A							)
      a__						=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							):
      a__						=F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
      a__						=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:
      a__						=os.path.join(_A			,   _A							)
      scaler.load_state_dict(torch.load(_A							)							)
      logger.info('''GradScaler state loaded successfully'''							)
  
    # Random states
    try:
      a__						=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     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				Any			,   _A   :				int = 0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=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     UpperCAmelCase__       (    _A   :				int			,   _A   :				Any			,   _A   :				int = 0							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=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 typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  