

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'''							)							)



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__)
