






"""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


UpperCamelCase											=     get_logger(__name__)






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







  '''simple docstring'''







  for i, state in enumerate(UpperCamelCase_       ):
    _a					      =						f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    save(UpperCamelCase_  ,			UpperCamelCase_       )
    logger.info(f'''Model weights saved in {output_model_file}'''       )
  # Optimizer states
  for i, opt in enumerate(UpperCamelCase_       ):
    _a					      =						opt.state_dict()
    _a					      =						f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    save(UpperCamelCase_  ,			UpperCamelCase_       )
    logger.info(f'''Optimizer state saved in {output_optimizer_file}'''       )
  # Scheduler states
  for i, scheduler in enumerate(UpperCamelCase_       ):
    _a					      =						scheduler.state_dict()
    _a					      =						f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    save(UpperCamelCase_  ,			UpperCamelCase_       )
    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(UpperCamelCase_  ,			UpperCamelCase_       )
    torch.save(UpperCamelCase_  ,			UpperCamelCase_       )
    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(UpperCamelCase_  ,			UpperCamelCase_       )
  torch.save(UpperCamelCase_  ,			UpperCamelCase_       )
  logger.info(f'''Random states saved in {output_states_file}'''       )
  return output_dir






def 							lowerCAmelCase			(   UpperCamelCase_:				Any  ,			UpperCamelCase_:				int  ,			UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				Any  ,			UpperCamelCase_:				Optional[Any]=None  ,			UpperCamelCase_:				Tuple=None  ,			**UpperCamelCase_:				str  ,			)							->				Any:







  '''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(UpperCamelCase_       ):
    _a					      =						f'''{MODEL_NAME}.bin''' if i == 0 else f'''{MODEL_NAME}_{i}.bin'''
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    models[i].load_state_dict(torch.load(UpperCamelCase_  ,			map_location=UpperCamelCase_       )  ,			**UpperCamelCase_       )
  logger.info("All model weights loaded successfully"       )

  # Optimizer states
  for i, opt in enumerate(UpperCamelCase_       ):
    _a					      =						f'''{OPTIMIZER_NAME}.bin''' if i == 0 else f'''{OPTIMIZER_NAME}_{i}.bin'''
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    _a					      =						torch.load(UpperCamelCase_  ,			map_location=UpperCamelCase_       )
    optimizers[i].load_state_dict(UpperCamelCase_       )
  logger.info("All optimizer states loaded successfully"       )

  # Scheduler states
  for i, scheduler in enumerate(UpperCamelCase_       ):
    _a					      =						f'''{SCHEDULER_NAME}.bin''' if i == 0 else f'''{SCHEDULER_NAME}_{i}.bin'''
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    scheduler.load_state_dict(torch.load(UpperCamelCase_       )       )
  logger.info("All scheduler states loaded successfully"       )

  # GradScaler state
  if scaler is not None:
    _a					      =						os.path.join(UpperCamelCase_  ,			UpperCamelCase_       )
    scaler.load_state_dict(torch.load(UpperCamelCase_       )       )
    logger.info("GradScaler state loaded successfully"       )

  # Random states
  try:
    _a					      =						torch.load(os.path.join(UpperCamelCase_  ,			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			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				Union[str, Any]  ,			UpperCamelCase_:				int = 0       )							->				Any:







  '''simple docstring'''







  _a					      =						Path(UpperCamelCase_       ) / f'''custom_checkpoint_{index}.pkl'''
  logger.info(f'''Saving the state of {get_pretty_name(UpperCamelCase_       )} to {save_location}'''       )
  torch.save(obj.state_dict()  ,			UpperCamelCase_       )






def 							lowerCAmelCase			(   UpperCamelCase_:				Optional[Any]  ,			UpperCamelCase_:				Tuple  ,			UpperCamelCase_:				int = 0       )							->				Optional[Any]:







  '''simple docstring'''







  _a					      =						f'''{path}/custom_checkpoint_{index}.pkl'''
  logger.info(f'''Loading the state of {get_pretty_name(UpperCamelCase_       )} from {load_location}'''       )
  obj.load_state_dict(torch.load(UpperCamelCase_  ,			map_location="cpu"       )       )










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase											=     {"""configuration_pegasus""": ["""PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusConfig"""]}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     ["""PegasusTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     ["""PegasusTokenizerFast"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """PegasusForCausalLM""",
         """PegasusForConditionalGeneration""",
         """PegasusModel""",
         """PegasusPreTrainedModel""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """TFPegasusForConditionalGeneration""",
         """TFPegasusModel""",
         """TFPegasusPreTrainedModel""",
     ]

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase											=     [
         """FlaxPegasusForConditionalGeneration""",
         """FlaxPegasusModel""",
         """FlaxPegasusPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus import PegasusTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_pegasus_fast import PegasusTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_pegasus import (
              PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
              PegasusForCausalLM,
              PegasusForConditionalGeneration,
              PegasusModel,
              PegasusPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_pegasus import (
              FlaxPegasusForConditionalGeneration,
              FlaxPegasusModel,
              FlaxPegasusPreTrainedModel,
          )

else:
     import sys

     UpperCamelCase											=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


