



import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
     import torch.distributed.checkpoint as dist_cp
     from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
     from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
     from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


a_    :  int						  =		get_logger(__name__)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=0			):


      """simple docstring"""



      os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__			)
      with FSDP.state_dict_type(
          UpperCAmelCase__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config			):
            lowerCamelCase             =		model.state_dict()
            if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                  lowerCamelCase             =		F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
                  if accelerator.process_index == 0:
                        logger.info(F"""Saving model to {output_model_file}"""			)
                        torch.save(UpperCAmelCase__ , UpperCAmelCase__			)
                        logger.info(F"""Model saved to {output_model_file}"""			)
            elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                  lowerCamelCase             =		(
                      F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
                      if model_index == 0
                      else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
                  )
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
                  logger.info(F"""Saving model to {output_model_file}"""			)
                  torch.save(UpperCAmelCase__ , UpperCAmelCase__			)
                  logger.info(F"""Model saved to {output_model_file}"""			)
            elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , F"""{MODEL_NAME}_{model_index}"""			)
                  os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__			)
                  logger.info(F"""Saving model to {ckpt_dir}"""			)
                  lowerCamelCase             =		{"model": state_dict}

                  dist_cp.save_state_dict(
                      state_dict=UpperCAmelCase__ , storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase__			) , planner=DefaultSavePlanner() , )
                  logger.info(F"""Model saved to {ckpt_dir}"""			)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=0			):


      """simple docstring"""



      accelerator.wait_for_everyone()
      with FSDP.state_dict_type(
          UpperCAmelCase__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config			):
            if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                  if type(UpperCAmelCase__			) != FSDP and accelerator.process_index != 0:
                        if not fsdp_plugin.sync_module_states:
                              raise ValueError(
                                  "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
                                  "initializing FSDP object"			)
                        return
                  lowerCamelCase             =		F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
                  logger.info(F"""Loading model from {input_model_file}"""			)
                  lowerCamelCase             =		torch.load(UpperCAmelCase__			)
                  logger.info(F"""Model loaded from {input_model_file}"""			)
            elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
                  lowerCamelCase             =		(
                      F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
                      if model_index == 0
                      else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
                  )
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
                  logger.info(F"""Loading model from {input_model_file}"""			)
                  lowerCamelCase             =		torch.load(UpperCAmelCase__			)
                  logger.info(F"""Model loaded from {input_model_file}"""			)
            elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                  lowerCamelCase             =		(
                      os.path.join(UpperCAmelCase__ , F"""{MODEL_NAME}_{model_index}"""			)
                      if F"""{MODEL_NAME}""" not in input_dir
                      else input_dir
                  )
                  logger.info(F"""Loading model from {ckpt_dir}"""			)
                  lowerCamelCase             =		{"model": model.state_dict()}
                  dist_cp.load_state_dict(
                      state_dict=UpperCAmelCase__ , storage_reader=dist_cp.FileSystemReader(UpperCAmelCase__			) , planner=DefaultLoadPlanner() , )
                  lowerCamelCase             =		state_dict["model"]
                  logger.info(F"""Model loaded from {ckpt_dir}"""			)
            model.load_state_dict(UpperCAmelCase__			)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=0			):


      """simple docstring"""



      os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__			)
      with FSDP.state_dict_type(
          UpperCAmelCase__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config			):
            lowerCamelCase             =		FSDP.optim_state_dict(UpperCAmelCase__ , UpperCAmelCase__			)
            if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                  if accelerator.process_index == 0:
                        lowerCamelCase             =		(
                            F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
                        )
                        lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
                        logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""			)
                        torch.save(UpperCAmelCase__ , UpperCAmelCase__			)
                        logger.info(F"""Optimizer state saved in {output_optimizer_file}"""			)
            else:
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , F"""{OPTIMIZER_NAME}_{optimizer_index}"""			)
                  os.makedirs(UpperCAmelCase__ , exist_ok=UpperCAmelCase__			)
                  logger.info(F"""Saving Optimizer state to {ckpt_dir}"""			)
                  dist_cp.save_state_dict(
                      state_dict={"optimizer": optim_state} , storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase__			) , planner=DefaultSavePlanner() , )
                  logger.info(F"""Optimizer state saved in {ckpt_dir}"""			)






def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=0			):


      """simple docstring"""



      accelerator.wait_for_everyone()
      with FSDP.state_dict_type(
          UpperCAmelCase__ , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config			):
            if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
                  lowerCamelCase             =		None
                  # below check should work but currently it isn't working (mostly opytorch issue),
                  # in the meantime disabling it at the cost of excess memory usage
                  # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
                  lowerCamelCase             =		(
                      F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
                  )
                  lowerCamelCase             =		os.path.join(UpperCAmelCase__ , UpperCAmelCase__			)
                  logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""			)
                  lowerCamelCase             =		torch.load(UpperCAmelCase__			)
                  logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""			)
            else:
                  lowerCamelCase             =		(
                      os.path.join(UpperCAmelCase__ , F"""{OPTIMIZER_NAME}_{optimizer_index}"""			)
                      if F"""{OPTIMIZER_NAME}""" not in input_dir
                      else input_dir
                  )
                  logger.info(F"""Loading Optimizer from {ckpt_dir}"""			)
                  lowerCamelCase             =		load_sharded_optimizer_state_dict(
                      model_state_dict=model.state_dict() , optimizer_key="optimizer" , storage_reader=dist_cp.FileSystemReader(UpperCAmelCase__			) , )
                  lowerCamelCase             =		optim_state["optimizer"]
                  logger.info(F"""Optimizer loaded from {ckpt_dir}"""			)
            lowerCamelCase             =		FSDP.optim_state_dict_to_load(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
            optimizer.load_state_dict(UpperCAmelCase__			)




from typing import TYPE_CHECKING

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


a_    :  Tuple						  =		{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_    :  Union[str, Any]						  =		[
         'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
         'UniSpeechForCTC',
         'UniSpeechForPreTraining',
         'UniSpeechForSequenceClassification',
         'UniSpeechModel',
         'UniSpeechPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_unispeech import (
              UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
              UniSpeechForCTC,
              UniSpeechForPreTraining,
              UniSpeechForSequenceClassification,
              UniSpeechModel,
              UniSpeechPreTrainedModel,
          )

else:
     import sys

     a_    :  Optional[Any]						  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)