

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			:					Optional[Any]						=			get_logger(__name__)





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  str			,  A__			:  Union[str, Any]			,  A__			:  Optional[Any]			,  A__			:  Optional[int]=0       ):
    os.makedirs(A__			,  exist_ok=A__       )
    with FSDP.state_dict_type(
        A__			,  fsdp_plugin.state_dict_type			,  fsdp_plugin.state_dict_config			,  fsdp_plugin.optim_state_dict_config       ):
        SCREAMING_SNAKE_CASE										=   model.state_dict()
        if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
            SCREAMING_SNAKE_CASE										=   F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
            SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  A__       )
            if accelerator.process_index == 0:
                logger.info(F"Saving model to {output_model_file}"       )
                torch.save(A__			,  A__       )
                logger.info(F"Model saved to {output_model_file}"       )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            SCREAMING_SNAKE_CASE										=   (
                F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
                if model_index == 0
                else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
            )
            SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  A__       )
            logger.info(F"Saving model to {output_model_file}"       )
            torch.save(A__			,  A__       )
            logger.info(F"Model saved to {output_model_file}"       )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  F"{MODEL_NAME}_{model_index}"       )
            os.makedirs(A__			,  exist_ok=A__       )
            logger.info(F"Saving model to {ckpt_dir}"       )
            SCREAMING_SNAKE_CASE										=   {"model": state_dict}

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





def 	__a     (      A__			:  Any			,  A__			:  Tuple			,  A__			:  Union[str, Any]			,  A__			:  Any			,  A__			:  List[Any]=0       ):
    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        A__			,  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(A__       ) != 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
            SCREAMING_SNAKE_CASE										=   F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
            SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  A__       )
            logger.info(F"Loading model from {input_model_file}"       )
            SCREAMING_SNAKE_CASE										=   torch.load(A__       )
            logger.info(F"Model loaded from {input_model_file}"       )
        elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
            SCREAMING_SNAKE_CASE										=   (
                F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
                if model_index == 0
                else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
            )
            SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  A__       )
            logger.info(F"Loading model from {input_model_file}"       )
            SCREAMING_SNAKE_CASE										=   torch.load(A__       )
            logger.info(F"Model loaded from {input_model_file}"       )
        elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
            SCREAMING_SNAKE_CASE										=   (
                os.path.join(A__			,  F"{MODEL_NAME}_{model_index}"       )
                if F"{MODEL_NAME}" not in input_dir
                else input_dir
            )
            logger.info(F"Loading model from {ckpt_dir}"       )
            SCREAMING_SNAKE_CASE										=   {"model": model.state_dict()}
            dist_cp.load_state_dict(
                state_dict=A__			,  storage_reader=dist_cp.FileSystemReader(A__       )			,  planner=DefaultLoadPlanner()			,  )
            SCREAMING_SNAKE_CASE										=   state_dict["model"]
            logger.info(F"Model loaded from {ckpt_dir}"       )
        model.load_state_dict(A__       )





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





def 	__a     (      A__			:  str			,  A__			:  List[str]			,  A__			:  List[Any]			,  A__			:  List[str]			,  A__			:  str			,  A__			:  Dict=0       ):
    accelerator.wait_for_everyone()
    with FSDP.state_dict_type(
        A__			,  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:
            SCREAMING_SNAKE_CASE										=   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:
            SCREAMING_SNAKE_CASE										=   (
                F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
            )
            SCREAMING_SNAKE_CASE										=   os.path.join(A__			,  A__       )
            logger.info(F"Loading Optimizer state from {input_optimizer_file}"       )
            SCREAMING_SNAKE_CASE										=   torch.load(A__       )
            logger.info(F"Optimizer state loaded from {input_optimizer_file}"       )
        else:
            SCREAMING_SNAKE_CASE										=   (
                os.path.join(A__			,  F"{OPTIMIZER_NAME}_{optimizer_index}"       )
                if F"{OPTIMIZER_NAME}" not in input_dir
                else input_dir
            )
            logger.info(F"Loading Optimizer from {ckpt_dir}"       )
            SCREAMING_SNAKE_CASE										=   load_sharded_optimizer_state_dict(
                model_state_dict=model.state_dict()			,  optimizer_key="optimizer"			,  storage_reader=dist_cp.FileSystemReader(A__       )			,  )
            SCREAMING_SNAKE_CASE										=   optim_state["optimizer"]
            logger.info(F"Optimizer loaded from {ckpt_dir}"       )
        SCREAMING_SNAKE_CASE										=   FSDP.optim_state_dict_to_load(A__			,  A__			,  A__       )
        optimizer.load_state_dict(A__       )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

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