"""simple docstring"""


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


UpperCAmelCase__  		=						get_logger(__name__)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase=0		):







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

						dist_cp.save_state_dict(
						    state_dict=lowercase   ,storage_writer=dist_cp.FileSystemWriter(lowercase		)   ,planner=DefaultSavePlanner()   ,)
						logger.info(f'''Model saved to {ckpt_dir}'''		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase=0		):







		"""simple docstring"""
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    lowercase   ,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(lowercase		) != 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
						_UpperCAmelCase						 =      f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
						_UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)
						logger.info(f'''Loading model from {input_model_file}'''		)
						_UpperCAmelCase						 =      torch.load(lowercase		)
						logger.info(f'''Model loaded from {input_model_file}'''		)
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						_UpperCAmelCase						 =      (
						    f'''{MODEL_NAME}_rank{accelerator.process_index}.bin'''
						    if model_index == 0
						    else f'''{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'''
						)
						_UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)
						logger.info(f'''Loading model from {input_model_file}'''		)
						_UpperCAmelCase						 =      torch.load(lowercase		)
						logger.info(f'''Model loaded from {input_model_file}'''		)
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						_UpperCAmelCase						 =      (
						    os.path.join(lowercase   ,f'''{MODEL_NAME}_{model_index}'''		)
						    if f'''{MODEL_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(f'''Loading model from {ckpt_dir}'''		)
						_UpperCAmelCase						 =      {"""model""": model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=lowercase   ,storage_reader=dist_cp.FileSystemReader(lowercase		)   ,planner=DefaultLoadPlanner()   ,)
						_UpperCAmelCase						 =      state_dict["""model"""]
						logger.info(f'''Model loaded from {ckpt_dir}'''		)
				model.load_state_dict(lowercase		)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase=0		):







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






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase   ,lowercase=0		):







		"""simple docstring"""
		accelerator.wait_for_everyone()
		with FSDP.state_dict_type(
		    lowercase   ,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:
						_UpperCAmelCase						 =      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:
						_UpperCAmelCase						 =      (
						    f'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else f'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
						)
						_UpperCAmelCase						 =      os.path.join(lowercase   ,lowercase		)
						logger.info(f'''Loading Optimizer state from {input_optimizer_file}'''		)
						_UpperCAmelCase						 =      torch.load(lowercase		)
						logger.info(f'''Optimizer state loaded from {input_optimizer_file}'''		)
				else:
						_UpperCAmelCase						 =      (
						    os.path.join(lowercase   ,f'''{OPTIMIZER_NAME}_{optimizer_index}'''		)
						    if f'''{OPTIMIZER_NAME}''' not in input_dir
						    else input_dir
						)
						logger.info(f'''Loading Optimizer from {ckpt_dir}'''		)
						_UpperCAmelCase						 =      load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict()   ,optimizer_key="""optimizer"""   ,storage_reader=dist_cp.FileSystemReader(lowercase		)   ,)
						_UpperCAmelCase						 =      optim_state["""optimizer"""]
						logger.info(f'''Optimizer loaded from {ckpt_dir}'''		)
				_UpperCAmelCase						 =      FSDP.optim_state_dict_to_load(lowercase   ,lowercase   ,lowercase		)
				optimizer.load_state_dict(lowercase		)

"""simple docstring"""


from typing import TYPE_CHECKING

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


UpperCAmelCase__  		=						{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """RobertaPreLayerNormForCausalLM""",
        """RobertaPreLayerNormForMaskedLM""",
        """RobertaPreLayerNormForMultipleChoice""",
        """RobertaPreLayerNormForQuestionAnswering""",
        """RobertaPreLayerNormForSequenceClassification""",
        """RobertaPreLayerNormForTokenClassification""",
        """RobertaPreLayerNormModel""",
        """RobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFRobertaPreLayerNormForCausalLM""",
        """TFRobertaPreLayerNormForMaskedLM""",
        """TFRobertaPreLayerNormForMultipleChoice""",
        """TFRobertaPreLayerNormForQuestionAnswering""",
        """TFRobertaPreLayerNormForSequenceClassification""",
        """TFRobertaPreLayerNormForTokenClassification""",
        """TFRobertaPreLayerNormMainLayer""",
        """TFRobertaPreLayerNormModel""",
        """TFRobertaPreLayerNormPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase__  		=						[
        """FlaxRobertaPreLayerNormForCausalLM""",
        """FlaxRobertaPreLayerNormForMaskedLM""",
        """FlaxRobertaPreLayerNormForMultipleChoice""",
        """FlaxRobertaPreLayerNormForQuestionAnswering""",
        """FlaxRobertaPreLayerNormForSequenceClassification""",
        """FlaxRobertaPreLayerNormForTokenClassification""",
        """FlaxRobertaPreLayerNormModel""",
        """FlaxRobertaPreLayerNormPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

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