
"""simple docstring"""


from typing import TYPE_CHECKING

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


_lowercase				 =					{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''ElectraTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ElectraForCausalLM''',
		    '''ElectraForMaskedLM''',
		    '''ElectraForMultipleChoice''',
		    '''ElectraForPreTraining''',
		    '''ElectraForQuestionAnswering''',
		    '''ElectraForSequenceClassification''',
		    '''ElectraForTokenClassification''',
		    '''ElectraModel''',
		    '''ElectraPreTrainedModel''',
		    '''load_tf_weights_in_electra''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFElectraForMaskedLM''',
		    '''TFElectraForMultipleChoice''',
		    '''TFElectraForPreTraining''',
		    '''TFElectraForQuestionAnswering''',
		    '''TFElectraForSequenceClassification''',
		    '''TFElectraForTokenClassification''',
		    '''TFElectraModel''',
		    '''TFElectraPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''FlaxElectraForCausalLM''',
		    '''FlaxElectraForMaskedLM''',
		    '''FlaxElectraForMultipleChoice''',
		    '''FlaxElectraForPreTraining''',
		    '''FlaxElectraForQuestionAnswering''',
		    '''FlaxElectraForSequenceClassification''',
		    '''FlaxElectraForTokenClassification''',
		    '''FlaxElectraModel''',
		    '''FlaxElectraPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
		from .tokenization_electra import ElectraTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_electra_fast import ElectraTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_electra import (
				    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				    ElectraPreTrainedModel,
				    load_tf_weights_in_electra,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_electra import (
				    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFElectraForMaskedLM,
				    TFElectraForMultipleChoice,
				    TFElectraForPreTraining,
				    TFElectraForQuestionAnswering,
				    TFElectraForSequenceClassification,
				    TFElectraForTokenClassification,
				    TFElectraModel,
				    TFElectraPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				    FlaxElectraPreTrainedModel,
				)

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

"""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


_lowercase				 =					get_logger(__name__)






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

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






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






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






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