
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 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





  """simple docstring"""
  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							):
    __SCREAMING_SNAKE_CASE      :	List[str]					=		model.state_dict()
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
      __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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:
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		(
          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      :	str					=		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:
      __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		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}'''							)
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		{'''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 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





  """simple docstring"""
  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
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		F'''{MODEL_NAME}.bin''' if model_index == 0 else F'''{MODEL_NAME}_{model_index}.bin'''
      __SCREAMING_SNAKE_CASE      :	int					=		os.path.join(snake_case       ,			snake_case							)
      logger.info(F'''Loading model from {input_model_file}'''							)
      __SCREAMING_SNAKE_CASE      :	str					=		torch.load(snake_case							)
      logger.info(F'''Model loaded from {input_model_file}'''							)
    elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(
          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      :	Tuple					=		os.path.join(snake_case       ,			snake_case							)
      logger.info(F'''Loading model from {input_model_file}'''							)
      __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		torch.load(snake_case							)
      logger.info(F'''Model loaded from {input_model_file}'''							)
    elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		(
          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}'''							)
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		{'''model''': model.state_dict()}
      dist_cp.load_state_dict(
          state_dict=snake_case       ,			storage_reader=dist_cp.FileSystemReader(snake_case							)       ,			planner=DefaultLoadPlanner()       ,			)
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		state_dict['''model''']
      logger.info(F'''Model loaded from {ckpt_dir}'''							)
    model.load_state_dict(snake_case							)


def 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





  """simple docstring"""
  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							):
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		FSDP.optim_state_dict(snake_case       ,			snake_case							)
    if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
      if accelerator.process_index == 0:
        __SCREAMING_SNAKE_CASE      :	Dict					=		(
            F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
        )
        __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		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:
      __SCREAMING_SNAKE_CASE      :	List[str]					=		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 							a__					(							snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case       ,			snake_case=0							):





  """simple docstring"""
  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:
      __SCREAMING_SNAKE_CASE      :	Tuple					=		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      :	int					=		(
          F'''{OPTIMIZER_NAME}.bin''' if optimizer_index == 0 else F'''{OPTIMIZER_NAME}_{optimizer_index}.bin'''
      )
      __SCREAMING_SNAKE_CASE      :	Any					=		os.path.join(snake_case       ,			snake_case							)
      logger.info(F'''Loading Optimizer state from {input_optimizer_file}'''							)
      __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		torch.load(snake_case							)
      logger.info(F'''Optimizer state loaded from {input_optimizer_file}'''							)
    else:
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		(
          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}'''							)
      __SCREAMING_SNAKE_CASE      :	str					=		load_sharded_optimizer_state_dict(
          model_state_dict=model.state_dict()       ,			optimizer_key='''optimizer'''       ,			storage_reader=dist_cp.FileSystemReader(snake_case							)       ,			)
      __SCREAMING_SNAKE_CASE      :	Tuple					=		optim_state['''optimizer''']
      logger.info(F'''Optimizer loaded from {ckpt_dir}'''							)
    __SCREAMING_SNAKE_CASE      :	List[str]					=		FSDP.optim_state_dict_to_load(snake_case       ,			snake_case       ,			snake_case							)
    optimizer.load_state_dict(snake_case							)

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


