




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__				:      List[Any]	   = get_logger(__name__)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					List[Any]:
     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):
          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(__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:
               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(__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:
               a     =				os.path.join(__UpperCamelCase		, f'''{MODEL_NAME}_{model_index}''')
               os.makedirs(__UpperCamelCase		, exist_ok=__UpperCamelCase)
               logger.info(f'''Saving model to {ckpt_dir}''')
               a     =				{"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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					Optional[int]:
     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
               a     =				f'''{MODEL_NAME}.bin''' if model_index == 0 else f'''{MODEL_NAME}_{model_index}.bin'''
               a     =				os.path.join(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Loading model from {input_model_file}''')
               a     =				torch.load(__UpperCamelCase)
               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(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Loading model from {input_model_file}''')
               a     =				torch.load(__UpperCamelCase)
               logger.info(f'''Model loaded from {input_model_file}''')
          elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
               a     =				(
                   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}''')
               a     =				{"model": model.state_dict()}
               dist_cp.load_state_dict(
                   state_dict=__UpperCamelCase		, storage_reader=dist_cp.FileSystemReader(__UpperCamelCase)		, planner=DefaultLoadPlanner()		, )
               a     =				state_dict["model"]
               logger.info(f'''Model loaded from {ckpt_dir}''')
          model.load_state_dict(__UpperCamelCase)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					List[Any]:
     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):
          a     =				FSDP.optim_state_dict(__UpperCamelCase		, __UpperCamelCase)
          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(__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:
               a     =				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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase=0)      ->					Optional[Any]:
     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:
               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(__UpperCamelCase		, __UpperCamelCase)
               logger.info(f'''Loading Optimizer state from {input_optimizer_file}''')
               a     =				torch.load(__UpperCamelCase)
               logger.info(f'''Optimizer state loaded from {input_optimizer_file}''')
          else:
               a     =				(
                   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}''')
               a     =				load_sharded_optimizer_state_dict(
                   model_state_dict=model.state_dict()		, optimizer_key="optimizer"		, storage_reader=dist_cp.FileSystemReader(__UpperCamelCase)		, )
               a     =				optim_state["optimizer"]
               logger.info(f'''Optimizer loaded from {ckpt_dir}''')
          a     =				FSDP.optim_state_dict_to_load(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)
          optimizer.load_state_dict(__UpperCamelCase)







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__				:      Tuple	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}




class       a__	( UpperCamelCase__       , UpperCamelCase__				):
     a		:      Any		 =     """resnet"""
     a		:      Tuple		 =     ["""basic""", """bottleneck"""]







     def __init__(     self  ,		A=3  ,		A=64  ,		A=[256, 512, 1024, 2048]  ,		A=[3, 4, 6, 3]  ,		A="bottleneck"  ,		A="relu"  ,		A=False  ,		A=None  ,		A=None  ,		**A  ,		)  ->   Any:



          '''simple docstring'''



          super().__init__(**A			)
          if layer_type not in self.layer_types:
               raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
          a     =				num_channels
          a     =				embedding_size
          a     =				hidden_sizes
          a     =				depths
          a     =				layer_type
          a     =				hidden_act
          a     =				downsample_in_first_stage
          a     =				["stem"] + [F'''stage{idx}''' for idx in range(1  ,		len(A			) + 1			)]
          a  ,				a     =				get_aligned_output_features_output_indices(
              out_features=A  ,		out_indices=A  ,		stage_names=self.stage_names			)







class       a__	( UpperCamelCase__				):
     a		:      Optional[int]		 =     version.parse("""1.11"""				)







     @property
     def       lowerCAmelCase_    (     self			)  ->   Mapping[str, Mapping[int, str]]:



          '''simple docstring'''



          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]			)







     @property
     def       lowerCAmelCase_    (     self			)  ->   float:



          '''simple docstring'''



          return 1e-3

