

"""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


_a							=							get_logger(__name__)




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	Any,      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Tuple=0)     ->						Optional[Any]:
   '''simple docstring'''


   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):
      __lowercase								=						model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         __lowercase								=						F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         __lowercase								=						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:
         __lowercase								=						(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         __lowercase								=						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:
         __lowercase								=						os.path.join(UpperCamelCase_,      F"""{MODEL_NAME}_{model_index}""")
         os.makedirs(UpperCamelCase_,      exist_ok=UpperCamelCase_)
         logger.info(F"""Saving model to {ckpt_dir}""")
         __lowercase								=						{"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   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	Optional[Any]=0)     ->						List[Any]:
   '''simple docstring'''


   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
         __lowercase								=						F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         __lowercase								=						os.path.join(UpperCamelCase_,      UpperCamelCase_)
         logger.info(F"""Loading model from {input_model_file}""")
         __lowercase								=						torch.load(UpperCamelCase_)
         logger.info(F"""Model loaded from {input_model_file}""")
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         __lowercase								=						(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         __lowercase								=						os.path.join(UpperCamelCase_,      UpperCamelCase_)
         logger.info(F"""Loading model from {input_model_file}""")
         __lowercase								=						torch.load(UpperCamelCase_)
         logger.info(F"""Model loaded from {input_model_file}""")
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         __lowercase								=						(
             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}""")
         __lowercase								=						{"model": model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=UpperCamelCase_,      storage_reader=dist_cp.FileSystemReader(UpperCamelCase_),      planner=DefaultLoadPlanner(),      )
         __lowercase								=						state_dict["model"]
         logger.info(F"""Model loaded from {ckpt_dir}""")
      model.load_state_dict(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Optional[Any],      UpperCamelCase_					:	str,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Union[str, Any]=0)     ->						Tuple:
   '''simple docstring'''


   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):
      __lowercase								=						FSDP.optim_state_dict(UpperCamelCase_,      UpperCamelCase_)
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            __lowercase								=						(
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            __lowercase								=						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:
         __lowercase								=						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   _A  (  UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	List[Any],      UpperCamelCase_					:	Any,      UpperCamelCase_					:	str=0)     ->						str:
   '''simple docstring'''


   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:
         __lowercase								=						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:
         __lowercase								=						(
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         __lowercase								=						os.path.join(UpperCamelCase_,      UpperCamelCase_)
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}""")
         __lowercase								=						torch.load(UpperCamelCase_)
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}""")
      else:
         __lowercase								=						(
             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}""")
         __lowercase								=						load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict(),      optimizer_key="optimizer",      storage_reader=dist_cp.FileSystemReader(UpperCamelCase_),      )
         __lowercase								=						optim_state["optimizer"]
         logger.info(F"""Optimizer loaded from {ckpt_dir}""")
      __lowercase								=						FSDP.optim_state_dict_to_load(UpperCamelCase_,      UpperCamelCase_,      UpperCamelCase_)
      optimizer.load_state_dict(UpperCamelCase_)







"""simple docstring"""





import argparse
import os
import re


_a							=							'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a							=							re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_a							=							re.compile(r'\s*\(\s*"(\S[^"]+)"')




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   with open(UpperCamelCase_,      "r",      encoding="utf-8") as f:
      __lowercase								=						f.read()

   __lowercase								=						content.split("\n")
   __lowercase								=						[]
   __lowercase								=						0
   while line_idx < len(UpperCamelCase_):
      if _re_intro_mapping.search(lines[line_idx]) is not None:
         __lowercase								=						len(re.search(r"^(\s*)\S",      lines[line_idx]).groups()[0]) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "("):
            new_lines.append(lines[line_idx])
            line_idx += 1

         __lowercase								=						[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               __lowercase								=						line_idx
               while not lines[line_idx].startswith(" " * indent + ")"):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1]))
            else:
               blocks.append(lines[line_idx])
            line_idx += 1

         # Sort blocks by their identifiers
         __lowercase								=						sorted(UpperCamelCase_,      key=lambda UpperCamelCase_: _re_identifier.search(UpperCamelCase_).groups()[0])
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx])
         line_idx += 1

   if overwrite:
      with open(UpperCamelCase_,      "w",      encoding="utf-8") as f:
         f.write("\n".join(UpperCamelCase_))
   elif "\n".join(UpperCamelCase_) != content:
      return True




def   _A  (  UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						[os.path.join(UpperCamelCase_,      UpperCamelCase_) for f in os.listdir(UpperCamelCase_) if f.endswith(".py")]
   __lowercase								=						[sort_auto_mapping(UpperCamelCase_,      overwrite=UpperCamelCase_) for fname in fnames]

   if not overwrite and any(UpperCamelCase_):
      __lowercase								=						[f for f, d in zip(UpperCamelCase_,      UpperCamelCase_) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_)}. Run `make style` to fix"""
          " this.")


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   _a							=							parser.parse_args()

   sort_all_auto_mappings(not args.check_only)




