
"""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			:       int									=				get_logger(__name__)






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






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





"""simple docstring"""

import argparse
from collections import defaultdict






def 							A		(       snake_case   :Tuple       ,      snake_case   :List[Any]       ,      snake_case   :List[Any]       ,      snake_case   :Union[str, Any]       ,      snake_case   :Optional[Any]	)   ->			Union[str, Any]:
 __UpperCamelCase       						= f'{file}_{class_name}_{test_name}'
 done_test[_id] += 1

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= f'class {class_name}('
 __UpperCamelCase       						= f'{4 * " "}def {test_name}('
 __UpperCamelCase       						= f'{8 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= f'{1_6 * " "}{correct_line.split()[0]}'
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= False
 __UpperCamelCase       						= 0
 __UpperCamelCase       						= 0

 __UpperCamelCase       						= []
 for line in lines:
  if line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and line.startswith(snake_case	):
   __UpperCamelCase       						= True
  elif in_class and in_func and (line.startswith(snake_case	) or line.startswith(snake_case	)):
   __UpperCamelCase       						= len(line.split(correct_line.split()[0]	)[0]	)
   count += 1

   if count == done_test[_id]:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line:
   if ")" not in line:
    continue
   else:
    __UpperCamelCase       						= True

  if in_class and in_func and in_line and insert_line:
   new_lines.append(f'{spaces * " "}{correct_line}'	)
   __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= __UpperCamelCase       						= False
  else:
   new_lines.append(snake_case	)

 with open(snake_case       ,      'w'	) as f:
  for line in new_lines:
   f.write(snake_case	)






def 							A		(       snake_case   :List[str]       ,      snake_case   :str=None	)   ->			Tuple:
 if fail is not None:
  with open(snake_case       ,      'r'	) as f:
   __UpperCamelCase       						= {l.strip() for l in f.readlines()}
 else:
  __UpperCamelCase       						= None

 with open(snake_case       ,      'r'	) as f:
  __UpperCamelCase       						= f.readlines()

 __UpperCamelCase       						= defaultdict(snake_case	)
 for line in correct_lines:
  __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase       						= line.split(';'	)
  if test_failures is None or "::".join([file, class_name, test_name]	) in test_failures:
   overwrite_file(snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case	)


if __name__ == "__main__":
      UpperCamelCase			:       str									=				argparse.ArgumentParser()
      parser.add_argument("--correct_filename", help="filename of tests with expected result")
      parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
      UpperCamelCase			:       List[Any]									=				parser.parse_args()

      main(args.correct_filename, args.fail_filename)



