


from heapq import heappop, heappush

import numpy as np




def    a   (							A__      ,    A__      ,    A__      ,    A__      ,    )	-> tuple[float | int, list[tuple[int, int]]]:




      '''simple docstring'''

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		grid.shape
      SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[-1, 1, 0, 0]
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		[0, 0, -1, 1]
      if allow_diagonal:
            dx += [-1, -1, 1, 1]
            dy += [-1, 1, -1, 1]

      SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  str						     =		[(0, source)], set()
      SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		np.full((rows, cols)      ,    np.inf							)
      SCREAMING_SNAKE_CASE__       :  List[Any]						     =		0
      SCREAMING_SNAKE_CASE__       :  Any						     =		np.empty((rows, cols)      ,    dtype=A__							)
      SCREAMING_SNAKE_CASE__       :  List[str]						     =		None

      while queue:
            ((SCREAMING_SNAKE_CASE__)     ,       (SCREAMING_SNAKE_CASE__))       :  Any						     =		heappop(A__							)
            if (x, y) in visited:
                  continue
            visited.add((x, y)							)

            if (x, y) == destination:
                  SCREAMING_SNAKE_CASE__       :  Any						     =		[]
                  while (x, y) != source:
                        path.append((x, y)							)
                        SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Any						     =		predecessors[x, y]
                  path.append(A__							)  # add the source manually
                  path.reverse()
                  return matrix[destination], path

            for i in range(len(A__							)							):
                  SCREAMING_SNAKE_CASE__     ,       SCREAMING_SNAKE_CASE__       :  Any						     =		x + dx[i], y + dy[i]
                  if 0 <= nx < rows and 0 <= ny < cols:
                        SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		grid[nx][ny]
                        if next_node == 1 and matrix[nx, ny] > dist + 1:
                              heappush(A__      ,    (dist + 1, (nx, ny))							)
                              SCREAMING_SNAKE_CASE__       :  List[Any]						     =		dist + 1
                              SCREAMING_SNAKE_CASE__       :  List[Any]						     =		(x, y)

      return np.inf, []


if __name__ == "__main__":
    import doctest

    doctest.testmod()













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_												=	get_logger(__name__)






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							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(A				,							A     )
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""     )
            torch.save(A				,							A     )
            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(A				,							A     )
         logger.info(F"""Saving model to {output_model_file}"""     )
         torch.save(A				,							A     )
         logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving model to {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": state_dict}

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






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							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(A     ) != 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(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         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(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=(
             os.path.join(A				,							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=A				,							storage_reader=dist_cp.FileSystemReader(A     )				,							planner=DefaultLoadPlanner()				,							)
         UpperCAmelCase__						=state_dict["model"]
         logger.info(F"""Model loaded from {ckpt_dir}"""     )
      model.load_state_dict(A     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=FSDP.optim_state_dict(A				,							A     )
      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(A				,							A     )
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
         dist_cp.save_state_dict(
             state_dict={"optimizer": optim_state}				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							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(A				,							A     )
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=(
             os.path.join(A				,							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(A     )				,							)
         UpperCAmelCase__						=optim_state["optimizer"]
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
      UpperCAmelCase__						=FSDP.optim_state_dict_to_load(A				,							A				,							A     )
      optimizer.load_state_dict(A     )



