



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: Union[str, Any]      			=		get_logger(__name__)






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






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





from __future__ import annotations

_lowercase: Tuple      			=		list[list[int]]

# assigning initial values to the grid
_lowercase: Matrix      			=		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
_lowercase: Matrix      			=		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def     a(      A       :    Matrix ,     A       :    int ,     A       :    int ,     A       :    int       )		->       bool:
					"""simple docstring"""





					for i in range(9       ):
										if grid[row][i] == n or grid[i][column] == n:
															return False

					for i in range(3       ):
										for j in range(3       ):
															if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
																				return False

					return True






def     a(      A       :    Matrix       )		->       tuple[int, int] | None:
					"""simple docstring"""





					for i in range(9       ):
										for j in range(9       ):
															if grid[i][j] == 0:
																				return i, j
					return None






def     a(      A       :    Matrix       )		->       Matrix | None:
					"""simple docstring"""





					if location := find_empty_location(A       ):
										a     ,				a					     =   location
					else:
										# If the location is ``None``, then the grid is solved.
										return grid

					for digit in range(1 ,     10       ):
										if is_safe(A ,     A ,     A ,     A       ):
															a					     =   digit

															if sudoku(A       ) is not None:
																				return grid

															a					     =   0

					return None






def     a(      A       :    Matrix       )		->       None:
					"""simple docstring"""





					for row in grid:
										for cell in row:
															print(A ,     end=" "       )
										print()


if __name__ == "__main__":
				# make a copy of grid so that you can compare with the unmodified grid
				for example_grid in (initial_grid, no_solution):
								print("\nExample grid:\n" + "=" * 20)
								print_solution(example_grid)
								print("\nExample grid solution:")
								_lowercase: List[str]      			=		sudoku(example_grid)
								if solution is not None:
												print_solution(solution)
								else:
												print("Cannot find a solution.")
