






from .constants import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SAFE_WEIGHTS_INDEX_NAME,
    SAFE_WEIGHTS_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    TORCH_LAUNCH_PARAMS,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
)
from .dataclasses import (
    BnbQuantizationConfig,
    ComputeEnvironment,
    CustomDtype,
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    DynamoBackend,
    FPaRecipeKwargs,
    FullyShardedDataParallelPlugin,
    GradientAccumulationPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    KwargsHandler,
    LoggerType,
    MegatronLMPlugin,
    PrecisionType,
    ProjectConfiguration,
    RNGType,
    SageMakerDistributedType,
    TensorInformation,
    TorchDynamoPlugin,
)
from .environment import get_int_from_env, parse_choice_from_env, parse_flag_from_env
from .imports import (
    get_ccl_version,
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_aim_available,
    is_bfaa_available,
    is_bnb_available,
    is_botoa_available,
    is_ccl_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_fpa_available,
    is_ipex_available,
    is_megatron_lm_available,
    is_mlflow_available,
    is_mps_available,
    is_npu_available,
    is_rich_available,
    is_safetensors_available,
    is_sagemaker_available,
    is_tensorboard_available,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)
from .modeling import (
    check_device_map,
    check_tied_parameters_in_config,
    check_tied_parameters_on_same_device,
    compute_module_sizes,
    convert_file_size_to_int,
    dtype_byte_size,
    find_tied_parameters,
    get_balanced_memory,
    get_max_layer_size,
    get_max_memory,
    get_mixed_precision_context_manager,
    id_tensor_storage,
    infer_auto_device_map,
    load_checkpoint_in_model,
    load_offloaded_weights,
    load_state_dict,
    named_module_tensors,
    retie_parameters,
    set_module_tensor_to_device,
    shard_checkpoint,
)
from .offload import (
    OffloadedWeightsLoader,
    PrefixedDataset,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
    save_offload_index,
)
from .operations import (
    broadcast,
    broadcast_object_list,
    concatenate,
    convert_outputs_to_fpaa,
    convert_to_fpaa,
    find_batch_size,
    find_device,
    gather,
    gather_object,
    get_data_structure,
    honor_type,
    initialize_tensors,
    is_namedtuple,
    is_tensor_information,
    is_torch_tensor,
    listify,
    pad_across_processes,
    recursively_apply,
    reduce,
    send_to_device,
    slice_tensors,
)
from .versions import compare_versions, is_torch_version


if is_deepspeed_available():
				from .deepspeed import (
				    DeepSpeedEngineWrapper,
				    DeepSpeedOptimizerWrapper,
				    DeepSpeedSchedulerWrapper,
				    DummyOptim,
				    DummyScheduler,
				    HfDeepSpeedConfig,
				)

from .bnb import has_abit_bnb_layers, load_and_quantize_model
from .fsdp_utils import load_fsdp_model, load_fsdp_optimizer, save_fsdp_model, save_fsdp_optimizer
from .launch import (
    PrepareForLaunch,
    _filter_args,
    prepare_deepspeed_cmd_env,
    prepare_multi_gpu_env,
    prepare_sagemager_args_inputs,
    prepare_simple_launcher_cmd_env,
    prepare_tpu,
)
from .megatron_lm import (
    AbstractTrainStep,
    BertTrainStep,
    GPTTrainStep,
    MegatronEngine,
    MegatronLMDummyDataLoader,
    MegatronLMDummyScheduler,
    MegatronLMOptimizerWrapper,
    MegatronLMSchedulerWrapper,
    TaTrainStep,
    avg_losses_across_data_parallel_group,
    gather_across_data_parallel_groups,
)
from .megatron_lm import initialize as megatron_lm_initialize
from .megatron_lm import prepare_data_loader as megatron_lm_prepare_data_loader
from .megatron_lm import prepare_model as megatron_lm_prepare_model
from .megatron_lm import prepare_optimizer as megatron_lm_prepare_optimizer
from .megatron_lm import prepare_scheduler as megatron_lm_prepare_scheduler
from .memory import find_executable_batch_size, release_memory
from .other import (
    extract_model_from_parallel,
    get_pretty_name,
    is_port_in_use,
    merge_dicts,
    patch_environment,
    save,
    wait_for_everyone,
    write_basic_config,
)
from .random import set_seed, synchronize_rng_state, synchronize_rng_states
from .torch_xla import install_xla
from .tqdm import tqdm
from .transformer_engine import convert_model, has_transformer_engine_layers












from __future__ import annotations

import time

a_		:						Tuple             =    list[tuple[int, int]]

a_		:						Optional[int]             =    [
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

a_		:						Dict             =    [[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 		_snake_case  :






			def __init__(       self		,					a		,					a		,					a		,					a		,					a)      ->       int:
						SCREAMING_SNAKE_CASE						= pos_x
						SCREAMING_SNAKE_CASE						= pos_y
						SCREAMING_SNAKE_CASE						= (pos_y, pos_x)
						SCREAMING_SNAKE_CASE						= goal_x
						SCREAMING_SNAKE_CASE						= goal_y
						SCREAMING_SNAKE_CASE						= parent



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= Node(start[1]		,					start[0]		,					goal[1]		,					goal[0]		,					a)
						SCREAMING_SNAKE_CASE						= Node(goal[1]		,					goal[0]		,					goal[1]		,					goal[0]		,					a)

						SCREAMING_SNAKE_CASE						= [self.start]
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.node_queue:
									SCREAMING_SNAKE_CASE						= self.node_queue.pop(0)

									if current_node.pos == self.target.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_path(a)

									SCREAMING_SNAKE_CASE						= self.get_successors(a)

									for node in successors:
												self.node_queue.append(a)

						if not self.reached:
									return [self.start.pos]
						return None






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       list[Node]:
						SCREAMING_SNAKE_CASE						= []
						for action in delta:
									SCREAMING_SNAKE_CASE						= parent.pos_x + action[1]
									SCREAMING_SNAKE_CASE						= parent.pos_y + action[0]
									if not (0 <= pos_x <= len(grid[0]) - 1 and 0 <= pos_y <= len(a) - 1):
												continue

									if grid[pos_y][pos_x] != 0:
												continue

									successors.append(
									    Node(a		,					a		,					self.target.pos_y		,					self.target.pos_x		,					a))
						return successors







			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= node
						SCREAMING_SNAKE_CASE						= []
						while current_node is not None:
									path.append((current_node.pos_y, current_node.pos_x))
									SCREAMING_SNAKE_CASE						= current_node.parent
						path.reverse()
						return path



class 		_snake_case  :






			def __init__(       self		,					a		,					a)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= BreadthFirstSearch(a		,					a)
						SCREAMING_SNAKE_CASE						= False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Path | None:
						while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
									SCREAMING_SNAKE_CASE						= self.fwd_bfs.node_queue.pop(0)
									SCREAMING_SNAKE_CASE						= self.bwd_bfs.node_queue.pop(0)

									if current_bwd_node.pos == current_fwd_node.pos:
												SCREAMING_SNAKE_CASE						= True
												return self.retrace_bidirectional_path(
												    a		,					a)

									SCREAMING_SNAKE_CASE						= current_bwd_node
									SCREAMING_SNAKE_CASE						= current_fwd_node

									SCREAMING_SNAKE_CASE						= {
									    self.fwd_bfs: self.fwd_bfs.get_successors(a),
									    self.bwd_bfs: self.bwd_bfs.get_successors(a),
									}

									for bfs in [self.fwd_bfs, self.bwd_bfs]:
												for node in successors[bfs]:
															bfs.node_queue.append(a)

						if not self.reached:
									return [self.fwd_bfs.start.pos]
						return None







			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       Path:
						SCREAMING_SNAKE_CASE						= self.fwd_bfs.retrace_path(a)
						SCREAMING_SNAKE_CASE						= self.bwd_bfs.retrace_path(a)
						bwd_path.pop()
						bwd_path.reverse()
						SCREAMING_SNAKE_CASE						= fwd_path + bwd_path
						return path


if __name__ == "__main__":
				# all coordinates are given in format [y,x]
				import doctest

				doctest.testmod()
				a_		:						Union[str, Any]             =    (0, 0)
				a_		:						Any             =    (len(grid) - 1, len(grid[0]) - 1)
				for elem in grid:
								print(elem)

				a_		:						Optional[Any]             =    time.time()
				a_		:						Union[str, Any]             =    BreadthFirstSearch(init, goal)
				a_		:						Optional[Any]             =    bfs.search()
				a_		:						List[str]             =    time.time() - start_bfs_time

				print('Unidirectional BFS computation time : ', bfs_time)

				a_		:						Optional[int]             =    time.time()
				a_		:						Union[str, Any]             =    BidirectionalBreadthFirstSearch(init, goal)
				a_		:						List[Any]             =    bd_bfs.search()
				a_		:						List[str]             =    time.time() - start_bd_bfs_time

				print('Bidirectional BFS computation time : ', bd_bfs_time)




