"""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  :  List[Any]=				get_logger(__name__)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       Optional[int]    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       str=0						)							->    Dict:

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

	'''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
			__snake_case    :							Optional[Any]   	=				F"{MODEL_NAME}.bin" if model_index == 0 else F"{MODEL_NAME}_{model_index}.bin"
			__snake_case    :							Dict   	=				os.path.join(_A    ,							_A						)
			logger.info(F"Loading model from {input_model_file}"						)
			__snake_case    :							Union[str, Any]   	=				torch.load(_A						)
			logger.info(F"Model loaded from {input_model_file}"						)
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			__snake_case    :							int   	=				(
			    F"{MODEL_NAME}_rank{accelerator.process_index}.bin"
			    if model_index == 0
			    else F"{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"
			)
			__snake_case    :							Optional[Any]   	=				os.path.join(_A    ,							_A						)
			logger.info(F"Loading model from {input_model_file}"						)
			__snake_case    :							List[Any]   	=				torch.load(_A						)
			logger.info(F"Model loaded from {input_model_file}"						)
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			__snake_case    :							str   	=				(
			    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}"						)
			__snake_case    :							Tuple   	=				{"model": model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=_A    ,							storage_reader=dist_cp.FileSystemReader(_A						)    ,							planner=DefaultLoadPlanner()    ,							)
			__snake_case    :							str   	=				state_dict["model"]
			logger.info(F"Model loaded from {ckpt_dir}"						)
		model.load_state_dict(_A						)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       Dict    ,							UpperCAmelCase_					:       str    ,							UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       List[str]    ,							UpperCAmelCase_					:       List[Any]    ,							UpperCAmelCase_					:       Tuple=0						)							->    List[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						):
		__snake_case    :							Optional[int]   	=				FSDP.optim_state_dict(_A    ,							_A						)
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				__snake_case    :							Union[str, Any]   	=				(
				    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
				)
				__snake_case    :							str   	=				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:
			__snake_case    :							Tuple   	=				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       (					UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Tuple    ,							UpperCAmelCase_					:       Optional[Any]    ,							UpperCAmelCase_					:       int    ,							UpperCAmelCase_					:       Any    ,							UpperCAmelCase_					:       Dict=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:
			__snake_case    :							str   	=				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:
			__snake_case    :							List[Any]   	=				(
			    F"{OPTIMIZER_NAME}.bin" if optimizer_index == 0 else F"{OPTIMIZER_NAME}_{optimizer_index}.bin"
			)
			__snake_case    :							Optional[int]   	=				os.path.join(_A    ,							_A						)
			logger.info(F"Loading Optimizer state from {input_optimizer_file}"						)
			__snake_case    :							Union[str, Any]   	=				torch.load(_A						)
			logger.info(F"Optimizer state loaded from {input_optimizer_file}"						)
		else:
			__snake_case    :							List[Any]   	=				(
			    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}"						)
			__snake_case    :							Dict   	=				load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()    ,							optimizer_key='optimizer'    ,							storage_reader=dist_cp.FileSystemReader(_A						)    ,							)
			__snake_case    :							Any   	=				optim_state["optimizer"]
			logger.info(F"Optimizer loaded from {ckpt_dir}"						)
		__snake_case    :							Any   	=				FSDP.optim_state_dict_to_load(_A    ,							_A    ,							_A						)
		optimizer.load_state_dict(_A						)






"""simple docstring"""







from __future__ import annotations

_a  :  str=				"#"

class 	UpperCamelCase    :



				def __init__(self       :   str)					->			None:
					__snake_case    :							dict   	=				{}



				def       _lowercase  (self       :   Union[str, Any]			,						_A       :   str)					->			None:
					__snake_case    :							Optional[Any]   	=				self._trie
					for char in text:
						if char not in trie:
							__snake_case    :							Optional[int]   	=				{}
						__snake_case    :							Optional[int]   	=				trie[char]
					__snake_case    :							Optional[int]   	=				True



				def       _lowercase  (self       :   List[str]			,						_A       :   str)					->			tuple | list:
					__snake_case    :							str   	=				self._trie
					for char in prefix:
						if char in trie:
							__snake_case    :							Optional[int]   	=				trie[char]
						else:
							return []
					return self._elements(_A)






				def       _lowercase  (self       :   List[Any]			,						_A       :   dict)					->			tuple:
					__snake_case    :							str   	=				[]
					for c, v in d.items():
						__snake_case    :							Optional[Any]   	=				[' '] if c == END else [(c + s) for s in self._elements(_A)]
						result.extend(_A)
					return tuple(_A)







_a  :  str=				Trie()
_a  :  Any=				("depart", "detergent", "daring", "dog", "deer", "deal")
for word in words:
	trie.insert_word(word)
def 						__UpperCAmelCase       (					UpperCAmelCase_					:       str						)							->    tuple:

	'''simple docstring'''





	__snake_case    :							Optional[int]   	=				trie.find_word(UpperCAmelCase_						)
	return tuple(string + word for word in suffixes						)
def 						__UpperCAmelCase       (					)							->    None:

	'''simple docstring'''





	print(autocomplete_using_trie('de'						)						)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()





