






"""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_    =							get_logger(__name__)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[Any]=0      )		->str:
						os.makedirs(UpperCAmelCase__,						exist_ok=UpperCAmelCase__      )
						with FSDP.state_dict_type(
						    UpperCAmelCase__,						fsdp_plugin.state_dict_type,						fsdp_plugin.state_dict_config,						fsdp_plugin.optim_state_dict_config      ):
												A__						:  List[str]		  =  model.state_dict()
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		A__						:  Tuple		  =  f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																		A__						:  List[Any]		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )
																		if accelerator.process_index == 0:
																								logger.info(f'Saving model to {output_model_file}'      )
																								torch.save(UpperCAmelCase__,						UpperCAmelCase__      )
																								logger.info(f'Model saved to {output_model_file}'      )
												elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																		A__						:  Any		  =  (
																		    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																		    if model_index == 0
																		    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																		)
																		A__						:  Optional[Any]		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )
																		logger.info(f'Saving model to {output_model_file}'      )
																		torch.save(UpperCAmelCase__,						UpperCAmelCase__      )
																		logger.info(f'Model saved to {output_model_file}'      )
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		A__						:  Optional[Any]		  =  os.path.join(UpperCAmelCase__,						f'{MODEL_NAME}_{model_index}'      )
																		os.makedirs(UpperCAmelCase__,						exist_ok=UpperCAmelCase__      )
																		logger.info(f'Saving model to {ckpt_dir}'      )
																		A__						:  Tuple		  =  {"""model""": state_dict}

																		dist_cp.save_state_dict(
																		    state_dict=UpperCAmelCase__,						storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase__      ),						planner=DefaultSavePlanner(),						)
																		logger.info(f'Model saved to {ckpt_dir}'      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					List[Any]=0      )		->List[Any]:
						accelerator.wait_for_everyone()
						with FSDP.state_dict_type(
						    UpperCAmelCase__,						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(UpperCAmelCase__      ) != 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__						:  str		  =  f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
																		A__						:  List[str]		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )
																		logger.info(f'Loading model from {input_model_file}'      )
																		A__						:  Union[str, Any]		  =  torch.load(UpperCAmelCase__      )
																		logger.info(f'Model loaded from {input_model_file}'      )
												elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
																		A__						:  Optional[Any]		  =  (
																		    f'{MODEL_NAME}_rank{accelerator.process_index}.bin'
																		    if model_index == 0
																		    else f'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
																		)
																		A__						:  str		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )
																		logger.info(f'Loading model from {input_model_file}'      )
																		A__						:  Any		  =  torch.load(UpperCAmelCase__      )
																		logger.info(f'Model loaded from {input_model_file}'      )
												elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
																		A__						:  Any		  =  (
																		    os.path.join(UpperCAmelCase__,						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__						:  Tuple		  =  {"""model""": model.state_dict()}
																		dist_cp.load_state_dict(
																		    state_dict=UpperCAmelCase__,						storage_reader=dist_cp.FileSystemReader(UpperCAmelCase__      ),						planner=DefaultLoadPlanner(),						)
																		A__						:  Optional[Any]		  =  state_dict["""model"""]
																		logger.info(f'Model loaded from {ckpt_dir}'      )
												model.load_state_dict(UpperCAmelCase__      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					Optional[Any]=0      )		->Any:
						os.makedirs(UpperCAmelCase__,						exist_ok=UpperCAmelCase__      )
						with FSDP.state_dict_type(
						    UpperCAmelCase__,						fsdp_plugin.state_dict_type,						fsdp_plugin.state_dict_config,						fsdp_plugin.optim_state_dict_config      ):
												A__						:  str		  =  FSDP.optim_state_dict(UpperCAmelCase__,						UpperCAmelCase__      )
												if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
																		if accelerator.process_index == 0:
																								A__						:  str		  =  (
																								    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																								)
																								A__						:  List[Any]		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )
																								logger.info(f'Saving Optimizer state to {output_optimizer_file}'      )
																								torch.save(UpperCAmelCase__,						UpperCAmelCase__      )
																								logger.info(f'Optimizer state saved in {output_optimizer_file}'      )
												else:
																		A__						:  Optional[int]		  =  os.path.join(UpperCAmelCase__,						f'{OPTIMIZER_NAME}_{optimizer_index}'      )
																		os.makedirs(UpperCAmelCase__,						exist_ok=UpperCAmelCase__      )
																		logger.info(f'Saving Optimizer state to {ckpt_dir}'      )
																		dist_cp.save_state_dict(
																		    state_dict={"""optimizer""": optim_state},						storage_writer=dist_cp.FileSystemWriter(UpperCAmelCase__      ),						planner=DefaultSavePlanner(),						)
																		logger.info(f'Optimizer state saved in {ckpt_dir}'      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					int=0      )		->List[Any]:
						accelerator.wait_for_everyone()
						with FSDP.state_dict_type(
						    UpperCAmelCase__,						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__						:  List[Any]		  =  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__						:  List[str]		  =  (
																		    f'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else f'{OPTIMIZER_NAME}_{optimizer_index}.bin'
																		)
																		A__						:  Tuple		  =  os.path.join(UpperCAmelCase__,						UpperCAmelCase__      )
																		logger.info(f'Loading Optimizer state from {input_optimizer_file}'      )
																		A__						:  Any		  =  torch.load(UpperCAmelCase__      )
																		logger.info(f'Optimizer state loaded from {input_optimizer_file}'      )
												else:
																		A__						:  Any		  =  (
																		    os.path.join(UpperCAmelCase__,						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__						:  Any		  =  load_sharded_optimizer_state_dict(
																		    model_state_dict=model.state_dict(),						optimizer_key="""optimizer""",						storage_reader=dist_cp.FileSystemReader(UpperCAmelCase__      ),						)
																		A__						:  List[str]		  =  optim_state["""optimizer"""]
																		logger.info(f'Optimizer loaded from {ckpt_dir}'      )
												A__						:  Optional[Any]		  =  FSDP.optim_state_dict_to_load(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )
												optimizer.load_state_dict(UpperCAmelCase__      )














"""simple docstring"""



from __future__ import annotations






class 							__SCREAMING_SNAKE_CASE						:


							def __init__(			self      :						Any			,    snake_case      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													A__  ,  A__						:  List[str]		  =  text, pattern
													A__  ,  A__						:  List[str]		  =  len(snake_case							), len(snake_case							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						str							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if char == self.pattern[i]:
																									return i
													return -1


							def 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						int							):
													'''simple docstring'''





													for i in range(self.patLen - 1			,    -1			,    -1							):
																			if self.pattern[i] != self.text[current_pos + i]:
																									return current_pos + i
													return -1


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  str		  =  []
													for i in range(self.textLen - self.patLen + 1							):
																			A__						:  List[Any]		  =  self.mismatch_in_text(snake_case							)
																			if mismatch_index == -1:
																									positions.append(snake_case							)
																			else:
																									A__						:  Dict		  =  self.match_in_pattern(self.text[mismatch_index]							)
																									A__						:  Union[str, Any]		  =  (
																									    mismatch_index - match_index
																									)  # shifting index lgtm [py/multiple-definition]
													return positions




A_    =							'''ABAABA'''
A_    =							'''AB'''
A_    =							BoyerMooreSearch(text, pattern)
A_    =							bms.bad_character_heuristic()

if len(positions) == 0:
						print('''No match found''')
else:
						print('''Pattern found in following positions: ''')
						print(positions)






