
"""simple docstring"""




import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowerCAmelCase__				=					''
lowerCAmelCase__				=					''
lowerCAmelCase__				=					''
lowerCAmelCase__				=					1  # (0 is vertical, 1 is horizontal)



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			get_dataset(__a,				__a     )
		print('''Processing...'''     )
		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			update_image_and_anno(__a,				__a,				__a     )

		for index, image in enumerate(__a     ):
				# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
				SCREAMING_SNAKE_CASE_    			=			random_chars(32     )
				SCREAMING_SNAKE_CASE_    			=			paths[index].split(os.sep     )[-1].rsplit('''.''',				1     )[0]
				SCREAMING_SNAKE_CASE_    			=			F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
				cva.imwrite(F'/{file_root}.jpg',				__a,				[cva.IMWRITE_JPEG_QUALITY, 85]     )
				print(F'Success {index+1}/{len(__a     )} with {file_name}'     )
				SCREAMING_SNAKE_CASE_    			=			[]
				for anno in new_annos[index]:
						SCREAMING_SNAKE_CASE_    			=			F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
						annos_list.append(__a     )
				with open(F'/{file_root}.txt',				'''w'''     ) as outfile:
						outfile.write('''\n'''.join(line for line in annos_list     )     )



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			[]
		for label_file in glob.glob(os.path.join(__a,				'''*.txt'''     )     ):
				SCREAMING_SNAKE_CASE_    			=			label_file.split(os.sep     )[-1].rsplit('''.''',				1     )[0]
				with open(__a     ) as in_file:
						SCREAMING_SNAKE_CASE_    			=			in_file.readlines()
				SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				F'{label_name}.jpg'     )

				SCREAMING_SNAKE_CASE_    			=			[]
				for obj_list in obj_lists:
						SCREAMING_SNAKE_CASE_    			=			obj_list.rstrip('''\n'''     ).split(''' '''     )
						boxes.append(
						    [
						        int(obj[0]     ),
						        float(obj[1]     ),
						        float(obj[2]     ),
						        float(obj[3]     ),
						        float(obj[4]     ),
						    ]     )
				if not boxes:
						continue
				img_paths.append(__a     )
				labels.append(__a     )
		return img_paths, labels



def 							_lowerCamelCase			(			__a,				__a,				__a = 1     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			[]
		for idx in range(len(__a     )     ):
				SCREAMING_SNAKE_CASE_    			=			[]
				SCREAMING_SNAKE_CASE_    			=			img_list[idx]
				path_list.append(__a     )
				SCREAMING_SNAKE_CASE_    			=			anno_list[idx]
				SCREAMING_SNAKE_CASE_    			=			cva.imread(__a     )
				if flip_type == 1:
						SCREAMING_SNAKE_CASE_    			=			cva.flip(__a,				__a     )
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_    			=			1 - bbox[1]
								new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]     )
				elif flip_type == 0:
						SCREAMING_SNAKE_CASE_    			=			cva.flip(__a,				__a     )
						for bbox in img_annos:
								SCREAMING_SNAKE_CASE_    			=			1 - bbox[2]
								new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]     )
				new_annos_lists.append(__a     )
				new_imgs_list.append(__a     )
		return new_imgs_list, new_annos_lists, path_list



def 							_lowerCamelCase			(			__a = 32     ):
		assert number_char > 1, "The number of character should greater than 1"
		SCREAMING_SNAKE_CASE_    			=			ascii_lowercase + digits
		return "".join(random.choice(__a     ) for _ in range(__a     )     )


if __name__ == "__main__":
						main()
						print('DONE ✅')

"""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


lowerCAmelCase__				=					get_logger(__name__)



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a=0     ):
		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     ):
				SCREAMING_SNAKE_CASE_    			=			model.state_dict()
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				F'{MODEL_NAME}_{model_index}'     )
						os.makedirs(__a,				exist_ok=__a     )
						logger.info(F'Saving model to {ckpt_dir}'     )
						SCREAMING_SNAKE_CASE_    			=			{'''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 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a=0     ):
		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
						SCREAMING_SNAKE_CASE_    			=			F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading model from {input_model_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Model loaded from {input_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
						    if model_index == 0
						    else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading model from {input_model_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Model loaded from {input_model_file}'     )
				elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
						SCREAMING_SNAKE_CASE_    			=			(
						    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}'     )
						SCREAMING_SNAKE_CASE_    			=			{'''model''': model.state_dict()}
						dist_cp.load_state_dict(
						    state_dict=__a,				storage_reader=dist_cp.FileSystemReader(__a     ),				planner=DefaultLoadPlanner(),				)
						SCREAMING_SNAKE_CASE_    			=			state_dict['''model''']
						logger.info(F'Model loaded from {ckpt_dir}'     )
				model.load_state_dict(__a     )



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a,				__a=0     ):
		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     ):
				SCREAMING_SNAKE_CASE_    			=			FSDP.optim_state_dict(__a,				__a     )
				if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
						if accelerator.process_index == 0:
								SCREAMING_SNAKE_CASE_    			=			(
								    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
								)
								SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			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 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a,				__a=0     ):
		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:
						SCREAMING_SNAKE_CASE_    			=			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:
						SCREAMING_SNAKE_CASE_    			=			(
						    F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
						)
						SCREAMING_SNAKE_CASE_    			=			os.path.join(__a,				__a     )
						logger.info(F'Loading Optimizer state from {input_optimizer_file}'     )
						SCREAMING_SNAKE_CASE_    			=			torch.load(__a     )
						logger.info(F'Optimizer state loaded from {input_optimizer_file}'     )
				else:
						SCREAMING_SNAKE_CASE_    			=			(
						    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}'     )
						SCREAMING_SNAKE_CASE_    			=			load_sharded_optimizer_state_dict(
						    model_state_dict=model.state_dict(),				optimizer_key='''optimizer''',				storage_reader=dist_cp.FileSystemReader(__a     ),				)
						SCREAMING_SNAKE_CASE_    			=			optim_state['''optimizer''']
						logger.info(F'Optimizer loaded from {ckpt_dir}'     )
				SCREAMING_SNAKE_CASE_    			=			FSDP.optim_state_dict_to_load(__a,				__a,				__a     )
				optimizer.load_state_dict(__a     )