"""simple docstring"""






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :  List[str]						     =	logging.get_logger(__name__)



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							DPTConfig(embedding_type='hybrid'       )

				if "large" in checkpoint_url:
								UpperCAmelCase__             =							1_0_2_4
								UpperCAmelCase__             =							4_0_9_6
								UpperCAmelCase__             =							2_4
								UpperCAmelCase__             =							1_6
								UpperCAmelCase__             =							[5, 1_1, 1_7, 2_3]
								UpperCAmelCase__             =							[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
								UpperCAmelCase__             =							(1, 3_8_4, 3_8_4)

				if "nyu" or "midas" in checkpoint_url:
								UpperCAmelCase__             =							7_6_8
								UpperCAmelCase__             =							[1, 1, 1, 0.5]
								UpperCAmelCase__             =							[2_5_6, 5_1_2, 7_6_8, 7_6_8]
								UpperCAmelCase__             =							1_5_0
								UpperCAmelCase__             =							1_6
								UpperCAmelCase__             =							(1, 3_8_4, 3_8_4)
								UpperCAmelCase__             =							False
								UpperCAmelCase__             =							'project'

				if "ade" in checkpoint_url:
								UpperCAmelCase__             =							True
								UpperCAmelCase__             =							7_6_8
								UpperCAmelCase__             =							[1, 1, 1, 0.5]
								UpperCAmelCase__             =							1_5_0
								UpperCAmelCase__             =							1_6
								UpperCAmelCase__             =							'huggingface/label-files'
								UpperCAmelCase__             =							'ade20k-id2label.json'
								UpperCAmelCase__             =							json.load(open(cached_download(hf_hub_url(lowerCamelCase					,			lowerCamelCase					,			repo_type='dataset'       )       )					,			'r'       )       )
								UpperCAmelCase__             =							{int(lowerCamelCase       ): v for k, v in idalabel.items()}
								UpperCAmelCase__             =							idalabel
								UpperCAmelCase__             =							{v: k for k, v in idalabel.items()}
								UpperCAmelCase__             =							[1, 1_5_0, 4_8_0, 4_8_0]

				return config, expected_shape



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							['pretrained.model.head.weight', 'pretrained.model.head.bias']
				for k in ignore_keys:
								state_dict.pop(lowerCamelCase					,			lowerCamelCase       )



def   a_						(					lowerCamelCase       ):
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								UpperCAmelCase__             =							name.replace('pretrained.model'					,			'dpt.encoder'       )
				if "pretrained.model" in name:
								UpperCAmelCase__             =							name.replace('pretrained.model'					,			'dpt.embeddings'       )
				if "patch_embed" in name:
								UpperCAmelCase__             =							name.replace('patch_embed'					,			''       )
				if "pos_embed" in name:
								UpperCAmelCase__             =							name.replace('pos_embed'					,			'position_embeddings'       )
				if "attn.proj" in name:
								UpperCAmelCase__             =							name.replace('attn.proj'					,			'attention.output.dense'       )
				if "proj" in name and "project" not in name:
								UpperCAmelCase__             =							name.replace('proj'					,			'projection'       )
				if "blocks" in name:
								UpperCAmelCase__             =							name.replace('blocks'					,			'layer'       )
				if "mlp.fc1" in name:
								UpperCAmelCase__             =							name.replace('mlp.fc1'					,			'intermediate.dense'       )
				if "mlp.fc2" in name:
								UpperCAmelCase__             =							name.replace('mlp.fc2'					,			'output.dense'       )
				if "norm1" in name and "backbone" not in name:
								UpperCAmelCase__             =							name.replace('norm1'					,			'layernorm_before'       )
				if "norm2" in name and "backbone" not in name:
								UpperCAmelCase__             =							name.replace('norm2'					,			'layernorm_after'       )
				if "scratch.output_conv" in name:
								UpperCAmelCase__             =							name.replace('scratch.output_conv'					,			'head'       )
				if "scratch" in name:
								UpperCAmelCase__             =							name.replace('scratch'					,			'neck'       )
				if "layer1_rn" in name:
								UpperCAmelCase__             =							name.replace('layer1_rn'					,			'convs.0'       )
				if "layer2_rn" in name:
								UpperCAmelCase__             =							name.replace('layer2_rn'					,			'convs.1'       )
				if "layer3_rn" in name:
								UpperCAmelCase__             =							name.replace('layer3_rn'					,			'convs.2'       )
				if "layer4_rn" in name:
								UpperCAmelCase__             =							name.replace('layer4_rn'					,			'convs.3'       )
				if "refinenet" in name:
								UpperCAmelCase__             =							int(name[len('neck.refinenet'       ) : len('neck.refinenet'       ) + 1]       )
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								UpperCAmelCase__             =							name.replace(f'''refinenet{layer_idx}'''					,			f'''fusion_stage.layers.{abs(layer_idx-4       )}'''       )
				if "out_conv" in name:
								UpperCAmelCase__             =							name.replace('out_conv'					,			'projection'       )
				if "resConfUnit1" in name:
								UpperCAmelCase__             =							name.replace('resConfUnit1'					,			'residual_layer1'       )
				if "resConfUnit2" in name:
								UpperCAmelCase__             =							name.replace('resConfUnit2'					,			'residual_layer2'       )
				if "conv1" in name:
								UpperCAmelCase__             =							name.replace('conv1'					,			'convolution1'       )
				if "conv2" in name:
								UpperCAmelCase__             =							name.replace('conv2'					,			'convolution2'       )
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess1.0.project.0'					,			'neck.reassemble_stage.readout_projects.0.0'       )
				if "pretrained.act_postprocess2.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess2.0.project.0'					,			'neck.reassemble_stage.readout_projects.1.0'       )
				if "pretrained.act_postprocess3.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess3.0.project.0'					,			'neck.reassemble_stage.readout_projects.2.0'       )
				if "pretrained.act_postprocess4.0.project.0" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess4.0.project.0'					,			'neck.reassemble_stage.readout_projects.3.0'       )

				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess1.3'					,			'neck.reassemble_stage.layers.0.projection'       )
				if "pretrained.act_postprocess1.4" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess1.4'					,			'neck.reassemble_stage.layers.0.resize'       )
				if "pretrained.act_postprocess2.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess2.3'					,			'neck.reassemble_stage.layers.1.projection'       )
				if "pretrained.act_postprocess2.4" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess2.4'					,			'neck.reassemble_stage.layers.1.resize'       )
				if "pretrained.act_postprocess3.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess3.3'					,			'neck.reassemble_stage.layers.2.projection'       )
				if "pretrained.act_postprocess4.3" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess4.3'					,			'neck.reassemble_stage.layers.3.projection'       )
				if "pretrained.act_postprocess4.4" in name:
								UpperCAmelCase__             =							name.replace('pretrained.act_postprocess4.4'					,			'neck.reassemble_stage.layers.3.resize'       )
				if "pretrained" in name:
								UpperCAmelCase__             =							name.replace('pretrained'					,			'dpt'       )
				if "bn" in name:
								UpperCAmelCase__             =							name.replace('bn'					,			'batch_norm'       )
				if "head" in name:
								UpperCAmelCase__             =							name.replace('head'					,			'head.head'       )
				if "encoder.norm" in name:
								UpperCAmelCase__             =							name.replace('encoder.norm'					,			'layernorm'       )
				if "auxlayer" in name:
								UpperCAmelCase__             =							name.replace('auxlayer'					,			'auxiliary_head.head'       )
				if "backbone" in name:
								UpperCAmelCase__             =							name.replace('backbone'					,			'backbone.bit.encoder'       )

				if ".." in name:
								UpperCAmelCase__             =							name.replace('..'					,			'.'       )

				if "stem.conv" in name:
								UpperCAmelCase__             =							name.replace('stem.conv'					,			'bit.embedder.convolution'       )
				if "blocks" in name:
								UpperCAmelCase__             =							name.replace('blocks'					,			'layers'       )
				if "convolution" in name and "backbone" in name:
								UpperCAmelCase__             =							name.replace('convolution'					,			'conv'       )
				if "layer" in name and "backbone" in name:
								UpperCAmelCase__             =							name.replace('layer'					,			'layers'       )
				if "backbone.bit.encoder.bit" in name:
								UpperCAmelCase__             =							name.replace('backbone.bit.encoder.bit'					,			'backbone.bit'       )
				if "embedder.conv" in name:
								UpperCAmelCase__             =							name.replace('embedder.conv'					,			'embedder.convolution'       )
				if "backbone.bit.encoder.stem.norm" in name:
								UpperCAmelCase__             =							name.replace('backbone.bit.encoder.stem.norm'					,			'backbone.bit.embedder.norm'       )
				return name



def   a_						(					lowerCamelCase					,			lowerCamelCase       ):
				for i in range(config.num_hidden_layers       ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								UpperCAmelCase__             =							state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.weight'''       )
								UpperCAmelCase__             =							state_dict.pop(f'''dpt.encoder.layer.{i}.attn.qkv.bias'''       )
								# next, add query, keys and values (in that order) to the state dict
								UpperCAmelCase__             =							in_proj_weight[: config.hidden_size, :]
								UpperCAmelCase__             =							in_proj_bias[: config.hidden_size]
								UpperCAmelCase__             =							in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								UpperCAmelCase__             =							in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								UpperCAmelCase__             =							in_proj_weight[
								    -config.hidden_size :, :
								]
								UpperCAmelCase__             =							in_proj_bias[-config.hidden_size :]



def   a_						(					):
				UpperCAmelCase__             =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__             =							Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase       ).raw       )
				return im



@torch.no_grad()
def   a_						(					lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase					,			lowerCamelCase       ):
				UpperCAmelCase__      ,	UpperCAmelCase__             =							get_dpt_config(lowerCamelCase       )
				# load original state_dict from URL
				# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
				UpperCAmelCase__             =							torch.load(lowerCamelCase					,			map_location='cpu'       )
				# remove certain keys
				remove_ignore_keys_(lowerCamelCase       )
				# rename keys
				for key in state_dict.copy().keys():
								UpperCAmelCase__             =							state_dict.pop(lowerCamelCase       )
								UpperCAmelCase__             =							val
				# read in qkv matrices
				read_in_q_k_v(lowerCamelCase					,			lowerCamelCase       )

				# load HuggingFace model
				UpperCAmelCase__             =							DPTForSemanticSegmentation(lowerCamelCase       ) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCamelCase       )
				model.load_state_dict(lowerCamelCase       )
				model.eval()

				# Check outputs on an image
				UpperCAmelCase__             =							4_8_0 if 'ade' in checkpoint_url else 3_8_4
				UpperCAmelCase__             =							DPTImageProcessor(size=lowerCamelCase       )

				UpperCAmelCase__             =							prepare_img()
				UpperCAmelCase__             =							image_processor(lowerCamelCase					,			return_tensors='pt'       )

				# forward pass
				UpperCAmelCase__             =							model(**lowerCamelCase       ).logits if 'ade' in checkpoint_url else model(**lowerCamelCase       ).predicted_depth

				if show_prediction:
								UpperCAmelCase__             =							(
								    torch.nn.functional.interpolate(
								        outputs.unsqueeze(1       )					,			size=(image.size[1], image.size[0])					,			mode='bicubic'					,			align_corners=lowerCamelCase					,			)
								    .squeeze()
								    .cpu()
								    .numpy()
								)

								Image.fromarray((prediction / prediction.max()) * 2_5_5       ).show()

				if pytorch_dump_folder_path is not None:
								Path(lowerCamelCase       ).mkdir(exist_ok=lowerCamelCase       )
								print(f'''Saving model to {pytorch_dump_folder_path}'''       )
								model.save_pretrained(lowerCamelCase       )
								print(f'''Saving image processor to {pytorch_dump_folder_path}'''       )
								image_processor.save_pretrained(lowerCamelCase       )

				if push_to_hub:
								model.push_to_hub('ybelkada/dpt-hybrid-midas'       )
								image_processor.push_to_hub('ybelkada/dpt-hybrid-midas'       )


if __name__ == "__main__":
						lowerCAmelCase__  :  Optional[Any]						     =	argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
						    type=str,
						    help='URL of the original DPT checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default=None,
						    type=str,
						    required=False,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						)
						parser.add_argument(
						    '--model_name',
						    default='dpt-large',
						    type=str,
						    help='Name of the model, in case you\'re pushing to the hub.',
						)
						parser.add_argument(
						    '--show_prediction',
						    action='store_true',
						)

						lowerCAmelCase__  :  List[str]						     =	parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)









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


UpperCamelCase__    			=			get_logger(__name__)







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

															dist_cp.save_state_dict(
															    state_dict=SCREAMING_SNAKE_CASE_      ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultSavePlanner()      ,      )
															logger.info(F"""Model saved to {ckpt_dir}"""     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Any=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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(SCREAMING_SNAKE_CASE_     ) != 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
															__lowerCAmelCase				   =       F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
															__lowerCAmelCase				   =       (
															    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
															    if model_index == 0
															    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{MODEL_NAME}_{model_index}"""     )
															    if F"""{MODEL_NAME}""" not in input_dir
															    else input_dir
															)
															logger.info(F"""Loading model from {ckpt_dir}"""     )
															__lowerCAmelCase				   =       {"model": model.state_dict()}
															dist_cp.load_state_dict(
															    state_dict=SCREAMING_SNAKE_CASE_      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultLoadPlanner()      ,      )
															__lowerCAmelCase				   =       state_dict["model"]
															logger.info(F"""Model loaded from {ckpt_dir}"""     )
										model.load_state_dict(SCREAMING_SNAKE_CASE_     )







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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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:
															__lowerCAmelCase				   =       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:
															__lowerCAmelCase				   =       (
															    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
															    if F"""{OPTIMIZER_NAME}""" not in input_dir
															    else input_dir
															)
															logger.info(F"""Loading Optimizer from {ckpt_dir}"""     )
															__lowerCAmelCase				   =       load_sharded_optimizer_state_dict(
															    model_state_dict=model.state_dict()      ,      optimizer_key="optimizer"      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      )
															__lowerCAmelCase				   =       optim_state["optimizer"]
															logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
										__lowerCAmelCase				   =       FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )




