




'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a  :				Optional[int]									=  logging.get_logger(__name__)

a  :				Any									=  torch.device('cpu')


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(SCREAMING_SNAKE_CASE_,      stream=SCREAMING_SNAKE_CASE_	).raw	)
					return im


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[Any]:

					'''simple docstring'''

					if swiftformer_name == "swiftformer_xs":
										return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]	)

					elif swiftformer_name == "swiftformer_s":
										return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]	)

					elif swiftformer_name == "swiftformer_l1":
										return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]	)

					elif swiftformer_name == "swiftformer_l3":
										return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Any:

					'''simple docstring'''

					snake_case_									=				dct.pop(SCREAMING_SNAKE_CASE_	)
					snake_case_									=				val


def 		__magic_name__    (	__UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				[]
					for k in state_dict.keys():
										snake_case_									=				k
										if ".pwconv" in k:
															snake_case_									=				k_new.replace('''.pwconv''',      '''.point_wise_conv'''	)
										if ".dwconv" in k:
															snake_case_									=				k_new.replace('''.dwconv''',      '''.depth_wise_conv'''	)
										if ".Proj." in k:
															snake_case_									=				k_new.replace('''.Proj.''',      '''.proj.'''	)
										if "patch_embed" in k_new:
															snake_case_									=				k_new.replace('''patch_embed''',      '''swiftformer.patch_embed.patch_embedding'''	)
										if "network" in k_new:
															snake_case_									=				k_new.split('''.'''	)
															if ls[2].isdigit():
																				snake_case_									=				'''swiftformer.encoder.network.''' + ls[1] + '''.blocks.''' + ls[2] + '''.''' + '''.'''.join(ls[3:]	)
															else:
																				snake_case_									=				k_new.replace('''network''',      '''swiftformer.encoder.network'''	)
										rename_keys.append((k, k_new)	)
					return rename_keys


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				SwiftFormerConfig()

					# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
					snake_case_									=				1000
					snake_case_									=				'''huggingface/label-files'''
					snake_case_									=				'''imagenet-1k-id2label.json'''
					snake_case_									=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      repo_type='''dataset'''	),      '''r'''	)	)
					snake_case_									=				{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
					snake_case_									=				idalabel
					snake_case_									=				{v: k for k, v in idalabel.items()}

					# size of the architecture
					if swiftformer_name == "swiftformer_xs":
										snake_case_									=				[3, 3, 6, 4]
										snake_case_									=				[48, 56, 112, 220]

					elif swiftformer_name == "swiftformer_s":
										snake_case_									=				[3, 3, 9, 6]
										snake_case_									=				[48, 64, 168, 224]

					elif swiftformer_name == "swiftformer_l1":
										snake_case_									=				[4, 3, 10, 5]
										snake_case_									=				[48, 96, 192, 384]

					elif swiftformer_name == "swiftformer_l3":
										snake_case_									=				[4, 4, 12, 6]
										snake_case_									=				[64, 128, 320, 512]

					# load state_dict of original model, remove and rename some keys
					if original_ckpt:
										if original_ckpt.startswith('''https'''	):
															snake_case_									=				torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_,      map_location='''cpu''',      check_hash=SCREAMING_SNAKE_CASE_	)
										else:
															snake_case_									=				torch.load(SCREAMING_SNAKE_CASE_,      map_location='''cpu'''	)
					snake_case_									=				checkpoint

					snake_case_									=				create_rename_keys(SCREAMING_SNAKE_CASE_	)
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

					# load HuggingFace model
					snake_case_									=				SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_	).eval()
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_	)

					# prepare test inputs
					snake_case_									=				prepare_img()
					snake_case_									=				ViTImageProcessor.from_pretrained('''preprocessor_config'''	)
					snake_case_									=				processor(images=SCREAMING_SNAKE_CASE_,      return_tensors='''pt'''	)

					# compare outputs from both models
					snake_case_									=				get_expected_output(SCREAMING_SNAKE_CASE_	)
					snake_case_									=				hf_model(inputs['''pixel_values''']	).logits

					assert hf_logits.shape == torch.Size([1, 1000]	)
					assert torch.allclose(hf_logits[0, 0:5],      SCREAMING_SNAKE_CASE_,      atol=1e-3	)

					Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
					print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"	)
					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
							a  :				Dict									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--swiftformer_name',
							    default='swiftformer_xs',
							    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
							    type=str,
							    help='Name of the SwiftFormer model you\'d like to convert.',
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default='./converted_outputs/',
							    type=str,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

							a  :				Any									=  parser.parse_args()
							convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)







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_     )




