






import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A_       :List[Any]       =						logging.get_logger(__name__)






def 							A     (       a_							,a_							,a_							,a_					)     ->						Any:
					__UpperCamelCase      :					List[Any]       		=original_name.split('.'					)[0]
					__UpperCamelCase      :					Any       		=key.split('.'					)
					__UpperCamelCase      :					Any       		=int(key_list[key_list.index(a_					) - 2]					)
					__UpperCamelCase      :					Optional[int]       		=int(key_list[key_list.index(a_					) - 1]					)
					__UpperCamelCase      :					Tuple       		=orig_block_num - offset

					__UpperCamelCase      :					Any       		=key.replace(F'{orig_block_num}.{layer_num}.{original_name}'							,F'block.{new_block_num}.{layer_num}.{new_name}'					)
					return key






def 							A     (       a_					)     ->						Optional[int]:
					__UpperCamelCase      :					List[str]       		=OrderedDict()
					__UpperCamelCase		,				__UpperCamelCase      :					Dict       		=0, 0
					for key, value in state_dict.items():
										if key.startswith('network'					):
															__UpperCamelCase      :					str       		=key.replace('network'							,'poolformer.encoder'					)
										if "proj" in key:
															# Works for the first embedding as well as the internal embedding layers
															if key.endswith('bias'					) and "patch_embed" not in key:
																				patch_emb_offset += 1
															__UpperCamelCase      :					int       		=key[: key.find('proj'					)]
															__UpperCamelCase      :					str       		=key.replace(a_							,F'patch_embeddings.{total_embed_found}.'					)
															__UpperCamelCase      :					int       		=key.replace('proj'							,'projection'					)
															if key.endswith('bias'					):
																				total_embed_found += 1
										if "patch_embeddings" in key:
															__UpperCamelCase      :					str       		='poolformer.encoder.' + key
										if "mlp.fc1" in key:
															__UpperCamelCase      :					Any       		=replace_key_with_offset(a_							,a_							,'mlp.fc1'							,'output.conv1'					)
										if "mlp.fc2" in key:
															__UpperCamelCase      :					Tuple       		=replace_key_with_offset(a_							,a_							,'mlp.fc2'							,'output.conv2'					)
										if "norm1" in key:
															__UpperCamelCase      :					Any       		=replace_key_with_offset(a_							,a_							,'norm1'							,'before_norm'					)
										if "norm2" in key:
															__UpperCamelCase      :					List[str]       		=replace_key_with_offset(a_							,a_							,'norm2'							,'after_norm'					)
										if "layer_scale_1" in key:
															__UpperCamelCase      :					Tuple       		=replace_key_with_offset(a_							,a_							,'layer_scale_1'							,'layer_scale_1'					)
										if "layer_scale_2" in key:
															__UpperCamelCase      :					Dict       		=replace_key_with_offset(a_							,a_							,'layer_scale_2'							,'layer_scale_2'					)
										if "head" in key:
															__UpperCamelCase      :					List[Any]       		=key.replace('head'							,'classifier'					)
										__UpperCamelCase      :					Optional[Any]       		=value
					return new_state_dict






def 							A     (       )     ->						int:
					__UpperCamelCase      :					List[Any]       		='http://images.cocodataset.org/val2017/000000039769.jpg'
					__UpperCamelCase      :					Dict       		=Image.open(requests.get(a_							,stream=a_					).raw					)

					return image






@torch.no_grad()
def 							A     (       a_							,a_							,a_					)     ->						List[str]:
					__UpperCamelCase      :					Optional[Any]       		=PoolFormerConfig()

					# set attributes based on model_name
					__UpperCamelCase      :					Union[str, Any]       		='huggingface/label-files'
					__UpperCamelCase      :					List[Any]       		=model_name[-3:]
					__UpperCamelCase      :					Optional[int]       		=1_000
					__UpperCamelCase      :					str       		='imagenet-1k-id2label.json'
					__UpperCamelCase      :					Any       		=(1, 1_000)

					# set config attributes
					__UpperCamelCase      :					Tuple       		=json.load(open(hf_hub_download(a_							,a_							,repo_type='dataset'					)							,'r'					)					)
					__UpperCamelCase      :					Optional[Any]       		={int(a_					): v for k, v in idalabel.items()}
					__UpperCamelCase      :					str       		=idalabel
					__UpperCamelCase      :					List[str]       		={v: k for k, v in idalabel.items()}
					if size == "s12":
										__UpperCamelCase      :					Tuple       		=[2, 2, 6, 2]
										__UpperCamelCase      :					List[Any]       		=[64, 128, 320, 512]
										__UpperCamelCase      :					Any       		=4.0
										__UpperCamelCase      :					Tuple       		=0.9
					elif size == "s24":
										__UpperCamelCase      :					str       		=[4, 4, 12, 4]
										__UpperCamelCase      :					Optional[Any]       		=[64, 128, 320, 512]
										__UpperCamelCase      :					int       		=4.0
										__UpperCamelCase      :					Union[str, Any]       		=0.9
					elif size == "s36":
										__UpperCamelCase      :					int       		=[6, 6, 18, 6]
										__UpperCamelCase      :					int       		=[64, 128, 320, 512]
										__UpperCamelCase      :					List[Any]       		=4.0
										__UpperCamelCase      :					Any       		=1e-6
										__UpperCamelCase      :					str       		=0.9
					elif size == "m36":
										__UpperCamelCase      :					Optional[Any]       		=[6, 6, 18, 6]
										__UpperCamelCase      :					Union[str, Any]       		=[96, 192, 384, 768]
										__UpperCamelCase      :					int       		=4.0
										__UpperCamelCase      :					Tuple       		=1e-6
										__UpperCamelCase      :					Dict       		=0.95
					elif size == "m48":
										__UpperCamelCase      :					Union[str, Any]       		=[8, 8, 24, 8]
										__UpperCamelCase      :					List[Any]       		=[96, 192, 384, 768]
										__UpperCamelCase      :					Optional[Any]       		=4.0
										__UpperCamelCase      :					Optional[Any]       		=1e-6
										__UpperCamelCase      :					Dict       		=0.95
					else:
										raise ValueError(F'Size {size} not supported'					)

					# load image processor
					__UpperCamelCase      :					List[str]       		=PoolFormerImageProcessor(crop_pct=a_					)

					# Prepare image
					__UpperCamelCase      :					Optional[Any]       		=prepare_img()
					__UpperCamelCase      :					int       		=image_processor(images=a_							,return_tensors='pt'					).pixel_values

					logger.info(F'Converting model {model_name}...'					)

					# load original state dict
					__UpperCamelCase      :					List[str]       		=torch.load(a_							,map_location=torch.device('cpu'					)					)

					# rename keys
					__UpperCamelCase      :					Union[str, Any]       		=rename_keys(a_					)

					# create HuggingFace model and load state dict
					__UpperCamelCase      :					List[Any]       		=PoolFormerForImageClassification(a_					)
					model.load_state_dict(a_					)
					model.eval()

					# Define image processor
					__UpperCamelCase      :					List[Any]       		=PoolFormerImageProcessor(crop_pct=a_					)
					__UpperCamelCase      :					str       		=image_processor(images=prepare_img()							,return_tensors='pt'					).pixel_values

					# forward pass
					__UpperCamelCase      :					List[Any]       		=model(a_					)
					__UpperCamelCase      :					Union[str, Any]       		=outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										__UpperCamelCase      :					List[Any]       		=torch.tensor([-0.3_045, -0.6_758, -0.4_869]					)
					elif size == "s24":
										__UpperCamelCase      :					Tuple       		=torch.tensor([0.4_402, -0.1_374, -0.8_045]					)
					elif size == "s36":
										__UpperCamelCase      :					List[Any]       		=torch.tensor([-0.6_080, -0.5_133, -0.5_898]					)
					elif size == "m36":
										__UpperCamelCase      :					Optional[int]       		=torch.tensor([0.3_952, 0.2_263, -1.2_668]					)
					elif size == "m48":
										__UpperCamelCase      :					List[str]       		=torch.tensor([0.1_167, -0.0_656, -0.3_423]					)
					else:
										raise ValueError(F'Size {size} not supported'					)

					# verify logits
					assert logits.shape == expected_shape
					assert torch.allclose(logits[0, :3]							,a_							,atol=1e-2					)

					# finally, save model and image processor
					logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'					)
					Path(a_					).mkdir(exist_ok=a_					)
					model.save_pretrained(a_					)
					print(F'Saving image processor to {pytorch_dump_folder_path}'					)
					image_processor.save_pretrained(a_					)


if __name__ == "__main__":
							A_       :Tuple       =						argparse.ArgumentParser()

							parser.add_argument(
							    '''--model_name''',
							    default='''poolformer_s12''',
							    type=str,
							    help='''Name of the model you\'d like to convert.''',
							)
							parser.add_argument(
							    '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
							)
							A_       :Dict       =						parser.parse_args()
							convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







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 					lowerCamelCase__       (						A__       :					Union[str, Any]  ,			A__       :					str  ,			A__       :					Any  ,			A__       :					Dict  ,			A__       :					Any=0	):





       '''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	):
              __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(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:
                     __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(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:
                     __lowerCamelCase   =						os.path.join(A__  ,			f'{MODEL_NAME}_{model_index}'	)
                     os.makedirs(A__  ,			exist_ok=A__	)
                     logger.info(f'Saving model to {ckpt_dir}'	)
                     __lowerCamelCase   =						{"""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__       :					int  ,			A__       :					Dict  ,			A__       :					int  ,			A__       :					List[str]  ,			A__       :					Any=0	):





       '''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
                     __lowerCamelCase   =						f'{MODEL_NAME}.bin' if model_index == 0 else f'{MODEL_NAME}_{model_index}.bin'
                     __lowerCamelCase   =						os.path.join(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     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(A__  ,			A__	)
                     logger.info(f'Loading model from {input_model_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Model loaded from {input_model_file}'	)
              elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
                     __lowerCamelCase   =						(
                         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}'	)
                     __lowerCamelCase   =						{"""model""": model.state_dict()}
                     dist_cp.load_state_dict(
                         state_dict=A__  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			planner=DefaultLoadPlanner()  ,			)
                     __lowerCamelCase   =						state_dict["""model"""]
                     logger.info(f'Model loaded from {ckpt_dir}'	)
              model.load_state_dict(A__	)




def 					lowerCamelCase__       (						A__       :					List[str]  ,			A__       :					List[str]  ,			A__       :					str  ,			A__       :					Dict  ,			A__       :					Optional[Any]  ,			A__       :					Optional[int]=0	):





       '''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	):
              __lowerCamelCase   =						FSDP.optim_state_dict(A__  ,			A__	)
              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(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:
                     __lowerCamelCase   =						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__       :					int  ,			A__       :					List[str]  ,			A__       :					int  ,			A__       :					Any  ,			A__       :					Union[str, Any]  ,			A__       :					List[Any]=0	):





       '''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:
                     __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(A__  ,			A__	)
                     logger.info(f'Loading Optimizer state from {input_optimizer_file}'	)
                     __lowerCamelCase   =						torch.load(A__	)
                     logger.info(f'Optimizer state loaded from {input_optimizer_file}'	)
              else:
                     __lowerCamelCase   =						(
                         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}'	)
                     __lowerCamelCase   =						load_sharded_optimizer_state_dict(
                         model_state_dict=model.state_dict()  ,			optimizer_key="""optimizer"""  ,			storage_reader=dist_cp.FileSystemReader(A__	)  ,			)
                     __lowerCamelCase   =						optim_state["""optimizer"""]
                     logger.info(f'Optimizer loaded from {ckpt_dir}'	)
              __lowerCamelCase   =						FSDP.optim_state_dict_to_load(A__  ,			A__  ,			A__	)
              optimizer.load_state_dict(A__	)

