



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()
_snake_case	=						logging.get_logger(__name__)






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[int]				     =					original_name.split("."						)[0]
							_lowerCAmelCase  :							Optional[Any]				     =					key.split("."						)
							_lowerCAmelCase  :							str				     =					int(key_list[key_list.index(_lowerCamelCase						) - 2]						)
							_lowerCAmelCase  :							Union[str, Any]				     =					int(key_list[key_list.index(_lowerCamelCase						) - 1]						)
							_lowerCAmelCase  :							str				     =					orig_block_num - offset

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






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					OrderedDict()
							_lowerCAmelCase     ,					_lowerCAmelCase  :							Dict				     =					0, 0
							for key, value in state_dict.items():
														if key.startswith("network"						):
																					_lowerCAmelCase  :							Dict				     =					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
																					_lowerCAmelCase  :							str				     =					key[: key.find("proj"						)]
																					_lowerCAmelCase  :							str				     =					key.replace(_lowerCamelCase       ,				F"patch_embeddings.{total_embed_found}."						)
																					_lowerCAmelCase  :							str				     =					key.replace("proj"       ,				"projection"						)
																					if key.endswith("bias"						):
																												total_embed_found += 1
														if "patch_embeddings" in key:
																					_lowerCAmelCase  :							Tuple				     =					"poolformer.encoder." + key
														if "mlp.fc1" in key:
																					_lowerCAmelCase  :							Optional[Any]				     =					replace_key_with_offset(_lowerCamelCase       ,				_lowerCamelCase       ,				"mlp.fc1"       ,				"output.conv1"						)
														if "mlp.fc2" in key:
																					_lowerCAmelCase  :							List[Any]				     =					replace_key_with_offset(_lowerCamelCase       ,				_lowerCamelCase       ,				"mlp.fc2"       ,				"output.conv2"						)
														if "norm1" in key:
																					_lowerCAmelCase  :							List[Any]				     =					replace_key_with_offset(_lowerCamelCase       ,				_lowerCamelCase       ,				"norm1"       ,				"before_norm"						)
														if "norm2" in key:
																					_lowerCAmelCase  :							str				     =					replace_key_with_offset(_lowerCamelCase       ,				_lowerCamelCase       ,				"norm2"       ,				"after_norm"						)
														if "layer_scale_1" in key:
																					_lowerCAmelCase  :							Optional[Any]				     =					replace_key_with_offset(_lowerCamelCase       ,				_lowerCamelCase       ,				"layer_scale_1"       ,				"layer_scale_1"						)
														if "layer_scale_2" in key:
																					_lowerCAmelCase  :							Any				     =					replace_key_with_offset(_lowerCamelCase       ,				_lowerCamelCase       ,				"layer_scale_2"       ,				"layer_scale_2"						)
														if "head" in key:
																					_lowerCAmelCase  :							Tuple				     =					key.replace("head"       ,				"classifier"						)
														_lowerCAmelCase  :							int				     =					value
							return new_state_dict






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							str				     =					"http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase  :							Dict				     =					Image.open(requests.get(_lowerCamelCase       ,				stream=_lowerCamelCase						).raw						)

							return image






@torch.no_grad()
def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					PoolFormerConfig()

							# set attributes based on model_name
							_lowerCAmelCase  :							Dict				     =					"huggingface/label-files"
							_lowerCAmelCase  :							List[str]				     =					model_name[-3:]
							_lowerCAmelCase  :							List[Any]				     =					1_000
							_lowerCAmelCase  :							Tuple				     =					"imagenet-1k-id2label.json"
							_lowerCAmelCase  :							str				     =					(1, 1_000)

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

							# load image processor
							_lowerCAmelCase  :							Optional[int]				     =					PoolFormerImageProcessor(crop_pct=_lowerCamelCase						)

							# Prepare image
							_lowerCAmelCase  :							List[str]				     =					prepare_img()
							_lowerCAmelCase  :							str				     =					image_processor(images=_lowerCamelCase       ,				return_tensors="pt"						).pixel_values

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

							# load original state dict
							_lowerCAmelCase  :							Tuple				     =					torch.load(_lowerCamelCase       ,				map_location=torch.device("cpu"						)						)

							# rename keys
							_lowerCAmelCase  :							List[str]				     =					rename_keys(_lowerCamelCase						)

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

							# Define image processor
							_lowerCAmelCase  :							Dict				     =					PoolFormerImageProcessor(crop_pct=_lowerCamelCase						)
							_lowerCAmelCase  :							List[str]				     =					image_processor(images=prepare_img()       ,				return_tensors="pt"						).pixel_values

							# forward pass
							_lowerCAmelCase  :							Optional[Any]				     =					model(_lowerCamelCase						)
							_lowerCAmelCase  :							List[Any]				     =					outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														_lowerCAmelCase  :							Optional[int]				     =					torch.tensor([-0.30_45, -0.67_58, -0.48_69]						)
							elif size == "s24":
														_lowerCAmelCase  :							Tuple				     =					torch.tensor([0.44_02, -0.13_74, -0.80_45]						)
							elif size == "s36":
														_lowerCAmelCase  :							Tuple				     =					torch.tensor([-0.60_80, -0.51_33, -0.58_98]						)
							elif size == "m36":
														_lowerCAmelCase  :							Tuple				     =					torch.tensor([0.39_52, 0.22_63, -1.26_68]						)
							elif size == "m48":
														_lowerCAmelCase  :							List[Any]				     =					torch.tensor([0.11_67, -0.06_56, -0.34_23]						)
							else:
														raise ValueError(F"Size {size} not supported"						)

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

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


if __name__ == "__main__":
						_snake_case	=						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."
						)
						_snake_case	=						parser.parse_args()
						convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





from __future__ import annotations






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							if not nums:
														raise ValueError("List is empty"						)
							return sum(_lowerCamelCase						) / len(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
