

"""simple docstring"""


from math import ceil






def    a__   (   __SCREAMING_SNAKE_CASE = 1_0_0_1			)     ->     int:
		SCREAMING_SNAKE_CASE:	Optional[int]   				=      1

		for i in range(1 ,			int(ceil(n / 2.0			)			)			):
				SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      2 * i + 1
				SCREAMING_SNAKE_CASE:	Dict   				=      2 * i
				SCREAMING_SNAKE_CASE:	str   				=      total + 4 * odd**2 - 6 * even

		return total


if __name__ == "__main__":
			import sys

			if len(sys.argv) == 1:
						print(solution())
			else:
						try:
									__A						       =      int(sys.argv[1])
									print(solution(n))
						except ValueError:
									print("Invalid entry - please enter a number")





"""simple docstring"""


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						       =      logging.get_logger(__name__)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		__lowerCAmelCase:	Dict   				=      original_name.split("."			)[0]
		__lowerCAmelCase:	Any   				=      key.split("."			)
		__lowerCAmelCase:	Union[str, Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 2]			)
		__lowerCAmelCase:	List[Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 1]			)
		__lowerCAmelCase:	List[str]   				=      orig_block_num - offset

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






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		__lowerCAmelCase:	List[Any]   				=      OrderedDict()
		__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      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:	int   				=      key[: key.find("proj"			)]
						__lowerCAmelCase:	Dict   				=      key.replace(__SCREAMING_SNAKE_CASE ,			F"patch_embeddings.{total_embed_found}."			)
						__lowerCAmelCase:	Optional[int]   				=      key.replace("proj" ,			"projection"			)
						if key.endswith("bias"			):
								total_embed_found += 1
				if "patch_embeddings" in key:
						__lowerCAmelCase:	int   				=      "poolformer.encoder." + key
				if "mlp.fc1" in key:
						__lowerCAmelCase:	Optional[Any]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc1" ,			"output.conv1"			)
				if "mlp.fc2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc2" ,			"output.conv2"			)
				if "norm1" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm1" ,			"before_norm"			)
				if "norm2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm2" ,			"after_norm"			)
				if "layer_scale_1" in key:
						__lowerCAmelCase:	Optional[int]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_1" ,			"layer_scale_1"			)
				if "layer_scale_2" in key:
						__lowerCAmelCase:	Any   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_2" ,			"layer_scale_2"			)
				if "head" in key:
						__lowerCAmelCase:	int   				=      key.replace("head" ,			"classifier"			)
				__lowerCAmelCase:	Tuple   				=      value
		return new_state_dict






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Union[str, Any]   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	int   				=      Image.open(requests.get(__SCREAMING_SNAKE_CASE ,			stream=__SCREAMING_SNAKE_CASE			).raw			)

		return image






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      PoolFormerConfig()

		# set attributes based on model_name
		__lowerCAmelCase:	Any   				=      "huggingface/label-files"
		__lowerCAmelCase:	int   				=      model_name[-3:]
		__lowerCAmelCase:	List[Any]   				=      1_0_0_0
		__lowerCAmelCase:	Tuple   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	str   				=      (1, 1_0_0_0)

		# set config attributes
		__lowerCAmelCase:	Dict   				=      json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			repo_type="dataset"			) ,			"r"			)			)
		__lowerCAmelCase:	List[str]   				=      {int(__SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
		__lowerCAmelCase:	Any   				=      idalabel
		__lowerCAmelCase:	Any   				=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				__lowerCAmelCase:	Dict   				=      [2, 2, 6, 2]
				__lowerCAmelCase:	str   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Optional[Any]   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      0.9
		elif size == "s24":
				__lowerCAmelCase:	Tuple   				=      [4, 4, 1_2, 4]
				__lowerCAmelCase:	List[str]   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Tuple   				=      4.0
				__lowerCAmelCase:	Optional[int]   				=      0.9
		elif size == "s36":
				__lowerCAmelCase:	int   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	int   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	List[str]   				=      4.0
				__lowerCAmelCase:	Dict   				=      1E-6
				__lowerCAmelCase:	List[Any]   				=      0.9
		elif size == "m36":
				__lowerCAmelCase:	Dict   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	Dict   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      1E-6
				__lowerCAmelCase:	Union[str, Any]   				=      0.95
		elif size == "m48":
				__lowerCAmelCase:	str   				=      [8, 8, 2_4, 8]
				__lowerCAmelCase:	Optional[int]   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	int   				=      1E-6
				__lowerCAmelCase:	str   				=      0.95
		else:
				raise ValueError(F"Size {size} not supported"			)

		# load image processor
		__lowerCAmelCase:	Union[str, Any]   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)

		# Prepare image
		__lowerCAmelCase:	int   				=      prepare_img()
		__lowerCAmelCase:	Tuple   				=      image_processor(images=__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			).pixel_values

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

		# load original state dict
		__lowerCAmelCase:	Optional[int]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)

		# rename keys
		__lowerCAmelCase:	Any   				=      rename_keys(__SCREAMING_SNAKE_CASE			)

		# create HuggingFace model and load state dict
		__lowerCAmelCase:	str   				=      PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		model.eval()

		# Define image processor
		__lowerCAmelCase:	Any   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      image_processor(images=prepare_img() ,			return_tensors="pt"			).pixel_values

		# forward pass
		__lowerCAmelCase:	int   				=      model(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.3045, -0.6758, -0.4869]			)
		elif size == "s24":
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([0.4402, -0.1374, -0.8045]			)
		elif size == "s36":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.6080, -0.5133, -0.5898]			)
		elif size == "m36":
				__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([0.3952, 0.2263, -1.2668]			)
		elif size == "m48":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([0.1167, -0.0656, -0.3423]			)
		else:
				raise ValueError(F"Size {size} not supported"			)

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

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


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


