





'''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	: Any              =       logging.get_logger(__name__)





def 			lowerCAmelCase_						(    a					:					str   , a					:					int   , a					:					Any   , a					:					str				):
							a__            =							original_name.split('.'				)[0]
							a__            =							key.split('.'				)
							a__            =							int(key_list[key_list.index(UpperCAmelCase_				) - 2]				)
							a__            =							int(key_list[key_list.index(UpperCAmelCase_				) - 1]				)
							a__            =							orig_block_num - offset

							a__            =							key.replace(f'''{orig_block_num}.{layer_num}.{original_name}'''   , f'''block.{new_block_num}.{layer_num}.{new_name}'''				)
							return key





def 			lowerCAmelCase_						(    a					:					int				):
							a__            =							OrderedDict()
							a__            =							0, 0
							for key, value in state_dict.items():
														if key.startswith('network'				):
																					a__            =							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
																					a__            =							key[: key.find('proj'				)]
																					a__            =							key.replace(UpperCAmelCase_   , f'''patch_embeddings.{total_embed_found}.'''				)
																					a__            =							key.replace('proj'   , 'projection'				)
																					if key.endswith('bias'				):
																												total_embed_found += 1
														if "patch_embeddings" in key:
																					a__            =							'''poolformer.encoder.''' + key
														if "mlp.fc1" in key:
																					a__            =							replace_key_with_offset(UpperCAmelCase_   , UpperCAmelCase_   , 'mlp.fc1'   , 'output.conv1'				)
														if "mlp.fc2" in key:
																					a__            =							replace_key_with_offset(UpperCAmelCase_   , UpperCAmelCase_   , 'mlp.fc2'   , 'output.conv2'				)
														if "norm1" in key:
																					a__            =							replace_key_with_offset(UpperCAmelCase_   , UpperCAmelCase_   , 'norm1'   , 'before_norm'				)
														if "norm2" in key:
																					a__            =							replace_key_with_offset(UpperCAmelCase_   , UpperCAmelCase_   , 'norm2'   , 'after_norm'				)
														if "layer_scale_1" in key:
																					a__            =							replace_key_with_offset(UpperCAmelCase_   , UpperCAmelCase_   , 'layer_scale_1'   , 'layer_scale_1'				)
														if "layer_scale_2" in key:
																					a__            =							replace_key_with_offset(UpperCAmelCase_   , UpperCAmelCase_   , 'layer_scale_2'   , 'layer_scale_2'				)
														if "head" in key:
																					a__            =							key.replace('head'   , 'classifier'				)
														a__            =							value
							return new_state_dict





def 			lowerCAmelCase_						(    ):
							a__            =							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							a__            =							Image.open(requests.get(UpperCAmelCase_   , stream=UpperCAmelCase_				).raw				)

							return image





@torch.no_grad()
def 			lowerCAmelCase_						(    a					:					Optional[int]   , a					:					str   , a					:					Optional[int]				):
							a__            =							PoolFormerConfig()

							# set attributes based on model_name
							a__            =							'''huggingface/label-files'''
							a__            =							model_name[-3:]
							a__            =							1000
							a__            =							'''imagenet-1k-id2label.json'''
							a__            =							(1, 1000)

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

							# load image processor
							a__            =							PoolFormerImageProcessor(crop_pct=UpperCAmelCase_				)

							# Prepare image
							a__            =							prepare_img()
							a__            =							image_processor(images=UpperCAmelCase_   , return_tensors='pt'				).pixel_values

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

							# load original state dict
							a__            =							torch.load(UpperCAmelCase_   , map_location=torch.device('cpu'				)				)

							# rename keys
							a__            =							rename_keys(UpperCAmelCase_				)

							# create HuggingFace model and load state dict
							a__            =							PoolFormerForImageClassification(UpperCAmelCase_				)
							model.load_state_dict(UpperCAmelCase_				)
							model.eval()

							# Define image processor
							a__            =							PoolFormerImageProcessor(crop_pct=UpperCAmelCase_				)
							a__            =							image_processor(images=prepare_img()   , return_tensors='pt'				).pixel_values

							# forward pass
							a__            =							model(UpperCAmelCase_				)
							a__            =							outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														a__            =							torch.tensor([-0.3_045, -0.6_758, -0.4_869]				)
							elif size == "s24":
														a__            =							torch.tensor([0.4_402, -0.1_374, -0.8_045]				)
							elif size == "s36":
														a__            =							torch.tensor([-0.6_080, -0.5_133, -0.5_898]				)
							elif size == "m36":
														a__            =							torch.tensor([0.3_952, 0.2_263, -1.2_668]				)
							elif size == "m48":
														a__            =							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]   , UpperCAmelCase_   , atol=1e-2				)

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


if __name__ == "__main__":
		__A	: List[str]              =       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	: Optional[int]              =       parser.parse_args()
		convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)







import os
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       with open(os.path.dirname(UpperCAmelCase_				) + '''/p022_names.txt'''				) as file:
              SCREAMING_SNAKE_CASE_							:				Any		=str(file.readlines()[0]				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=names.replace('''"'''			,							''''''				).split(''','''				)

       names.sort()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

       for i, name in enumerate(UpperCAmelCase_				):
              for letter in name:
                     name_score += ord(UpperCAmelCase_				) - 6_4

              total_score += (i + 1) * name_score
              SCREAMING_SNAKE_CASE_							:				Any		=0
       return total_score


if __name__ == "__main__":
 print(solution())
