
"""simple docstring"""





def  lowerCamelCase__						(     __snake_case = 60_08_51_47_51_43      )				->						int:


					"""simple docstring"""





					try:
										_UpperCamelCase									=    int(__snake_case      )
					except (TypeError, ValueError):
										raise TypeError('''Parameter n must be int or castable to int.'''      )
					if n <= 0:
										raise ValueError('''Parameter n must be greater than or equal to one.'''      )
					_UpperCamelCase									=    2
					_UpperCamelCase									=    0
					if n == 2:
										return 2
					while n > 2:
										while n % i != 0:
															i += 1
										_UpperCamelCase									=    i
										while n % i == 0:
															_UpperCamelCase									=    n // i
										i += 1
					return int(__snake_case      )


if __name__ == "__main__":
				print(F"""{solution() = }""")








"""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  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    original_name.split('''.'''      )[0]
					_UpperCamelCase									=    key.split('''.'''      )
					_UpperCamelCase									=    int(key_list[key_list.index(__snake_case      ) - 2]      )
					_UpperCamelCase									=    int(key_list[key_list.index(__snake_case      ) - 1]      )
					_UpperCamelCase									=    orig_block_num - offset

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



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





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



def  lowerCamelCase__						(     )				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_UpperCamelCase									=    Image.open(requests.get(__snake_case,	stream=__snake_case      ).raw      )

					return image



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case      )				->						int:


					"""simple docstring"""





					_UpperCamelCase									=    PoolFormerConfig()

					# set attributes based on model_name
					_UpperCamelCase									=    '''huggingface/label-files'''
					_UpperCamelCase									=    model_name[-3:]
					_UpperCamelCase									=    10_00
					_UpperCamelCase									=    '''imagenet-1k-id2label.json'''
					_UpperCamelCase									=    (1, 10_00)

					# set config attributes
					_UpperCamelCase									=    json.load(open(hf_hub_download(__snake_case,	__snake_case,	repo_type='''dataset'''      ),	'''r'''      )      )
					_UpperCamelCase									=    {int(__snake_case      ): v for k, v in idalabel.items()}
					_UpperCamelCase									=    idalabel
					_UpperCamelCase									=    {v: k for k, v in idalabel.items()}
					if size == "s12":
										_UpperCamelCase									=    [2, 2, 6, 2]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s24":
										_UpperCamelCase									=    [4, 4, 12, 4]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    0.9
					elif size == "s36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [64, 1_28, 3_20, 5_12]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.9
					elif size == "m36":
										_UpperCamelCase									=    [6, 6, 18, 6]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					elif size == "m48":
										_UpperCamelCase									=    [8, 8, 24, 8]
										_UpperCamelCase									=    [96, 1_92, 3_84, 7_68]
										_UpperCamelCase									=    4.0
										_UpperCamelCase									=    1e-6
										_UpperCamelCase									=    0.95
					else:
										raise ValueError(F'''Size {size} not supported'''      )

					# load image processor
					_UpperCamelCase									=    PoolFormerImageProcessor(crop_pct=__snake_case      )

					# Prepare image
					_UpperCamelCase									=    prepare_img()
					_UpperCamelCase									=    image_processor(images=__snake_case,	return_tensors='''pt'''      ).pixel_values

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

					# load original state dict
					_UpperCamelCase									=    torch.load(__snake_case,	map_location=torch.device('''cpu'''      )      )

					# rename keys
					_UpperCamelCase									=    rename_keys(__snake_case      )

					# create HuggingFace model and load state dict
					_UpperCamelCase									=    PoolFormerForImageClassification(__snake_case      )
					model.load_state_dict(__snake_case      )
					model.eval()

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

					# forward pass
					_UpperCamelCase									=    model(__snake_case      )
					_UpperCamelCase									=    outputs.logits

					# define expected logit slices for different models
					if size == "s12":
										_UpperCamelCase									=    torch.tensor([-0.3045, -0.6758, -0.4869]      )
					elif size == "s24":
										_UpperCamelCase									=    torch.tensor([0.4402, -0.1374, -0.8045]      )
					elif size == "s36":
										_UpperCamelCase									=    torch.tensor([-0.6080, -0.5133, -0.5898]      )
					elif size == "m36":
										_UpperCamelCase									=    torch.tensor([0.3952, 0.2263, -1.2668]      )
					elif size == "m48":
										_UpperCamelCase									=    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],	__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(__snake_case      ).mkdir(exist_ok=__snake_case      )
					model.save_pretrained(__snake_case      )
					print(F'''Saving image processor to {pytorch_dump_folder_path}'''      )
					image_processor.save_pretrained(__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)






