





"""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()
UpperCamelCase_									=logging.get_logger(__name__)





def 	a_    (							_lowercase	,						_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : int         =   original_name.split('''.'''       )[0]
						_UpperCamelCase      : Union[str, Any]         =   key.split('''.'''       )
						_UpperCamelCase      : Dict         =   int(key_list[key_list.index(snake_case_       ) - 2]       )
						_UpperCamelCase      : Optional[Any]         =   int(key_list[key_list.index(snake_case_       ) - 1]       )
						_UpperCamelCase      : List[str]         =   orig_block_num - offset

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





def 	a_    (							_lowercase       ):
						_UpperCamelCase      : List[Any]         =   OrderedDict()
						_UpperCamelCase      : Optional[Any]         =   0, 0
						for key, value in state_dict.items():
												if key.startswith('''network'''       ):
																		_UpperCamelCase      : Tuple         =   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      : List[str]         =   key[: key.find('''proj'''       )]
																		_UpperCamelCase      : List[str]         =   key.replace(snake_case_	,						F"""patch_embeddings.{total_embed_found}."""       )
																		_UpperCamelCase      : Tuple         =   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      : Union[str, Any]         =   replace_key_with_offset(snake_case_	,						snake_case_	,						'''mlp.fc1'''	,						'''output.conv1'''       )
												if "mlp.fc2" in key:
																		_UpperCamelCase      : Union[str, Any]         =   replace_key_with_offset(snake_case_	,						snake_case_	,						'''mlp.fc2'''	,						'''output.conv2'''       )
												if "norm1" in key:
																		_UpperCamelCase      : Tuple         =   replace_key_with_offset(snake_case_	,						snake_case_	,						'''norm1'''	,						'''before_norm'''       )
												if "norm2" in key:
																		_UpperCamelCase      : Any         =   replace_key_with_offset(snake_case_	,						snake_case_	,						'''norm2'''	,						'''after_norm'''       )
												if "layer_scale_1" in key:
																		_UpperCamelCase      : int         =   replace_key_with_offset(snake_case_	,						snake_case_	,						'''layer_scale_1'''	,						'''layer_scale_1'''       )
												if "layer_scale_2" in key:
																		_UpperCamelCase      : Any         =   replace_key_with_offset(snake_case_	,						snake_case_	,						'''layer_scale_2'''	,						'''layer_scale_2'''       )
												if "head" in key:
																		_UpperCamelCase      : Dict         =   key.replace('''head'''	,						'''classifier'''       )
												_UpperCamelCase      : Any         =   value
						return new_state_dict





def 	a_    (							):
						_UpperCamelCase      : List[str]         =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
						_UpperCamelCase      : Optional[Any]         =   Image.open(requests.get(snake_case_	,						stream=snake_case_       ).raw       )

						return image





@torch.no_grad()
def 	a_    (							_lowercase	,						_lowercase	,						_lowercase       ):
						_UpperCamelCase      : str         =   PoolFormerConfig()

						# set attributes based on model_name
						_UpperCamelCase      : str         =   """huggingface/label-files"""
						_UpperCamelCase      : Optional[Any]         =   model_name[-3:]
						_UpperCamelCase      : Optional[int]         =   1000
						_UpperCamelCase      : List[str]         =   """imagenet-1k-id2label.json"""
						_UpperCamelCase      : Any         =   (1, 1000)

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

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

						# Prepare image
						_UpperCamelCase      : int         =   prepare_img()
						_UpperCamelCase      : Tuple         =   image_processor(images=snake_case_	,						return_tensors='''pt'''       ).pixel_values

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

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

						# rename keys
						_UpperCamelCase      : str         =   rename_keys(snake_case_       )

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

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

						# forward pass
						_UpperCamelCase      : Optional[int]         =   model(snake_case_       )
						_UpperCamelCase      : Union[str, Any]         =   outputs.logits

						# define expected logit slices for different models
						if size == "s12":
												_UpperCamelCase      : List[Any]         =   torch.tensor([-0.3045, -0.6758, -0.4869]       )
						elif size == "s24":
												_UpperCamelCase      : Any         =   torch.tensor([0.4402, -0.1374, -0.8045]       )
						elif size == "s36":
												_UpperCamelCase      : Dict         =   torch.tensor([-0.6080, -0.5133, -0.5898]       )
						elif size == "m36":
												_UpperCamelCase      : Tuple         =   torch.tensor([0.3952, 0.2263, -1.2668]       )
						elif size == "m48":
												_UpperCamelCase      : Dict         =   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__":
						UpperCamelCase_									=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."""
						)
						UpperCamelCase_									=parser.parse_args()
						convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)










"""simple docstring"""




class 			_a						:






				def __init__(			self		:   Optional[int],			lowerCAmelCase__		:   list							)     ->				None:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   set_counts
										_UpperCamelCase      : Any         =   max(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   len(lowerCAmelCase__							)
										_UpperCamelCase      : int         =   [1] * num_sets
										_UpperCamelCase      : List[Any]         =   list(range(lowerCAmelCase__							)							)






				def     snake_case     (			self		:   int,			lowerCAmelCase__		:   int,			lowerCAmelCase__		:   int							)     ->				bool:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   self.get_parent(lowerCAmelCase__							)
										_UpperCamelCase      : Any         =   self.get_parent(lowerCAmelCase__							)

										if src_parent == dst_parent:
																return False

										if self.ranks[dst_parent] >= self.ranks[src_parent]:
																self.set_counts[dst_parent] += self.set_counts[src_parent]
																_UpperCamelCase      : List[Any]         =   0
																_UpperCamelCase      : str         =   dst_parent
																if self.ranks[dst_parent] == self.ranks[src_parent]:
																						self.ranks[dst_parent] += 1
																_UpperCamelCase      : Union[str, Any]         =   self.set_counts[dst_parent]
										else:
																self.set_counts[src_parent] += self.set_counts[dst_parent]
																_UpperCamelCase      : Tuple         =   0
																_UpperCamelCase      : Optional[Any]         =   src_parent
																_UpperCamelCase      : int         =   self.set_counts[src_parent]

										_UpperCamelCase      : Optional[Any]         =   max(self.max_set,			lowerCAmelCase__							)
										return True






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   int							)     ->				int:




										'''simple docstring'''



										if self.parents[disj_set] == disj_set:
																return disj_set
										_UpperCamelCase      : Union[str, Any]         =   self.get_parent(self.parents[disj_set]							)
										return self.parents[disj_set]



