



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					(   lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : int      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : Optional[Any]    )				->			Union[str, Any]:






						'''simple docstring'''



						UpperCAmelCase_=       original_name.split("""."""    )[0]
						UpperCAmelCase_=       key.split("""."""    )
						UpperCAmelCase_=       int(key_list[key_list.index(lowerCAmelCase_    ) - 2]    )
						UpperCAmelCase_=       int(key_list[key_list.index(lowerCAmelCase_    ) - 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 	__a					(   lowerCAmelCase_       : Optional[Any]    )				->			List[Any]:






						'''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(lowerCAmelCase_      ,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(lowerCAmelCase_      ,lowerCAmelCase_      ,"""mlp.fc1"""      ,"""output.conv1"""    )
												if "mlp.fc2" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""mlp.fc2"""      ,"""output.conv2"""    )
												if "norm1" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""norm1"""      ,"""before_norm"""    )
												if "norm2" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""norm2"""      ,"""after_norm"""    )
												if "layer_scale_1" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""layer_scale_1"""      ,"""layer_scale_1"""    )
												if "layer_scale_2" in key:
																		UpperCAmelCase_=       replace_key_with_offset(lowerCAmelCase_      ,lowerCAmelCase_      ,"""layer_scale_2"""      ,"""layer_scale_2"""    )
												if "head" in key:
																		UpperCAmelCase_=       key.replace("""head"""      ,"""classifier"""    )
												UpperCAmelCase_=       value
						return new_state_dict



def 	__a					(   )				->			Dict:






						'''simple docstring'''



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

						return image



@torch.no_grad()
def 	__a					(   lowerCAmelCase_       : str      ,lowerCAmelCase_       : List[str]      ,lowerCAmelCase_       : List[str]    )				->			Tuple:






						'''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(lowerCAmelCase_      ,lowerCAmelCase_      ,repo_type="""dataset"""    )      ,"""r"""    )    )
						UpperCAmelCase_=       {int(lowerCAmelCase_    ): 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=lowerCAmelCase_    )

						# Prepare image
						UpperCAmelCase_=       prepare_img()
						UpperCAmelCase_=       image_processor(images=lowerCAmelCase_      ,return_tensors="""pt"""    ).pixel_values

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

						# load original state dict
						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location=torch.device("""cpu"""    )    )

						# rename keys
						UpperCAmelCase_=       rename_keys(lowerCAmelCase_    )

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

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

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

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









def 	__a					(   lowerCAmelCase_       : Dict    )				->			Dict:






						'''simple docstring'''



						return [
						    {
						        0: [1, 2],
						        1: [0, 2],
						        2: [0, 1, 3, 5],
						        3: [2, 4],
						        4: [3],
						        5: [2, 6, 8],
						        6: [5, 7],
						        7: [6, 8],
						        8: [5, 7],
						    },
						    {
						        0: [6],
						        1: [9],
						        2: [4, 5],
						        3: [4],
						        4: [2, 3],
						        5: [2],
						        6: [0, 7],
						        7: [6],
						        8: [],
						        9: [1],
						    },
						    {
						        0: [4],
						        1: [6],
						        2: [],
						        3: [5, 6, 7],
						        4: [0, 6],
						        5: [3, 8, 9],
						        6: [1, 3, 4, 7],
						        7: [3, 6, 8, 9],
						        8: [5, 7],
						        9: [5, 7],
						    },
						    {
						        0: [1, 3],
						        1: [0, 2, 4],
						        2: [1, 3, 4],
						        3: [0, 2, 4],
						        4: [1, 2, 3],
						    },
						][index]



def 	__a					(   lowerCAmelCase_       : dict[int, list[int]]    )				->			list[tuple[int, int]]:






						'''simple docstring'''



						UpperCAmelCase_=       0
						UpperCAmelCase_=       len(lowerCAmelCase_    )  # No of vertices in graph
						UpperCAmelCase_=       [0] * n
						UpperCAmelCase_=       [False] * n

						def dfs(lowerCAmelCase_       : Optional[int]      ,lowerCAmelCase_       : Dict      ,lowerCAmelCase_       : Any      ,lowerCAmelCase_       : int    ):
												UpperCAmelCase_=       True
												UpperCAmelCase_=       id_
												id_ += 1
												for to in graph[at]:
																		if to == parent:
																								pass
																		elif not visited[to]:
																								dfs(lowerCAmelCase_      ,lowerCAmelCase_      ,lowerCAmelCase_      ,id_    )
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )
																								if id_ <= low[to]:
																														bridges.append((at, to) if at < to else (to, at)    )
																		else:
																								# This edge is a back edge and cannot be a bridge
																								UpperCAmelCase_=       min(low[at]      ,low[to]    )

						UpperCAmelCase_=       []
						for i in range(lowerCAmelCase_    ):
												if not visited[i]:
																		dfs(lowerCAmelCase_      ,-1      ,lowerCAmelCase_      ,id_    )
						return bridges


if __name__ == "__main__":
	import doctest

	doctest.testmod()




