"""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					:					List[str]=       logging.get_logger(__name__)
def   __UpperCAmelCase    (   UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    List[str]      ,							UpperCAmelCase_   :    List[str]  )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							int						=			original_name.split('.'  )[0]
	__snake_case      :							int						=			key.split('.'  )
	__snake_case      :							Optional[Any]						=			int(key_list[key_list.index(UpperCAmelCase_  ) - 2]  )
	__snake_case      :							Tuple						=			int(key_list[key_list.index(UpperCAmelCase_  ) - 1]  )
	__snake_case      :							Dict						=			orig_block_num - offset

	__snake_case      :							Optional[Any]						=			key.replace(F"{orig_block_num}.{layer_num}.{original_name}"      ,							F"block.{new_block_num}.{layer_num}.{new_name}"  )
	return key
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Union[str, Any]  )							->      List[Any]:

	'''simple docstring'''





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

	'''simple docstring'''





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

	return image
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    List[Any]  )							->      str:

	'''simple docstring'''





	__snake_case      :							Any						=			PoolFormerConfig()

	# set attributes based on model_name
	__snake_case      :							Union[str, Any]						=			'huggingface/label-files'
	__snake_case      :							Dict						=			model_name[-3:]
	__snake_case      :							Optional[int]						=			10_00
	__snake_case      :							str						=			'imagenet-1k-id2label.json'
	__snake_case      :							str						=			(1, 10_00)

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

	# load image processor
	__snake_case      :							int						=			PoolFormerImageProcessor(crop_pct=UpperCAmelCase_  )

	# Prepare image
	__snake_case      :							List[str]						=			prepare_img()
	__snake_case      :							Union[str, Any]						=			image_processor(images=UpperCAmelCase_      ,							return_tensors='pt'  ).pixel_values

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

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

	# rename keys
	__snake_case      :							str						=			rename_keys(UpperCAmelCase_  )

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

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

	# forward pass
	__snake_case      :							Any						=			model(UpperCAmelCase_  )
	__snake_case      :							Optional[int]						=			outputs.logits

	# define expected logit slices for different models
	if size == "s12":
		__snake_case      :							Optional[Any]						=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]  )
	elif size == "s24":
		__snake_case      :							List[Any]						=			torch.tensor([0.4_402, -0.1_374, -0.8_045]  )
	elif size == "s36":
		__snake_case      :							Optional[Any]						=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]  )
	elif size == "m36":
		__snake_case      :							List[Any]						=			torch.tensor([0.3_952, 0.2_263, -1.2_668]  )
	elif size == "m48":
		__snake_case      :							Optional[int]						=			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					:					str=       parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)






"""simple docstring"""







from __future__ import annotations

from math import pow, sqrt
def   __UpperCAmelCase    (   UpperCAmelCase_   :    float      ,							UpperCAmelCase_   :    float      ,							UpperCAmelCase_   :    float  )							->      dict[str, float]:

	'''simple docstring'''





	if (resistance, reactance, impedance).count(0  ) != 1:
		raise ValueError('One and only one argument must be 0'  )
	if resistance == 0:
		return {"resistance": sqrt(pow(UpperCAmelCase_      ,							2  ) - pow(UpperCAmelCase_      ,							2  )  )}
	elif reactance == 0:
		return {"reactance": sqrt(pow(UpperCAmelCase_      ,							2  ) - pow(UpperCAmelCase_      ,							2  )  )}
	elif impedance == 0:
		return {"impedance": sqrt(pow(UpperCAmelCase_      ,							2  ) + pow(UpperCAmelCase_      ,							2  )  )}
	else:
		raise ValueError('Exactly one argument must be 0'  )


if __name__ == "__main__":
	import doctest

	doctest.testmod()





