



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

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				original_name.split('''.''')[0]
						__lowerCAmelCase							       =				key.split('''.''')
						__lowerCAmelCase							       =				int(key_list[key_list.index(lowerCamelCase) - 2])
						__lowerCAmelCase							       =				int(key_list[key_list.index(lowerCamelCase) - 1])
						__lowerCAmelCase							       =				orig_block_num - offset

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

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

def 							__magic_name__(						):
						__lowerCAmelCase							       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__lowerCAmelCase							       =				Image.open(requests.get(lowerCamelCase,				stream=lowerCamelCase).raw)

						return image

@torch.no_grad()
def 							__magic_name__(						lowerCamelCase,				lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				PoolFormerConfig()

						# set attributes based on model_name
						__lowerCAmelCase							       =				'''huggingface/label-files'''
						__lowerCAmelCase							       =				model_name[-3:]
						__lowerCAmelCase							       =				1_0_0_0
						__lowerCAmelCase							       =				'''imagenet-1k-id2label.json'''
						__lowerCAmelCase							       =				(1, 1_0_0_0)

						# set config attributes
						__lowerCAmelCase							       =				json.load(open(hf_hub_download(lowerCamelCase,				lowerCamelCase,				repo_type='''dataset'''),				'''r'''))
						__lowerCAmelCase							       =				{int(lowerCamelCase): v for k, v in idalabel.items()}
						__lowerCAmelCase							       =				idalabel
						__lowerCAmelCase							       =				{v: k for k, v in idalabel.items()}
						if size == "s12":
												__lowerCAmelCase							       =				[2, 2, 6, 2]
												__lowerCAmelCase							       =				[6_4, 1_2_8, 3_2_0, 5_1_2]
												__lowerCAmelCase							       =				4.0
												__lowerCAmelCase							       =				0.9
						elif size == "s24":
												__lowerCAmelCase							       =				[4, 4, 1_2, 4]
												__lowerCAmelCase							       =				[6_4, 1_2_8, 3_2_0, 5_1_2]
												__lowerCAmelCase							       =				4.0
												__lowerCAmelCase							       =				0.9
						elif size == "s36":
												__lowerCAmelCase							       =				[6, 6, 1_8, 6]
												__lowerCAmelCase							       =				[6_4, 1_2_8, 3_2_0, 5_1_2]
												__lowerCAmelCase							       =				4.0
												__lowerCAmelCase							       =				1E-6
												__lowerCAmelCase							       =				0.9
						elif size == "m36":
												__lowerCAmelCase							       =				[6, 6, 1_8, 6]
												__lowerCAmelCase							       =				[9_6, 1_9_2, 3_8_4, 7_6_8]
												__lowerCAmelCase							       =				4.0
												__lowerCAmelCase							       =				1E-6
												__lowerCAmelCase							       =				0.95
						elif size == "m48":
												__lowerCAmelCase							       =				[8, 8, 2_4, 8]
												__lowerCAmelCase							       =				[9_6, 1_9_2, 3_8_4, 7_6_8]
												__lowerCAmelCase							       =				4.0
												__lowerCAmelCase							       =				1E-6
												__lowerCAmelCase							       =				0.95
						else:
												raise ValueError(F"""Size {size} not supported""")

						# load image processor
						__lowerCAmelCase							       =				PoolFormerImageProcessor(crop_pct=lowerCamelCase)

						# Prepare image
						__lowerCAmelCase							       =				prepare_img()
						__lowerCAmelCase							       =				image_processor(images=lowerCamelCase,				return_tensors='''pt''').pixel_values

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

						# load original state dict
						__lowerCAmelCase							       =				torch.load(lowerCamelCase,				map_location=torch.device('''cpu'''))

						# rename keys
						__lowerCAmelCase							       =				rename_keys(lowerCamelCase)

						# create HuggingFace model and load state dict
						__lowerCAmelCase							       =				PoolFormerForImageClassification(lowerCamelCase)
						model.load_state_dict(lowerCamelCase)
						model.eval()

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

						# forward pass
						__lowerCAmelCase							       =				model(lowerCamelCase)
						__lowerCAmelCase							       =				outputs.logits

						# define expected logit slices for different models
						if size == "s12":
												__lowerCAmelCase							       =				torch.tensor([-0.30_45, -0.67_58, -0.48_69])
						elif size == "s24":
												__lowerCAmelCase							       =				torch.tensor([0.44_02, -0.13_74, -0.80_45])
						elif size == "s36":
												__lowerCAmelCase							       =				torch.tensor([-0.60_80, -0.51_33, -0.58_98])
						elif size == "m36":
												__lowerCAmelCase							       =				torch.tensor([0.39_52, 0.22_63, -1.26_68])
						elif size == "m48":
												__lowerCAmelCase							       =				torch.tensor([0.11_67, -0.06_56, -0.34_23])
						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__":
	_UpperCAmelCase				: 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."""
	)
	_UpperCAmelCase				: Optional[int]							    =				parser.parse_args()
	convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)





'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 							__magic_name__(						lowerCamelCase,				lowerCamelCase):
						__lowerCAmelCase							       =				BeautifulSoup(requests.get(lowerCamelCase,				params=lowerCamelCase).content,				'''html.parser''')
						__lowerCAmelCase							       =				soup.find('''div''',				attrs={'''class''': '''gs_ri'''})
						__lowerCAmelCase							       =				div.find('''div''',				attrs={'''class''': '''gs_fl'''}).find_all('''a''')
						return anchors[2].get_text()


if __name__ == "__main__":
	_UpperCAmelCase				: Union[str, Any]							    =				{
	    """title""": (
	        """Precisely geometry controlled microsupercapacitors for ultrahigh areal """
	        """capacitance, volumetric capacitance, and energy density"""
	    ),
	    """journal""": """Chem. Mater.""",
	    """volume""": 3_0,
	    """pages""": """3979-3990""",
	    """year""": 2_0_1_8,
	    """hl""": """en""",
	}
	print(get_citation("""https://scholar.google.com/scholar_lookup""", params=params))

