

'''simple docstring'''






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__											=	logging.get_logger(__name__)


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = '''huggingface/label-files'''
 __lowercase							  = '''imagenet-1k-id2label.json'''
 __lowercase							  = json.load(open(hf_hub_download(A__     ,				A__     ,				repo_type='''dataset'''      )     ,				'''r'''      )      )
 __lowercase							  = {int(A__      ): v for k, v in idalabel.items()}
 __lowercase							  = {v: k for k, v in idalabel.items()}

 __lowercase							  = '''std_conv''' if '''bit''' in model_name else False

 # note that when using BiT as backbone for ViT-hybrid checkpoints,
 # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
 # config.conv_layer = "std_conv_same"
 __lowercase							  = BitConfig(
     conv_layer=A__     ,				num_labels=1000     ,				idalabel=A__     ,				labelaid=A__     ,				)

 return config


def 		_A     (    A__      ):






 """simple docstring"""



 if "stem.conv" in name:
  __lowercase							  = name.replace('''stem.conv'''     ,				'''bit.embedder.convolution'''      )
 if "blocks" in name:
  __lowercase							  = name.replace('''blocks'''     ,				'''layers'''      )
 if "head.fc" in name:
  __lowercase							  = name.replace('''head.fc'''     ,				'''classifier.1'''      )
 if name.startswith('''norm'''      ):
  __lowercase							  = '''bit.''' + name
 if "bit" not in name and "classifier" not in name:
  __lowercase							  = '''bit.encoder.''' + name

 return name


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __lowercase							  = Image.open(requests.get(A__     ,				stream=A__      ).raw      )
 return im


@torch.no_grad()
def 		_A     (    A__     ,				A__     ,				A__=False      ):






 """simple docstring"""



 __lowercase							  = get_config(A__      )

 # load original model from timm
 __lowercase							  = create_model(A__     ,				pretrained=A__      )
 timm_model.eval()

 # load state_dict of original model
 __lowercase							  = timm_model.state_dict()
 for key in state_dict.copy().keys():
  __lowercase							  = state_dict.pop(A__      )
  __lowercase							  = val.squeeze() if '''head''' in key else val

 # load HuggingFace model
 __lowercase							  = BitForImageClassification(A__      )
 model.eval()
 model.load_state_dict(A__      )

 # create image processor
 __lowercase							  = create_transform(**resolve_data_config({}     ,				model=A__      )      )
 __lowercase							  = transform.transforms

 __lowercase							  = {
     '''bilinear''': PILImageResampling.BILINEAR,
     '''bicubic''': PILImageResampling.BICUBIC,
     '''nearest''': PILImageResampling.NEAREST,
 }

 __lowercase							  = BitImageProcessor(
     do_resize=A__     ,				size={'''shortest_edge''': timm_transforms[0].size}     ,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]     ,				do_center_crop=A__     ,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}     ,				do_normalize=A__     ,				image_mean=timm_transforms[-1].mean.tolist()     ,				image_std=timm_transforms[-1].std.tolist()     ,				)

 __lowercase							  = prepare_img()
 __lowercase							  = transform(A__      ).unsqueeze(0      )
 __lowercase							  = processor(A__     ,				return_tensors='''pt'''      ).pixel_values

 # verify pixel values
 assert torch.allclose(A__     ,				A__      )

 # verify logits
 with torch.no_grad():
  __lowercase							  = model(A__      )
  __lowercase							  = outputs.logits

 print('''Logits:'''     ,				logits[0, :3]      )
 print('''Predicted class:'''     ,				model.config.idalabel[logits.argmax(-1      ).item()]      )
 __lowercase							  = timm_model(A__      )
 assert timm_logits.shape == outputs.logits.shape
 assert torch.allclose(A__     ,				outputs.logits     ,				atol=1e-3      )
 print('''Looks ok!'''      )

 if pytorch_dump_folder_path is not None:
  Path(A__      ).mkdir(exist_ok=A__      )
  print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"      )
  model.save_pretrained(A__      )
  processor.save_pretrained(A__      )

 if push_to_hub:
  print(F"Pushing model {model_name} and processor to the hub"      )
  model.push_to_hub(F"ybelkada/{model_name}"      )
  processor.push_to_hub(F"ybelkada/{model_name}"      )


if __name__ == "__main__":
 lowerCAmelCase__											=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''resnetv2_50x1_bitm''',
     type=str,
     help='''Name of the BiT timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument(
     '''--push_to_hub''',
     action='''store_true''',
     help='''Whether to push the model to the hub.''',
 )

 lowerCAmelCase__											=	parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""






import requests
from bsa import BeautifulSoup



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


if __name__ == "__main__":
						lowerCAmelCase__  :  Optional[int]						     =	{
						    'title': (
						        'Precisely geometry controlled microsupercapacitors for ultrahigh areal '
						        'capacitance, volumetric capacitance, and energy density'
						    ),
						    'journal': 'Chem. Mater.',
						    'volume': 30,
						    'pages': '3979-3990',
						    'year': 2_018,
						    'hl': 'en',
						}
						print(get_citation('https://scholar.google.com/scholar_lookup', params=params))



