'''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()
_A			:      List[str]					      =				logging.get_logger(__name__)







def      UpperCamelCase_						(					snake_case_       :       int      )					->     Optional[Any]:






 '''simple docstring'''

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

 __lowerCAmelCase											=						"""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"
 __lowerCAmelCase											=						BitConfig(
     conv_layer=snake_case_						,		num_labels=10_00						,		idalabel=snake_case_						,		labelaid=snake_case_						,		)

 return config







def      UpperCamelCase_						(					snake_case_       :       Tuple      )					->     Optional[Any]:






 '''simple docstring'''

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

 return name







def      UpperCamelCase_						(					)					->     List[Any]:






 '''simple docstring'''

 __lowerCAmelCase											=						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 __lowerCAmelCase											=						Image.open(requests.get(snake_case_						,		stream=snake_case_      ).raw      )
 return im







@torch.no_grad()
def      UpperCamelCase_						(					snake_case_       :       Optional[Any]						,		snake_case_       :       Union[str, Any]						,		snake_case_       :       List[Any]=False      )					->     Dict:






 '''simple docstring'''

 __lowerCAmelCase											=						get_config(snake_case_      )

 # load original model from timm
 __lowerCAmelCase											=						create_model(snake_case_						,		pretrained=snake_case_      )
 timm_model.eval()

 # load state_dict of original model
 __lowerCAmelCase											=						timm_model.state_dict()
 for key in state_dict.copy().keys():
  __lowerCAmelCase											=						state_dict.pop(snake_case_      )
  __lowerCAmelCase											=						val.squeeze() if """head""" in key else val

 # load HuggingFace model
 __lowerCAmelCase											=						BitForImageClassification(snake_case_      )
 model.eval()
 model.load_state_dict(snake_case_      )

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

 __lowerCAmelCase											=						{
     """bilinear""": PILImageResampling.BILINEAR,
     """bicubic""": PILImageResampling.BICUBIC,
     """nearest""": PILImageResampling.NEAREST,
 }

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

 __lowerCAmelCase											=						prepare_img()
 __lowerCAmelCase											=						transform(snake_case_      ).unsqueeze(0      )
 __lowerCAmelCase											=						processor(snake_case_						,		return_tensors="""pt"""      ).pixel_values

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

 # verify logits
 with torch.no_grad():
  __lowerCAmelCase											=						model(snake_case_      )
  __lowerCAmelCase											=						outputs.logits

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

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

 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__":
 _A			:      List[Any]					      =				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.''',
 )

 _A			:      int					      =				parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''

def      UpperCamelCase_						(					snake_case_       :       Union[str, Any]=2_81_23      )					->     str:






 '''simple docstring'''

 __lowerCAmelCase											=						[1] * (limit + 1)

 for i in range(2						,		int(limit**0.5      ) + 1      ):
  sum_divs[i * i] += i
  for k in range(i + 1						,		limit // i + 1      ):
   sum_divs[k * i] += k + i

 __lowerCAmelCase											=						set()
 __lowerCAmelCase											=						0

 for n in range(1						,		limit + 1      ):
  if sum_divs[n] > n:
   abundants.add(snake_case_      )

  if not any((n - a in abundants) for a in abundants      ):
   res += n

 return res


if __name__ == "__main__":
 print(solution())






