


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


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	int   	=		'huggingface/label-files'
       UpperCamelCase    :	Tuple   	=		'imagenet-1k-id2label.json'
       UpperCamelCase    :	Dict   	=		json.load(open(hf_hub_download(__lowercase							,			__lowercase							,			repo_type='''dataset'''       )							,			'''r'''       )       )
       UpperCamelCase    :	Dict   	=		{int(__lowercase       ): v for k, v in idalabel.items()}
       UpperCamelCase    :	Union[str, Any]   	=		{v: k for k, v in idalabel.items()}

       UpperCamelCase    :	Dict   	=		'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"
       UpperCamelCase    :	Union[str, Any]   	=		BitConfig(
           conv_layer=__lowercase							,			num_labels=1_0_0_0							,			idalabel=__lowercase							,			labelaid=__lowercase							,			)

       return config


def      a	(							SCREAMING_SNAKE_CASE_      :		Dict       ):






       """simple docstring"""





       if "stem.conv" in name:
              UpperCamelCase    :	str   	=		name.replace('''stem.conv'''							,			'''bit.embedder.convolution'''       )
       if "blocks" in name:
              UpperCamelCase    :	Optional[Any]   	=		name.replace('''blocks'''							,			'''layers'''       )
       if "head.fc" in name:
              UpperCamelCase    :	Any   	=		name.replace('''head.fc'''							,			'''classifier.1'''       )
       if name.startswith('''norm'''       ):
              UpperCamelCase    :	Tuple   	=		'bit.' + name
       if "bit" not in name and "classifier" not in name:
              UpperCamelCase    :	List[Any]   	=		'bit.encoder.' + name

       return name


def      a	(							):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		'http://images.cocodataset.org/val2017/000000039769.jpg'
       UpperCamelCase    :	str   	=		Image.open(requests.get(__lowercase							,			stream=__lowercase       ).raw       )
       return im


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		List[Any]							,			SCREAMING_SNAKE_CASE_      :		str							,			SCREAMING_SNAKE_CASE_      :		List[str]=False       ):






       """simple docstring"""





       UpperCamelCase    :	Any   	=		get_config(__lowercase       )

       # load original model from timm
       UpperCamelCase    :	List[str]   	=		create_model(__lowercase							,			pretrained=__lowercase       )
       timm_model.eval()

       # load state_dict of original model
       UpperCamelCase    :	List[str]   	=		timm_model.state_dict()
       for key in state_dict.copy().keys():
              UpperCamelCase    :	List[str]   	=		state_dict.pop(__lowercase       )
              UpperCamelCase    :	Dict   	=		val.squeeze() if 'head' in key else val

       # load HuggingFace model
       UpperCamelCase    :	Union[str, Any]   	=		BitForImageClassification(__lowercase       )
       model.eval()
       model.load_state_dict(__lowercase       )

       # create image processor
       UpperCamelCase    :	List[str]   	=		create_transform(**resolve_data_config({}							,			model=__lowercase       )       )
       UpperCamelCase    :	Dict   	=		transform.transforms

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

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

       UpperCamelCase    :	Tuple   	=		prepare_img()
       UpperCamelCase    :	List[Any]   	=		transform(__lowercase       ).unsqueeze(0       )
       UpperCamelCase    :	str   	=		processor(__lowercase							,			return_tensors='''pt'''       ).pixel_values

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

       # verify logits
       with torch.no_grad():
              UpperCamelCase    :	Optional[Any]   	=		model(__lowercase       )
              UpperCamelCase    :	Optional[int]   	=		outputs.logits

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

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

       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__":
  __UpperCAmelCase : 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.",
  )

  __UpperCAmelCase : Union[str, Any] 			=				parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





def      a	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		int       ):






       """simple docstring"""





       if a < 0 or b < 0:
              raise ValueError('''the value of both inputs must be positive'''       )
       UpperCamelCase    :	int   	=		str(bin(SCREAMING_SNAKE_CASE_       )       )[2:]  # remove the leading "0b"
       UpperCamelCase    :	List[str]   	=		str(bin(SCREAMING_SNAKE_CASE_       )       )[2:]
       UpperCamelCase    :	Tuple   	=		max(len(SCREAMING_SNAKE_CASE_       )							,			len(SCREAMING_SNAKE_CASE_       )       )
       return "0b" + "".join(
           str(int('''1''' in (char_a, char_b)       )       )
           for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_       )							,			b_binary.zfill(SCREAMING_SNAKE_CASE_       )       )       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

