



"""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()
lowercase__		:				Union[str, Any]          =	logging.get_logger(__name__)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				Optional[int]						)						->   str:

  """simple docstring"""




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

  lowerCAmelCase_   :				Union[str, Any]								= '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_   :				Dict								= BitConfig(
      conv_layer=lowerCAmelCase__    ,   num_labels=1000    ,   idalabel=lowerCAmelCase__    ,   labelaid=lowerCAmelCase__    ,   )

  return config

def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[str]						)						->   Tuple:

  """simple docstring"""




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

  return name

def 				UpperCamelCase_    (    )						->   Dict:

  """simple docstring"""




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

@torch.no_grad()
def 				UpperCamelCase_    (    lowerCAmelCase__      :				List[Any]    ,   lowerCAmelCase__      :				Tuple    ,   lowerCAmelCase__      :				Tuple=False						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				Dict								= get_config(lowerCAmelCase__						)

  # load original model from timm
  lowerCAmelCase_   :				List[Any]								= create_model(lowerCAmelCase__    ,   pretrained=lowerCAmelCase__						)
  timm_model.eval()

  # load state_dict of original model
  lowerCAmelCase_   :				Optional[int]								= timm_model.state_dict()
  for key in state_dict.copy().keys():
    lowerCAmelCase_   :				Tuple								= state_dict.pop(lowerCAmelCase__						)
    lowerCAmelCase_   :				str								= val.squeeze() if 'head' in key else val

  # load HuggingFace model
  lowerCAmelCase_   :				Optional[Any]								= BitForImageClassification(lowerCAmelCase__						)
  model.eval()
  model.load_state_dict(lowerCAmelCase__						)

  # create image processor
  lowerCAmelCase_   :				Union[str, Any]								= create_transform(**resolve_data_config({}    ,   model=lowerCAmelCase__						)						)
  lowerCAmelCase_   :				Any								= transform.transforms

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

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

  lowerCAmelCase_   :				Union[str, Any]								= prepare_img()
  lowerCAmelCase_   :				Optional[int]								= transform(lowerCAmelCase__						).unsqueeze(0						)
  lowerCAmelCase_   :				Union[str, Any]								= processor(lowerCAmelCase__    ,   return_tensors='pt'						).pixel_values

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

  # verify logits
  with torch.no_grad():
    lowerCAmelCase_   :				str								= model(lowerCAmelCase__						)
    lowerCAmelCase_   :				Any								= outputs.logits

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

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

  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__":
     lowercase__		:				Optional[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.""",
     )

     lowercase__		:				Dict          =	parser.parse_args()
     convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = 100						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= (n * (n + 1) // 2) ** 2
  lowerCAmelCase_   :				Optional[int]								= n * (n + 1) * (2 * n + 1) // 6
  return sum_cubes - sum_squares


if __name__ == "__main__":
     print(f'{solution() = }')





