
"""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__   :					str							=   logging.get_logger(__name__)




def   _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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

  _lowercase:			List[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"
  _lowercase:			Any       =     BitConfig(
      conv_layer=_UpperCamelCase ,				num_labels=1_000 ,				idalabel=_UpperCamelCase ,				labelaid=_UpperCamelCase ,				)

  return config




def   _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



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

  return name




def   _lowerCAmelCase		(		):






  """simple docstring"""



  _lowercase:			List[Any]       =     '''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _lowercase:			Dict       =     Image.open(requests.get(_UpperCamelCase ,				stream=_UpperCamelCase						).raw						)
  return im




@torch.no_grad()
def   _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=False						):






  """simple docstring"""



  _lowercase:			Tuple       =     get_config(_UpperCamelCase						)

  # load original model from timm
  _lowercase:			Optional[int]       =     create_model(_UpperCamelCase ,				pretrained=_UpperCamelCase						)
  timm_model.eval()

  # load state_dict of original model
  _lowercase:			Union[str, Any]       =     timm_model.state_dict()
  for key in state_dict.copy().keys():
    _lowercase:			Dict       =     state_dict.pop(_UpperCamelCase						)
    _lowercase:			Any       =     val.squeeze() if '''head''' in key else val

  # load HuggingFace model
  _lowercase:			List[str]       =     BitForImageClassification(_UpperCamelCase						)
  model.eval()
  model.load_state_dict(_UpperCamelCase						)

  # create image processor
  _lowercase:			Dict       =     create_transform(**resolve_data_config({} ,				model=_UpperCamelCase						)						)
  _lowercase:			str       =     transform.transforms

  _lowercase:			Optional[Any]       =     {
      '''bilinear''': PILImageResampling.BILINEAR,
      '''bicubic''': PILImageResampling.BICUBIC,
      '''nearest''': PILImageResampling.NEAREST,
  }

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

  _lowercase:			Any       =     prepare_img()
  _lowercase:			Optional[Any]       =     transform(_UpperCamelCase						).unsqueeze(0						)
  _lowercase:			Dict       =     processor(_UpperCamelCase ,				return_tensors='''pt'''						).pixel_values

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

  # verify logits
  with torch.no_grad():
    _lowercase:			Dict       =     model(_UpperCamelCase						)
    _lowercase:			List[str]       =     outputs.logits

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

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

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


"""simple docstring"""




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  if len(_UpperCamelCase						) == 1:
    return True
  _lowercase:			Any       =     series[1] - series[0]
  for index in range(len(_UpperCamelCase						) - 1						):
    if series[index + 1] - series[index] != common_diff:
      return False
  return True




def  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  if not isinstance(_UpperCamelCase ,				_UpperCamelCase						):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''						)
  if len(_UpperCamelCase						) == 0:
    raise ValueError('''Input list must be a non empty list'''						)
  _lowercase:			Tuple       =     0
  for val in series:
    answer += val
  return answer / len(_UpperCamelCase						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
