

"""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					:Dict    = logging.get_logger(__name__)




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

      SCREAMING_SNAKE_CASE__   :      Optional[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"
      SCREAMING_SNAKE_CASE__   :      Dict      		=	BitConfig(
          conv_layer=__lowerCAmelCase  ,					num_labels=1000  ,					idalabel=__lowerCAmelCase  ,					labelaid=__lowerCAmelCase  ,					)

      return config




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

      return name




def  _lowercase					(    )       ->				List[str]:
      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      SCREAMING_SNAKE_CASE__   :      Tuple      		=	Image.open(requests.get(__lowerCAmelCase  ,					stream=__lowerCAmelCase							).raw							)
      return im




@torch.no_grad()
def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase=False							)       ->				Optional[int]:
      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	get_config(__lowerCAmelCase							)

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

      # load state_dict of original model
      SCREAMING_SNAKE_CASE__   :      int      		=	timm_model.state_dict()
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__   :      int      		=	state_dict.pop(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	val.squeeze() if """head""" in key else val

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__   :      Any      		=	BitForImageClassification(__lowerCAmelCase							)
      model.eval()
      model.load_state_dict(__lowerCAmelCase							)

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

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	{
          """bilinear""": PILImageResampling.BILINEAR,
          """bicubic""": PILImageResampling.BICUBIC,
          """nearest""": PILImageResampling.NEAREST,
      }

      SCREAMING_SNAKE_CASE__   :      Tuple      		=	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()  ,					)

      SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	prepare_img()
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	transform(__lowerCAmelCase							).unsqueeze(0							)
      SCREAMING_SNAKE_CASE__   :      Any      		=	processor(__lowerCAmelCase  ,					return_tensors="""pt"""							).pixel_values

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

      # verify logits
      with torch.no_grad():
            SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	model(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	outputs.logits

      print("""Logits:"""  ,					logits[0, :3]							)
      print("""Predicted class:"""  ,					model.config.idalabel[logits.argmax(-1							).item()]							)
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	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__":
  a					: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					:List[Any]    = parser.parse_args()
  convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""


class      __a							:






     '''simple docstring'''

     def __init__(						self	,    _a	,    _a	,    _a  )  ->      str:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Any      		=	name
           SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	value
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	weight

     def __repr__(						self  )  ->      List[Any]:


           """simple docstring"""


           return f'''{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'''

     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value

     def 					_a			(						self  )  ->      int:


           """simple docstring"""


           return self.name

     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


           return self.weight





     def 					_a			(						self  )  ->      Dict:


           """simple docstring"""


           return self.value / self.weight





def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Dict:
      SCREAMING_SNAKE_CASE__   :      Any      		=	[]
      for i in range(len(__lowerCAmelCase							)							):
            menu.append(Things(name[i]  ,					value[i]  ,					weight[i]							)							)
      return menu




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Union[str, Any]:
      SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	sorted(__lowerCAmelCase  ,					key=__lowerCAmelCase  ,					reverse=__lowerCAmelCase							)
      SCREAMING_SNAKE_CASE__   :      str      		=	[]
      SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	0.0, 0.0
      for i in range(len(__lowerCAmelCase							)							):
            if (total_cost + items_copy[i].get_weight()) <= max_cost:
                  result.append(items_copy[i]							)
                  total_cost += items_copy[i].get_weight()
                  total_value += items_copy[i].get_value()
      return (result, total_value)




def  _lowercase					(    )       ->				List[str]:
      pass


if __name__ == "__main__":
  import doctest

  doctest.testmod()




