





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				:	Any           =       logging.get_logger(__name__)



def  UpperCamelCase__     (   _A:    Optional[int]				):




      '''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=1000							,    idalabel=__snake_case							,    labelaid=__snake_case							,    )

      return config



def  UpperCamelCase__     (   _A:    Dict				):




      '''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__     (   ):




      '''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__     (   _A:    Optional[int]							,    _A:    Tuple							,    _A:    List[Any]=False				):




      '''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				:	int           =       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"""






from abc import ABC, abstractmethod
from argparse import ArgumentParser



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@staticmethod
	@abstractmethod
	def 				_SCREAMING_SNAKE_CASE  (	_SCREAMING_SNAKE_CASE:						ArgumentParser)							->			Optional[int]:

				"""simple docstring"""

				raise NotImplementedError()







	@abstractmethod
	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			Dict:

				"""simple docstring"""

				raise NotImplementedError()