



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()
__lowerCamelCase         =			logging.get_logger(__name__)







def    _snake_case (       __snake_case    )     ->     Tuple:






      '''simple docstring'''







      UpperCAmelCase_  :	Dict				      =		"huggingface/label-files"
      UpperCAmelCase_  :	Tuple				      =		"imagenet-1k-id2label.json"
      UpperCAmelCase_  :	int				      =		json.load(open(hf_hub_download(__snake_case					,  __snake_case					,  repo_type="dataset"    )					,  "r"    )    )
      UpperCAmelCase_  :	str				      =		{int(__snake_case    ): v for k, v in idalabel.items()}
      UpperCAmelCase_  :	Optional[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=__snake_case					,  num_labels=1_0_0_0					,  idalabel=__snake_case					,  labelaid=__snake_case					,  )

      return config







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







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

      return name







def    _snake_case (       )     ->     str:






      '''simple docstring'''







      UpperCAmelCase_  :	Union[str, Any]				      =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      UpperCAmelCase_  :	Union[str, Any]				      =		Image.open(requests.get(__snake_case					,  stream=__snake_case    ).raw    )
      return im







@torch.no_grad()
def    _snake_case (       __snake_case					,  __snake_case					,  __snake_case=False    )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	List[str]				      =		get_config(__snake_case    )

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

      # load state_dict of original model
      UpperCAmelCase_  :	Optional[Any]				      =		timm_model.state_dict()
      for key in state_dict.copy().keys():
            UpperCAmelCase_  :	int				      =		state_dict.pop(__snake_case    )
            UpperCAmelCase_  :	Any				      =		val.squeeze() if "head" in key else val

      # load HuggingFace model
      UpperCAmelCase_  :	List[Any]				      =		BitForImageClassification(__snake_case    )
      model.eval()
      model.load_state_dict(__snake_case    )

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

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

      UpperCAmelCase_  :	Union[str, Any]				      =		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()					,  )

      UpperCAmelCase_  :	Any				      =		prepare_img()
      UpperCAmelCase_  :	int				      =		transform(__snake_case    ).unsqueeze(0    )
      UpperCAmelCase_  :	List[Any]				      =		processor(__snake_case					,  return_tensors="pt"    ).pixel_values

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

      # verify logits
      with torch.no_grad():
            UpperCAmelCase_  :	Any				      =		model(__snake_case    )
            UpperCAmelCase_  :	Optional[Any]				      =		outputs.logits

      print("Logits:"					,  logits[0, :3]    )
      print("Predicted class:"					,  model.config.idalabel[logits.argmax(-1    ).item()]    )
      UpperCAmelCase_  :	Dict				      =		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__":
   __lowerCamelCase         =			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.''',
   )

   __lowerCamelCase         =			parser.parse_args()
   convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





import logging

from transformers import PretrainedConfig


__lowerCamelCase         =			logging.getLogger(__name__)


__lowerCamelCase         =			{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class   snake_case_ (lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""bertabs"""



       def __init__(     self     ,lowercase=30522     ,lowercase=512     ,lowercase=6     ,lowercase=512     ,lowercase=8     ,lowercase=512     ,lowercase=0.2     ,lowercase=6     ,lowercase=768     ,lowercase=8     ,lowercase=2048     ,lowercase=0.2     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	Any				      =		vocab_size
             UpperCAmelCase_  :	int				      =		max_pos

             UpperCAmelCase_  :	List[str]				      =		enc_layers
             UpperCAmelCase_  :	List[str]				      =		enc_hidden_size
             UpperCAmelCase_  :	Any				      =		enc_heads
             UpperCAmelCase_  :	Any				      =		enc_ff_size
             UpperCAmelCase_  :	Optional[int]				      =		enc_dropout

             UpperCAmelCase_  :	List[str]				      =		dec_layers
             UpperCAmelCase_  :	List[Any]				      =		dec_hidden_size
             UpperCAmelCase_  :	List[str]				      =		dec_heads
             UpperCAmelCase_  :	List[Any]				      =		dec_ff_size
             UpperCAmelCase_  :	List[str]				      =		dec_dropout
