



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    )     ->     List[Any]:






      '''simple docstring'''







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

      UpperCAmelCase_  :	str				      =		'''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_  :	Optional[int]				      =		BitConfig(
          conv_layer=_lowercase					,  num_labels=1_0_0_0					,  idalabel=_lowercase					,  labelaid=_lowercase					,  )

      return config







def    _snake_case (       __snake_case    )     ->     Optional[int]:






      '''simple docstring'''







      if "stem.conv" in name:
            UpperCAmelCase_  :	Optional[int]				      =		name.replace("stem.conv"					,  "bit.embedder.convolution"    )
      if "blocks" in name:
            UpperCAmelCase_  :	Tuple				      =		name.replace("blocks"					,  "layers"    )
      if "head.fc" in name:
            UpperCAmelCase_  :	Union[str, Any]				      =		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_  :	Union[str, Any]				      =		'''bit.encoder.''' + name

      return name







def    _snake_case (       )     ->     str:






      '''simple docstring'''







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







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






      '''simple docstring'''







      UpperCAmelCase_  :	List[Any]				      =		get_config(_lowercase    )

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

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

      # load HuggingFace model
      UpperCAmelCase_  :	str				      =		BitForImageClassification(_lowercase    )
      model.eval()
      model.load_state_dict(_lowercase    )

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

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

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

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

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

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

      print("Logits:"					,  logits[0, :3]    )
      print("Predicted class:"					,  model.config.idalabel[logits.argmax(-1    ).item()]    )
      UpperCAmelCase_  :	List[Any]				      =		timm_model(_lowercase    )
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowercase					,  outputs.logits					,  atol=1E-3    )
      print("Looks ok!"    )

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

      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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase         =			logging.get_logger(__name__)

__lowerCamelCase         =			{
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}


class   snake_case_ (lowercase__ , lowercase__  ):




       """simple docstring"""






       _lowerCamelCase			    =				"""focalnet"""



       def __init__(     self     ,lowercase=224     ,lowercase=4     ,lowercase=3     ,lowercase=96     ,lowercase=False     ,lowercase=[192, 384, 768, 768]     ,lowercase=[2, 2, 6, 2]     ,lowercase=[2, 2, 2, 2]     ,lowercase=[3, 3, 3, 3]     ,lowercase="gelu"     ,lowercase=4.0     ,lowercase=0.0     ,lowercase=0.1     ,lowercase=False     ,lowercase=1E-4     ,lowercase=False     ,lowercase=False     ,lowercase=False     ,lowercase=0.02     ,lowercase=1E-5     ,lowercase=32     ,lowercase=None     ,lowercase=None     ,**lowercase     ,):

             """simple docstring"""





             super().__init__(**lowercase)

             UpperCAmelCase_  :	int				      =		image_size
             UpperCAmelCase_  :	Optional[Any]				      =		patch_size
             UpperCAmelCase_  :	Dict				      =		num_channels
             UpperCAmelCase_  :	Dict				      =		embed_dim
             UpperCAmelCase_  :	Optional[int]				      =		use_conv_embed
             UpperCAmelCase_  :	int				      =		hidden_sizes
             UpperCAmelCase_  :	Optional[int]				      =		depths
             UpperCAmelCase_  :	Optional[Any]				      =		focal_levels
             UpperCAmelCase_  :	Tuple				      =		focal_windows
             UpperCAmelCase_  :	int				      =		hidden_act
             UpperCAmelCase_  :	Any				      =		mlp_ratio
             UpperCAmelCase_  :	List[Any]				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Tuple				      =		drop_path_rate
             UpperCAmelCase_  :	List[str]				      =		use_layerscale
             UpperCAmelCase_  :	List[str]				      =		layerscale_value
             UpperCAmelCase_  :	List[str]				      =		use_post_layernorm
             UpperCAmelCase_  :	Dict				      =		use_post_layernorm_in_modulation
             UpperCAmelCase_  :	int				      =		normalize_modulator
             UpperCAmelCase_  :	Dict				      =		initializer_range
             UpperCAmelCase_  :	str				      =		layer_norm_eps
             UpperCAmelCase_  :	Union[str, Any]				      =		encoder_stride
             UpperCAmelCase_  :	str				      =		["stem"] + [F"""stage{idx}""" for idx in range(1     ,len(self.depths) + 1)]
             UpperCAmelCase_      ,      UpperCAmelCase_  :	Any				      =		get_aligned_output_features_output_indices(
                 out_features=lowercase     ,out_indices=lowercase     ,stage_names=self.stage_names)
