





"""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()
lowerCamelCase__						= logging.get_logger(__name__)




def        lowercase__     (       lowercase_      )       ->		Any:




      """simple docstring"""

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

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

      return config




def        lowercase__     (       lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

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

      return name




def        lowercase__     (       )       ->		List[str]:




      """simple docstring"""

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




@torch.no_grad()
def        lowercase__     (       lowercase_		,lowercase_		,lowercase_=False      )       ->		Any:




      """simple docstring"""

      _UpperCamelCase   :						int    =							get_config(lowercase_      )

      # load original model from timm
      _UpperCamelCase   :						int    =							create_model(lowercase_		,pretrained=lowercase_      )
      timm_model.eval()

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

      # load HuggingFace model
      _UpperCamelCase   :						Any    =							BitForImageClassification(lowercase_      )
      model.eval()
      model.load_state_dict(lowercase_      )

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

      _UpperCamelCase   :						int    =							{
          "bilinear": PILImageResampling.BILINEAR,
          "bicubic": PILImageResampling.BICUBIC,
          "nearest": PILImageResampling.NEAREST,
      }

      _UpperCamelCase   :						Optional[int]    =							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   :						Optional[Any]    =							transform(lowercase_      ).unsqueeze(0      )
      _UpperCamelCase   :						List[Any]    =							processor(lowercase_		,return_tensors="pt"      ).pixel_values

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

      # verify logits
      with torch.no_grad():
            _UpperCamelCase   :						Optional[int]    =							model(lowercase_      )
            _UpperCamelCase   :						str    =							outputs.logits

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







"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCamelCase__						= {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ResNetForImageClassification",
      "ResNetModel",
      "ResNetPreTrainedModel",
      "ResNetBackbone",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFResNetForImageClassification",
      "TFResNetModel",
      "TFResNetPreTrainedModel",
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase__						= [
      "FlaxResNetForImageClassification",
      "FlaxResNetModel",
      "FlaxResNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_resnet import (
        RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        ResNetBackbone,
        ResNetForImageClassification,
        ResNetModel,
        ResNetPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_resnet import (
        TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFResNetForImageClassification,
        TFResNetModel,
        TFResNetPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
  import sys

  lowerCamelCase__						= _LazyModule(__name__, globals()["__file__"], _import_structure)
