



'''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: Tuple							=						logging.get_logger(__name__)
def 				lowerCamelCase__	(						_A    ):
  a						: str					       =					'huggingface/label-files'
  a						: int					       =					'imagenet-1k-id2label.json'
  a						: str					       =					json.load(open(hf_hub_download(_A			,      _A			,      repo_type='dataset'    )			,      'r'    )    )
  a						: int					       =					{int(_A    ): v for k, v in idalabel.items()}
  a						: Optional[int]					       =					{v: k for k, v in idalabel.items()}

  a						: List[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"
  a						: List[str]					       =					BitConfig(
      conv_layer=_A			,      num_labels=1000			,      idalabel=_A			,      labelaid=_A			,      )

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

  return name
def 				lowerCamelCase__	(						):
  a						: int					       =					'http://images.cocodataset.org/val2017/000000039769.jpg'
  a						: Optional[Any]					       =					Image.open(requests.get(_A			,      stream=_A    ).raw    )
  return im
@torch.no_grad()
def 				lowerCamelCase__	(						_A			,      _A			,      _A=False    ):
  a						: Optional[Any]					       =					get_config(_A    )

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

  # load state_dict of original model
  a						: Optional[Any]					       =					timm_model.state_dict()
  for key in state_dict.copy().keys():
    a						: List[Any]					       =					state_dict.pop(_A    )
    a						: Tuple					       =					val.squeeze() if 'head' in key else val

  # load HuggingFace model
  a						: Optional[Any]					       =					BitForImageClassification(_A    )
  model.eval()
  model.load_state_dict(_A    )

  # create image processor
  a						: List[str]					       =					create_transform(**resolve_data_config({}			,      model=_A    )    )
  a						: List[str]					       =					transform.transforms

  a						: List[Any]					       =					{
      'bilinear': PILImageResampling.BILINEAR,
      'bicubic': PILImageResampling.BICUBIC,
      'nearest': PILImageResampling.NEAREST,
  }

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

  a						: Dict					       =					prepare_img()
  a						: Optional[Any]					       =					transform(_A    ).unsqueeze(0    )
  a						: str					       =					processor(_A			,      return_tensors='pt'    ).pixel_values

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

  # verify logits
  with torch.no_grad():
    a						: Union[str, Any]					       =					model(_A    )
    a						: str					       =					outputs.logits

  print('Logits:'			,      logits[0, :3]    )
  print('Predicted class:'			,      model.config.idalabel[logits.argmax(-1    ).item()]    )
  a						: Dict					       =					timm_model(_A    )
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(_A			,      outputs.logits			,      atol=1E-3    )
  print('Looks ok!'    )

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

  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: Union[str, Any]							=						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: Optional[Any]							=						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_tokenizers_available, is_torch_available


lowerCAmelCase: Optional[int]							=						{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     lowerCAmelCase: int							=						[
         'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'GraphormerForGraphClassification',
         'GraphormerModel',
         'GraphormerPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_graphormer import (
              GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              GraphormerForGraphClassification,
              GraphormerModel,
              GraphormerPreTrainedModel,
          )


else:
     import sys

     lowerCAmelCase: str							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)