

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

       A_		   = """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_		   = BitConfig(
           conv_layer=UpperCAmelCase__,    num_labels=10_00,    idalabel=UpperCAmelCase__,    labelaid=UpperCAmelCase__,    )

       return config
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     Optional[Any]:
       if "stem.conv" in name:
              A_		   = name.replace("""stem.conv""",    """bit.embedder.convolution"""			)
       if "blocks" in name:
              A_		   = name.replace("""blocks""",    """layers"""			)
       if "head.fc" in name:
              A_		   = name.replace("""head.fc""",    """classifier.1"""			)
       if name.startswith("""norm"""			):
              A_		   = """bit.""" + name
       if "bit" not in name and "classifier" not in name:
              A_		   = """bit.encoder.""" + name

       return name
def  UpperCAmelCase__      ( )     ->     Dict:
       A_		   = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_		   = Image.open(requests.get(UpperCAmelCase__,    stream=UpperCAmelCase__			).raw			)
       return im
@torch.no_grad()
def  UpperCAmelCase__      ( UpperCAmelCase__,    UpperCAmelCase__,    UpperCAmelCase__=False			)     ->     str:
       A_		   = get_config(UpperCAmelCase__			)

       # load original model from timm
       A_		   = create_model(UpperCAmelCase__,    pretrained=UpperCAmelCase__			)
       timm_model.eval()

       # load state_dict of original model
       A_		   = timm_model.state_dict()
       for key in state_dict.copy().keys():
              A_		   = state_dict.pop(UpperCAmelCase__			)
              A_		   = val.squeeze() if """head""" in key else val

       # load HuggingFace model
       A_		   = BitForImageClassification(UpperCAmelCase__			)
       model.eval()
       model.load_state_dict(UpperCAmelCase__			)

       # create image processor
       A_		   = create_transform(**resolve_data_config({},    model=UpperCAmelCase__			)			)
       A_		   = transform.transforms

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

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

       A_		   = prepare_img()
       A_		   = transform(UpperCAmelCase__			).unsqueeze(0			)
       A_		   = processor(UpperCAmelCase__,    return_tensors="""pt"""			).pixel_values

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

       # verify logits
       with torch.no_grad():
              A_		   = model(UpperCAmelCase__			)
              A_		   = outputs.logits

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

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

       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 functools import lru_cache
@lru_cache
def  UpperCAmelCase__      ( UpperCAmelCase__			)     ->     int:
       if num < 0:
              raise ValueError("""Number should not be negative."""			)

       return 1 if num in (0, 1) else num * factorial(num - 1			)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
