





'''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()
__magic_name__   :						str    =			logging.get_logger(__name__)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  _snake_case     		=				"huggingface/label-files"
  _snake_case     		=				"imagenet-1k-id2label.json"
  _snake_case     		=				json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	repo_type="dataset"       )					,	"r"       )       )
  _snake_case     		=				{int(SCREAMING_SNAKE_CASE__       ): v for k, v in idalabel.items()}
  _snake_case     		=				{v: k for k, v in idalabel.items()}

  _snake_case     		=				"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"
  _snake_case     		=				BitConfig(
      conv_layer=SCREAMING_SNAKE_CASE__					,	num_labels=10_00					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__					,	)

  return config




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  if "stem.conv" in name:
    _snake_case     		=				name.replace("stem.conv"					,	"bit.embedder.convolution"       )
  if "blocks" in name:
    _snake_case     		=				name.replace("blocks"					,	"layers"       )
  if "head.fc" in name:
    _snake_case     		=				name.replace("head.fc"					,	"classifier.1"       )
  if name.startswith("norm"       ):
    _snake_case     		=				"bit." + name
  if "bit" not in name and "classifier" not in name:
    _snake_case     		=				"bit.encoder." + name

  return name




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				"http://images.cocodataset.org/val2017/000000039769.jpg"
  _snake_case     		=				Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__       ).raw       )
  return im




@torch.no_grad()
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False       ):







  '''simple docstring'''



  _snake_case     		=				get_config(SCREAMING_SNAKE_CASE__       )

  # load original model from timm
  _snake_case     		=				create_model(SCREAMING_SNAKE_CASE__					,	pretrained=SCREAMING_SNAKE_CASE__       )
  timm_model.eval()

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

  # load HuggingFace model
  _snake_case     		=				BitForImageClassification(SCREAMING_SNAKE_CASE__       )
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE__       )

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

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

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

  _snake_case     		=				prepare_img()
  _snake_case     		=				transform(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )
  _snake_case     		=				processor(SCREAMING_SNAKE_CASE__					,	return_tensors="pt"       ).pixel_values

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

  # verify logits
  with torch.no_grad():
    _snake_case     		=				model(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				outputs.logits

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

  if pytorch_dump_folder_path is not None:
    Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
    print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(SCREAMING_SNAKE_CASE__       )
    processor.save_pretrained(SCREAMING_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__":
 __magic_name__   :						List[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.""",
 )

 __magic_name__   :						Optional[Any]    =			parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import fa_score, matthews_corrcoef


__magic_name__   :						List[str]    =			(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  return (preds == labels).mean()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__       )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       ), f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mrpc":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "sts-b":
    return pearson_and_spearman(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "qqp":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "rte":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "hans":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
    raise ValueError(f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''       )
  if task_name == "xnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )


