

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 	_A  (					A__   ):






 """simple docstring"""



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

 __lowercase													=				'''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"
 __lowercase													=				BitConfig(
     conv_layer=A__  ,       num_labels=1000  ,       idalabel=A__  ,       labelaid=A__  ,       )

 return config


def 	_A  (					A__   ):






 """simple docstring"""



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

 return name


def 	_A  (					):






 """simple docstring"""



 __lowercase													=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 __lowercase													=				Image.open(requests.get(A__  ,       stream=A__   ).raw   )
 return im


@torch.no_grad()
def 	_A  (					A__  ,       A__  ,       A__=False   ):






 """simple docstring"""



 __lowercase													=				get_config(A__   )

 # load original model from timm
 __lowercase													=				create_model(A__  ,       pretrained=A__   )
 timm_model.eval()

 # load state_dict of original model
 __lowercase													=				timm_model.state_dict()
 for key in state_dict.copy().keys():
  __lowercase													=				state_dict.pop(A__   )
  __lowercase													=				val.squeeze() if '''head''' in key else val

 # load HuggingFace model
 __lowercase													=				BitForImageClassification(A__   )
 model.eval()
 model.load_state_dict(A__   )

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

 __lowercase													=				{
     '''bilinear''': PILImageResampling.BILINEAR,
     '''bicubic''': PILImageResampling.BICUBIC,
     '''nearest''': PILImageResampling.NEAREST,
 }

 __lowercase													=				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()  ,       )

 __lowercase													=				prepare_img()
 __lowercase													=				transform(A__   ).unsqueeze(0   )
 __lowercase													=				processor(A__  ,       return_tensors='''pt'''   ).pixel_values

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

 # verify logits
 with torch.no_grad():
  __lowercase													=				model(A__   )
  __lowercase													=				outputs.logits

 print('''Logits:'''  ,       logits[0, :3]   )
 print('''Predicted class:'''  ,       model.config.idalabel[logits.argmax(-1   ).item()]   )
 __lowercase													=				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__      		=			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 scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCAmelCase__      		=			'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCAmelCase__      		=			R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       , _KWARGS_DESCRIPTION   )
class     lowercase_			(datasets.Metric   ):




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return datasets.MetricInfo(
       description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
           {
               '''predictions''': datasets.Value('''float'''    ),
               '''references''': datasets.Value('''float'''    ),
           }    )      ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}

