


"""simple docstring"""


from scipy.stats import spearmanr

import datasets


__lowerCamelCase      :Optional[Any]	    =		"""
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      :Any	    =		"""
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      :int	    =		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 			A__	(						datasets.Metric):


     """simple docstring"""

     def 		a__						(	self:						Any	)->    List[str]:
           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 		a__						(	self:						Optional[int]     ,		__a:						Any     ,		__a:						int     ,		__a:						Any=False	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	spearmanr(snake_case__     ,		snake_case__	)
           if return_pvalue:
                 return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
           else:
                 return {"spearmanr": results[0]}








"""simple docstring"""


import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase      :Dict	    =		logging.get_logger()




def   snake_case		(     UpperCamelCase__       :						int			,				UpperCamelCase__       :						str			,				UpperCamelCase__       :						LevitConfig			,				UpperCamelCase__       :						Path			,				UpperCamelCase__       :						bool = True    )   ->      Dict:
      print(F'Converting {name}...'    )

      with torch.no_grad():
            if hidden_sizes == 128:
                  if name[-1] == "S":
                        lowerCamelCase					:					Optional[Any]        =	timm.create_model("""levit_128s"""			,				pretrained=UpperCamelCase__    )
                  else:
                        lowerCamelCase					:					Dict        =	timm.create_model("""levit_128"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 192:
                  lowerCamelCase					:					Tuple        =	timm.create_model("""levit_192"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 256:
                  lowerCamelCase					:					Optional[int]        =	timm.create_model("""levit_256"""			,				pretrained=UpperCamelCase__    )
            if hidden_sizes == 384:
                  lowerCamelCase					:					Dict        =	timm.create_model("""levit_384"""			,				pretrained=UpperCamelCase__    )

            from_model.eval()
            lowerCamelCase					:					Optional[Any]        =	LevitForImageClassificationWithTeacher(UpperCamelCase__    ).eval()
            lowerCamelCase					:					Tuple        =	OrderedDict()

            lowerCamelCase					:					Optional[Any]        =	from_model.state_dict()
            lowerCamelCase					:					str        =	list(from_model.state_dict().keys()    )
            lowerCamelCase					:					List[Any]        =	list(our_model.state_dict().keys()    )
            print(len(UpperCamelCase__    )			,				len(UpperCamelCase__    )    )
            for i in range(len(UpperCamelCase__    )    ):
                  lowerCamelCase					:					str        =	weights[og_keys[i]]
            our_model.load_state_dict(UpperCamelCase__    )

            lowerCamelCase					:					int        =	torch.randn((2, 3, 224, 224)    )
            lowerCamelCase					:					Any        =	from_model(UpperCamelCase__    )
            lowerCamelCase					:					List[Any]        =	our_model(UpperCamelCase__    ).logits

      assert torch.allclose(UpperCamelCase__			,				UpperCamelCase__    ), "The model logits don't match the original one."

      lowerCamelCase					:					Dict        =	name
      print(UpperCamelCase__    )

      if push_to_hub:
            our_model.save_pretrained(save_directory / checkpoint_name    )
            lowerCamelCase					:					Optional[int]        =	LevitImageProcessor()
            image_processor.save_pretrained(save_directory / checkpoint_name    )

            print(F'Pushed {checkpoint_name}'    )




def   snake_case		(     UpperCamelCase__       :						Path			,				UpperCamelCase__       :						str = None			,				UpperCamelCase__       :						bool = True    )   ->      Optional[int]:
      lowerCamelCase					:					Optional[Any]        =	"""imagenet-1k-id2label.json"""
      lowerCamelCase					:					List[Any]        =	1000
      lowerCamelCase					:					Dict        =	(1, num_labels)

      lowerCamelCase					:					List[Any]        =	"""huggingface/label-files"""
      lowerCamelCase					:					Optional[int]        =	num_labels
      lowerCamelCase					:					List[str]        =	json.load(open(hf_hub_download(UpperCamelCase__			,				UpperCamelCase__			,				repo_type="""dataset"""    )			,				"""r"""    )    )
      lowerCamelCase					:					Any        =	{int(UpperCamelCase__    ): v for k, v in idalabel.items()}

      lowerCamelCase					:					List[Any]        =	idalabel
      lowerCamelCase					:					str        =	{v: k for k, v in idalabel.items()}

      lowerCamelCase					:					Tuple        =	partial(UpperCamelCase__			,				num_labels=UpperCamelCase__			,				idalabel=UpperCamelCase__			,				labelaid=UpperCamelCase__    )

      lowerCamelCase					:					Optional[int]        =	{
          """levit-128S""": 128,
          """levit-128""": 128,
          """levit-192""": 192,
          """levit-256""": 256,
          """levit-384""": 384,
      }

      lowerCamelCase					:					List[Any]        =	{
          """levit-128S""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 6, 8]			,				depths=[2, 3, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-128""": ImageNetPreTrainedConfig(
              hidden_sizes=[128, 256, 384]			,				num_attention_heads=[4, 8, 12]			,				depths=[4, 4, 4]			,				key_dim=[16, 16, 16]			,				drop_path_rate=0			,				),
          """levit-192""": ImageNetPreTrainedConfig(
              hidden_sizes=[192, 288, 384]			,				num_attention_heads=[3, 5, 6]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-256""": ImageNetPreTrainedConfig(
              hidden_sizes=[256, 384, 512]			,				num_attention_heads=[4, 6, 8]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0			,				),
          """levit-384""": ImageNetPreTrainedConfig(
              hidden_sizes=[384, 512, 768]			,				num_attention_heads=[6, 9, 12]			,				depths=[4, 4, 4]			,				key_dim=[32, 32, 32]			,				drop_path_rate=0.1			,				),
      }

      if model_name:
            convert_weight_and_push(
                names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				names_to_config[model_name]			,				UpperCamelCase__			,				UpperCamelCase__    )
      else:
            for model_name, config in names_to_config.items():
                  convert_weight_and_push(names_to_hidden_sizes[model_name]			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__			,				UpperCamelCase__    )
      return config, expected_shape


if __name__ == "__main__":
    __lowerCamelCase      :Union[str, Any]	    =		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--model_name',
        default=None,
        type=str,
        help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path',
        default='levit-dump-folder/',
        type=Path,
        required=False,
        help='Path to the output PyTorch model directory.',
    )
    parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
    parser.add_argument(
        '--no-push_to_hub',
        dest='push_to_hub',
        action='store_false',
        help='Do not push model and image processor to the hub',
    )

    __lowerCamelCase      :List[Any]	    =		parser.parse_args()
    __lowerCamelCase      :Path	    =		args.pytorch_dump_folder_path
    pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
    convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)




