
from scipy.stats import spearmanr

import datasets


__lowerCAmelCase			:     Any           =   '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'

__lowerCAmelCase			:     Optional[int]           =   '\nArgs:\n    predictions (`List[float]`): Predicted labels, as returned by a model.\n    references (`List[float]`): Ground truth labels.\n    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n            only the spearmanr score. Defaults to `False`.\nReturns:\n    spearmanr (`float`): Spearman correlation coefficient.\n    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n    Example 1:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n        >>> print(results)\n        {\'spearmanr\': -0.7}\n\n    Example 2:\n        >>> spearmanr_metric = datasets.load_metric("spearmanr")\n        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n        ...                                     predictions=[10, 9, 2.5, 6, 4],\n        ...                                     return_pvalue=True)\n        >>> print(results[\'spearmanr\'])\n        -0.7\n        >>> print(round(results[\'spearmanr_pvalue\'], 2))\n        0.19\n'

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




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


  '''simple docstring'''







  def 							_lowercase							( self			: List[Any]    )			->	Dict:







    """simple docstring"""






    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 							_lowercase							( self			: Dict			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: Tuple=False    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       spearmanr(SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_    )
    if return_pvalue:
      return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
    else:
      return {"spearmanr": results[0]}






import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def 		UpperCamelCase	(   snake_case__       :    List[str]       ,     snake_case__       :    Union[str, Any]       ,     snake_case__       :    Union[str, Any]	)	->     int:
   # Initialise PyTorch model
   UpperCamelCase    :  Any							=	MobileBertConfig.from_json_file(snake_case__	)
   print(F"""Building PyTorch model from configuration: {config}"""	)
   UpperCamelCase    :  Tuple							=	MobileBertForPreTraining(snake_case__	)
   # Load weights from tf checkpoint
   UpperCamelCase    :  int							=	load_tf_weights_in_mobilebert(snake_case__       ,     snake_case__       ,     snake_case__	)
   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}"""	)
   torch.save(model.state_dict()       ,     snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--mobilebert_config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained MobileBERT model. \n'''
            '''This specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __UpperCAmelCase          =       parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)



