






'''simple docstring'''







from scipy.stats import pearsonr

import datasets


_A	:			Optional[int]         ='''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.
'''


_A	:			Optional[int]         ='''
Args:
    predictions (`list` of `int`): Predicted class labels, as returned by a model.
    references (`list` of `int`): Ground truth labels.
    return_pvalue (`boolean`): If `True`, returns the p-value, along with the correlation coefficient. If `False`, returns only the correlation coefficient. Defaults to `False`.

Returns:
    pearsonr (`float`): Pearson correlation coefficient. Minimum possible value is -1. Maximum possible value is 1. Values of 1 and -1 indicate exact linear positive and negative relationships, respectively. A value of 0 implies no correlation.
    p-value (`float`): P-value, which roughly indicates the probability of an The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets. Minimum possible value is 0. Maximum possible value is 1. Higher values indicate higher probabilities.

Examples:

    Example 1-A simple example using only predictions and references.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5])
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74

    Example 2-The same as Example 1, but that also returns the `p-value`.
        >>> pearsonr_metric = datasets.load_metric("pearsonr")
        >>> results = pearsonr_metric.compute(predictions=[10, 9, 2.5, 6, 4], references=[1, 2, 3, 4, 5], return_pvalue=True)
        >>> print(sorted(list(results.keys())))
        [\'p-value\', \'pearsonr\']
        >>> print(round(results[\'pearsonr\'], 2))
        -0.74
        >>> print(round(results[\'p-value\'], 2))
        0.15
'''


_A	:			List[str]         ='''
@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, Ilhan 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, Antonio 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		):




							def 		lowerCamelCase_				(							self:				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.pearsonr.html"""]			,							)




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Union[str, Any]			,							UpperCamelCase__:				Any=False     ):
														if return_pvalue:
																					lowerCamelCase__					:	str         =       pearsonr(UpperCamelCase__			,							UpperCamelCase__     )
																					return {"pearsonr": results[0], "p-value": results[1]}
														else:
																					return {"pearsonr": float(pearsonr(UpperCamelCase__			,							UpperCamelCase__     )[0]     )}





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser







def        _lowerCamelCase(	lowercase__=None   , lowercase__=None	)						->	Dict:

		'''simple docstring'''







		return field(default_factory=lambda: default   , metadata=lowercase__	)





@dataclass
class       A    :
							UpperCamelCase_   :							str       =field(
							    metadata={'''help''': '''The csv file to plot.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={'''help''': '''Disable logarithmic scale when plotting'''}					,				)
							UpperCamelCase_   :							bool       =field(
							    default=A_					,				metadata={
							        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
							    }					,				)
							UpperCamelCase_   :							Optional[str]       =field(
							    default=A_					,				metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}					,				)
							UpperCamelCase_   :							Optional[List[str]]       =list_field(
							    default=A_					,				metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''} )







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		try:
				int(lowercase__	)
				return True
		except ValueError:
				return False







def        _lowerCamelCase(	lowercase__	)						->	int:

		'''simple docstring'''







		try:
				float(lowercase__	)
				return True
		except ValueError:
				return False





class       A    :


							def __init__(self      ,				lowerCAmelCase					):
									__lowercase=			args
									__lowercase=			defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)

									with open(self.args.csv_file      ,				newline=''					) as csv_file:
											__lowercase=			csv.DictReader(lowerCAmelCase					)
											for row in reader:
													__lowercase=			row['model']
													self.result_dict[model_name]["bsz"].append(int(row['batch_size']					)					)
													self.result_dict[model_name]["seq_len"].append(int(row['sequence_length']					)					)
													if can_convert_to_int(row['result']					):
															# value is not None
															__lowercase=			int(row['result']					)
													elif can_convert_to_float(row['result']					):
															# value is not None
															__lowercase=			float(row['result']					)







							def 			_A       (self					):
									__lowercase,							__lowercase=			plt.subplots()
									__lowercase=			'Time usage' if self.args.is_time else 'Memory usage'
									__lowercase=			title_str + ' for training' if self.args.is_train else title_str + ' for inference'

									if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale('log'					)
											ax.set_yscale('log'					)

									for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()					)

									for model_name_idx, model_name in enumerate(self.result_dict.keys()					):
											__lowercase=			sorted(set(self.result_dict[model_name]['bsz']					)					)
											__lowercase=			sorted(set(self.result_dict[model_name]['seq_len']					)					)
											__lowercase=			self.result_dict[model_name]['result']

											((__lowercase),							(__lowercase))=			(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__lowercase=			(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
													if self.args.plot_along_batch:
															__lowercase=			np.asarray(
															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,				dtype=lowerCAmelCase      ,				)
													else:
															__lowercase=			np.asarray(
															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,				dtype=np.floataa      ,				)

													((__lowercase),							(__lowercase))=			(
													    ('batch_size', 'len') if self.args.plot_along_batch else ('in #tokens', 'bsz')
													)

													__lowercase=			np.asarray(lowerCAmelCase      ,				lowerCAmelCase					)[: len(lowerCAmelCase					)]
													plt.scatter(
													    lowerCAmelCase      ,				lowerCAmelCase      ,				label=f'{label_model_name} - {inner_loop_label}: {inner_loop_value}'					)
													plt.plot(lowerCAmelCase      ,				lowerCAmelCase      ,				'--'					)

											title_str += f' {label_model_name} vs.'

									__lowercase=			title_str[:-4]
									__lowercase=			'Time in s' if self.args.is_time else 'Memory in MB'

									# plot
									plt.title(lowerCAmelCase					)
									plt.xlabel(lowerCAmelCase					)
									plt.ylabel(lowerCAmelCase					)
									plt.legend()

									if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file					)
									else:
											plt.show()







def        _lowerCamelCase(	)						->	str:

		'''simple docstring'''







		__lowercase=			HfArgumentParser(lowercase__	)
		__lowercase=			parser.parse_args_into_dataclasses()[0]
		__lowercase=			Plot(args=lowercase__	)
		plot.plot()


if __name__ == "__main__":
	main()






