





"""simple docstring"""






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


A	     =					'''\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'''

A	     =					'''\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'''

A	     =					'''\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    \"accuracy\": Accuracy\n    \"f1\": F1 score\n    \"pearson\": Pearson Correlation\n    \"spearmanr\": Spearman Correlation\n    \"matthews_correlation\": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'''

def       __A (     a_  :Optional[int]				,    a_  :Any)						->						List[Any]:
				return float((preds == labels).mean())

def       __A (     a_  :Dict				,    a_  :Optional[int])						->						Optional[int]:
				__a	:  Optional[int]         =					simple_accuracy(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE)
				__a	:  List[Any]         =					float(fa_score(y_true=__SCREAMING_SNAKE_CASE				,    y_pred=__SCREAMING_SNAKE_CASE))
				return {
				    "accuracy": acc,
				    "f1": fa,
				}

def       __A (     a_  :Tuple				,    a_  :Dict)						->						List[str]:
				__a	:  Any         =					float(pearsonr(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE)[0])
				__a	:  List[str]         =					float(spearmanr(__SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE)[0])
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}






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







					'''simple docstring'''




					def 	_lowerCamelCase						(  self       ):


									if self.config_name not in [
									    "sst2",
									    "mnli",
									    "mnli_mismatched",
									    "mnli_matched",
									    "cola",
									    "stsb",
									    "mrpc",
									    "qqp",
									    "qnli",
									    "rte",
									    "wnli",
									    "hans",
									]:
													raise KeyError(
													    '''You should supply a configuration name selected in '''
													    '''[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", '''
													    '''\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]'''       )
									return datasets.MetricInfo(
									    description=_DESCRIPTION		,     citation=_CITATION		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
									        {
									            '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''       ),
									            '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''       ),
									        }       )		,     codebase_urls=[]		,     reference_urls=[]		,     format='''numpy'''		,     )







					def 	_lowerCamelCase						(  self		,     _UpperCAmelCase		,     _UpperCAmelCase       ):


									if self.config_name == "cola":
													return {"matthews_correlation": matthews_corrcoef(UpperCamelCase__		,     UpperCamelCase__       )}
									elif self.config_name == "stsb":
													return pearson_and_spearman(UpperCamelCase__		,     UpperCamelCase__       )
									elif self.config_name in ["mrpc", "qqp"]:
													return acc_and_fa(UpperCamelCase__		,     UpperCamelCase__       )
									elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
													return {"accuracy": simple_accuracy(UpperCamelCase__		,     UpperCamelCase__       )}
									else:
													raise KeyError(
													    '''You should supply a configuration name selected in '''
													    '''[\"sst2\", \"mnli\", \"mnli_mismatched\", \"mnli_matched\", '''
													    '''\"cola\", \"stsb\", \"mrpc\", \"qqp\", \"qnli\", \"rte\", \"wnli\", \"hans\"]'''       )






"""simple docstring"""






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       __A (     a_  :List[Any]=None				,    a_  :Tuple=None)						->						List[Any]:
				return field(default_factory=lambda: default				,    metadata=a_)




@dataclass
class 			__lowercase     :







					'''simple docstring'''




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






def       __A (     a_  :Optional[Any])						->						Any:
				try:
								int(a_)
								return True
				except ValueError:
								return False

def       __A (     a_  :List[Any])						->						Any:
				try:
								float(a_)
								return True
				except ValueError:
								return False




class 			__lowercase     :







					'''simple docstring'''




					def __init__(  self		,     _UpperCAmelCase       ):
									__a	:  Dict         =					args
									__a	:  Tuple         =					defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )

									with open(self.args.csv_file		,     newline=''''''       ) as csv_file:
													__a	:  int         =					csv.DictReader(_UpperCAmelCase       )
													for row in reader:
																	__a	:  Union[str, Any]         =					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
																					__a	:  Optional[int]         =					int(row['''result''']       )
																	elif can_convert_to_float(row['''result''']       ):
																					# value is not None
																					__a	:  Optional[Any]         =					float(row['''result''']       )







					def 	_lowerCamelCase						(  self       ):
									__a    ,  __a	:  Optional[int]         =					plt.subplots()
									__a	:  str         =					'''Time usage''' if self.args.is_time else '''Memory usage'''
									__a	:  str         =					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()       ):
													__a	:  str         =					sorted(set(self.result_dict[model_name]['''bsz''']       )       )
													__a	:  Dict         =					sorted(set(self.result_dict[model_name]['''seq_len''']       )       )
													__a	:  Dict         =					self.result_dict[model_name]['''result''']

													((__a)    ,  (__a))	:  List[Any]         =					(
													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
													)

													__a	:  Any         =					(
													    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:
																					__a	:  Optional[int]         =					np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]		,     dtype=_UpperCAmelCase		,     )
																	else:
																					__a	:  Dict         =					np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]		,     dtype=np.floataa		,     )

																	((__a)    ,  (__a))	:  Union[str, Any]         =					(
																	    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																	)

																	__a	:  Any         =					np.asarray(_UpperCAmelCase		,     _UpperCAmelCase       )[: len(_UpperCAmelCase       )]
																	plt.scatter(
																	    _UpperCAmelCase		,     _UpperCAmelCase		,     label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""       )
																	plt.plot(_UpperCAmelCase		,     _UpperCAmelCase		,     '''--'''       )

													title_str += f""" {label_model_name} vs."""

									__a	:  Optional[Any]         =					title_str[:-4]
									__a	:  Optional[int]         =					'''Time in s''' if self.args.is_time else '''Memory in MB'''

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

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






def       __A (     )						->						List[str]:
				__a	:  List[str]         =					HfArgumentParser(a_)
				__a	:  Optional[int]         =					parser.parse_args_into_dataclasses()[0]
				__a	:  Tuple         =					Plot(args=a_)
				plot.plot()


if __name__ == "__main__":
		main()