




"""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      _snake_case			(				lowercase__=None							,    lowercase__=None		):
				return field(default_factory=lambda: default							,    metadata=lowercase__		)






@dataclass
class lowerCAmelCase__	:






			'''simple docstring'''



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







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







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






class lowerCAmelCase__	:






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							_lowerCamelCase     :			Any    =    args
							_lowerCamelCase     :			Tuple    =    defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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






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

											((_lowerCamelCase), (_lowerCamelCase))     :			Optional[int]    =    (
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											_lowerCamelCase     :			Union[str, 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:
																			_lowerCamelCase     :			Optional[Any]    =    np.asarray(
																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,      dtype=lowercase  ,      )
															else:
																			_lowerCamelCase     :			Optional[int]    =    np.asarray(
																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,      dtype=np.floataa  ,      )

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

															_lowerCamelCase     :			List[Any]    =    np.asarray(lowercase  ,      lowercase  )[: len(lowercase  )]
															plt.scatter(
															    lowercase  ,      lowercase  ,      label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''  )
															plt.plot(lowercase  ,      lowercase  ,      '--'  )

											title_str += F''' {label_model_name} vs.'''

							_lowerCamelCase     :			Dict    =    title_str[:-4]
							_lowerCamelCase     :			List[Any]    =    'Time in s' if self.args.is_time else 'Memory in MB'

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

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







def      _snake_case			(				):
				_lowerCamelCase     :			Any    =    HfArgumentParser(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    parser.parse_args_into_dataclasses()[0]
				_lowerCamelCase     :			Dict    =    Plot(args=lowercase__		)
				plot.plot()


if __name__ == "__main__":
			main()





"""simple docstring"""






from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowercase__     						=						"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

lowercase__     						=						"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

lowercase__     						=						"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def      _snake_case			(				lowercase__							,    lowercase__		):
				return float((preds == labels).mean()		)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__="binary"		):
				_lowerCamelCase     :			str    =    simple_accuracy(lowercase__							,    lowercase__		)
				_lowerCamelCase     :			Any    =    float(fa_score(y_true=lowercase__							,    y_pred=lowercase__							,    average=lowercase__		)		)
				return {
				    "accuracy": acc,
				    "f1": fa,
				}







def      _snake_case			(				lowercase__							,    lowercase__		):
				_lowerCamelCase     :			Any    =    {}
				for id_pred, label in zip(lowercase__							,    lowercase__		):
								_lowerCamelCase     :			Tuple    =    f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
								_lowerCamelCase     :			Union[str, Any]    =    id_pred['prediction']
								if question_id in question_map:
												question_map[question_id].append((pred, label)		)
								else:
												_lowerCamelCase     :			Optional[Any]    =    [(pred, label)]
				_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    [], []
				for question, preds_labels in question_map.items():
								_lowerCamelCase, _lowerCamelCase     :			Tuple    =    zip(*lowercase__		)
								_lowerCamelCase     :			List[str]    =    fa_score(y_true=lowercase__							,    y_pred=lowercase__							,    average='macro'		)
								fas.append(lowercase__		)
								_lowerCamelCase     :			int    =    int(sum(pred == label for pred, label in preds_labels		) == len(lowercase__		)		)
								ems.append(lowercase__		)
				_lowerCamelCase     :			Optional[Any]    =    float(sum(lowercase__		) / len(lowercase__		)		)
				_lowerCamelCase     :			Optional[int]    =    sum(lowercase__		) / len(lowercase__		)
				_lowerCamelCase     :			List[Any]    =    float(fa_score(y_true=lowercase__							,    y_pred=[id_pred['prediction'] for id_pred in ids_preds]		)		)
				return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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






			'''simple docstring'''



			def 					A_    (  self  ):
							if self.config_name not in [
							    "boolq",
							    "cb",
							    "copa",
							    "multirc",
							    "record",
							    "rte",
							    "wic",
							    "wsc",
							    "wsc.fixed",
							    "axb",
							    "axg",
							]:
											raise KeyError(
											    'You should supply a configuration name selected in '
											    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'  )
							return datasets.MetricInfo(
							    description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(self._get_feature_types()  )  ,      codebase_urls=[]  ,      reference_urls=[]  ,      format='numpy' if not self.config_name == 'record' and not self.config_name == 'multirc' else None  ,      )
			def 					A_    (  self  ):
							if self.config_name == "record":
											return {
											    "predictions": {
											        "idx": {
											            "passage": datasets.Value('int64'  ),
											            "query": datasets.Value('int64'  ),
											        },
											        "prediction_text": datasets.Value('string'  ),
											    },
											    "references": {
											        "idx": {
											            "passage": datasets.Value('int64'  ),
											            "query": datasets.Value('int64'  ),
											        },
											        "answers": datasets.Sequence(datasets.Value('string'  )  ),
											    },
											}
							elif self.config_name == "multirc":
											return {
											    "predictions": {
											        "idx": {
											            "answer": datasets.Value('int64'  ),
											            "paragraph": datasets.Value('int64'  ),
											            "question": datasets.Value('int64'  ),
											        },
											        "prediction": datasets.Value('int64'  ),
											    },
											    "references": datasets.Value('int64'  ),
											}
							else:
											return {
											    "predictions": datasets.Value('int64'  ),
											    "references": datasets.Value('int64'  ),
											}






			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							if self.config_name == "axb":
											return {"matthews_correlation": matthews_corrcoef(lowercase  ,      lowercase  )}
							elif self.config_name == "cb":
											return acc_and_fa(lowercase  ,      lowercase  ,      fa_avg='macro'  )
							elif self.config_name == "record":
											_lowerCamelCase     :			List[str]    =    [
											    {
											        'qas': [
											            {'id': ref['idx']['query'], 'answers': [{'text': ans} for ans in ref['answers']]}
											            for ref in references
											        ]
											    }
											]
											_lowerCamelCase     :			Union[str, Any]    =    {pred['idx']['query']: pred['prediction_text'] for pred in predictions}
											return evaluate_record(lowercase  ,      lowercase  )[0]
							elif self.config_name == "multirc":
											return evaluate_multirc(lowercase  ,      lowercase  )
							elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
											return {"accuracy": simple_accuracy(lowercase  ,      lowercase  )}
							else:
											raise KeyError(
											    'You should supply a configuration name selected in '
											    '["boolq", "cb", "copa", "multirc", "record", "rte", "wic", "wsc", "wsc.fixed", "axb", "axg",]'  )