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

import datasets


lowerCAmelCase					:    List[str]           = '\\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'

lowerCAmelCase					:    Optional[Any]           = '\\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'

lowerCAmelCase					:    Optional[int]           = '\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						:		Dict		,     A						:		str):
							return float((preds == labels).mean())







def 		A_(     A						:		List[str]		,     A						:		List[str]):
							UpperCamelCase   			=     simple_accuracy(A		,     A)
							UpperCamelCase   			=     float(fa_score(y_true=A		,     y_pred=A))
							return {
							    "accuracy": acc,
							    "f1": fa,
							}







def 		A_(     A						:		Any		,     A						:		List[str]):
							UpperCamelCase   			=     float(pearsonr(A		,     A)[0])
							UpperCamelCase   			=     float(spearmanr(A		,     A)[0])
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}





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







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											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 						UpperCAmelCase_    (       self						,				A_						,				A_     )->		int:







											'''simple docstring'''






											if self.config_name == "cola":
																		return {"matthews_correlation": matthews_corrcoef(A_						,				A_     )}
											elif self.config_name == "stsb":
																		return pearson_and_spearman(A_						,				A_     )
											elif self.config_name in ["mrpc", "qqp"]:
																		return acc_and_fa(A_						,				A_     )
											elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
																		return {"accuracy": simple_accuracy(A_						,				A_     )}
											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 copy
import os

import cva
import numpy as np
from matplotlib import pyplot as plt


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ''
											UpperCamelCase   			=     ''
											UpperCamelCase   			=     []
											UpperCamelCase   			=     0
											UpperCamelCase   			=     256
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0
											UpperCamelCase   			=     0







				def 						UpperCAmelCase_    (       self						,				A_     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     cva.imread(A_						,				0     )
											UpperCamelCase   			=     copy.deepcopy(self.img     )
											UpperCamelCase			,	UpperCamelCase			,	UpperCamelCase   			=     plt.hist(self.img.ravel()						,				256						,				[0, 256]						,				label='x'     )
											UpperCamelCase   			=     np.sum(A_     )
											for i in range(len(A_     )     ):
																		UpperCamelCase   			=     x[i] / self.k
																		self.sk += prk
																		UpperCamelCase   			=     (self.L - 1) * self.sk
																		if self.rem != 0:
																									UpperCamelCase   			=     int(last % last     )
																		UpperCamelCase   			=     int(last + 1 if self.rem >= 0.5 else last     )
																		self.last_list.append(A_     )
																		UpperCamelCase   			=     int(np.ma.count(self.img     ) / self.img[1].size     )
																		UpperCamelCase   			=     self.img[1].size
											for i in range(self.number_of_cols     ):
																		for j in range(self.number_of_rows     ):
																									UpperCamelCase   			=     self.img[j][i]
																									if num != self.last_list[num]:
																																UpperCamelCase   			=     self.last_list[num]
											cva.imwrite('output_data/output.jpg'						,				self.img     )







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											plt.hist(self.img.ravel()						,				256						,				[0, 256]     )







				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											cva.imshow('Output-Image'						,				self.img     )
											cva.imshow('Input-Image'						,				self.original_image     )
											cva.waitKey(5000     )
											cva.destroyAllWindows()


if __name__ == "__main__":
						lowerCAmelCase					:    Union[str, Any]           = os.path.join(os.path.basename(__file__), 'image_data/input.jpg')
						lowerCAmelCase					:    str           = ConstantStretch()
						stretcher.stretch(file_path)
						stretcher.plot_histogram()
						stretcher.show_image()




