

'''simple docstring'''

import argparse
import json
import re
import string
import sys
from collections import Counter





def 				lowercase (					__magic_name__							):



					'''simple docstring'''







					def remove_articles(__magic_name__							):
										return re.sub(R"\b(a|an|the)\b"			,   " "			,   __magic_name__							)

					def white_space_fix(__magic_name__							):
										return " ".join(text.split()							)

					def remove_punc(__magic_name__							):
										UpperCAmelCase		:							Any        =       set(string.punctuation							)
										return "".join(ch for ch in text if ch not in exclude							)

					def lower(__magic_name__							):
										return text.lower()

					return white_space_fix(remove_articles(remove_punc(lower(__magic_name__							)							)							)							)





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Union[str, Any]        =       normalize_answer(__magic_name__							).split()
					UpperCAmelCase		:							Tuple        =       normalize_answer(__magic_name__							).split()
					UpperCAmelCase		:							Dict        =       Counter(__magic_name__							) & Counter(__magic_name__							)
					UpperCAmelCase		:							Tuple        =       sum(common.values()							)
					if num_same == 0:
										return 0
					UpperCAmelCase		:							List[Any]        =       1.0 * num_same / len(__magic_name__							)
					UpperCAmelCase		:							Dict        =       1.0 * num_same / len(__magic_name__							)
					UpperCAmelCase		:							List[Any]        =       (2 * precision * recall) / (precision + recall)
					return fa





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					return normalize_answer(__magic_name__							) == normalize_answer(__magic_name__							)





def 				lowercase (					__magic_name__			,   __magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Tuple        =       []
					for ground_truth in ground_truths:
										UpperCAmelCase		:							Dict        =       metric_fn(__magic_name__			,   __magic_name__							)
										scores_for_ground_truths.append(__magic_name__							)
					return max(__magic_name__							)





def 				lowercase (					__magic_name__			,   __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Optional[int]        =       0
					UpperCAmelCase		:							List[Any]        =       []
					for passage in dataset:
										for qa in passage["qas"]:
															total += 1
															if qa["id"] not in predictions:
																				UpperCAmelCase		:							List[str]        =       F"Unanswered question {qa['id']} will receive score 0."
																				print(__magic_name__			,   file=sys.stderr							)
																				continue

															UpperCAmelCase		:							Optional[int]        =       [x["text"] for x in qa["answers"]]
															UpperCAmelCase		:							List[str]        =       predictions[qa["id"]]

															UpperCAmelCase		:							str        =       metric_max_over_ground_truths(__magic_name__			,   __magic_name__			,   __magic_name__							)
															if int(_exact_match							) == 1:
																				correct_ids.append(qa["id"]							)
															exact_match += _exact_match

															fa += metric_max_over_ground_truths(__magic_name__			,   __magic_name__			,   __magic_name__							)

					UpperCAmelCase		:							Optional[int]        =       exact_match / total
					UpperCAmelCase		:							int        =       fa / total

					return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
				a       :      int  	=			"1.0"
				a       :      Optional[Any]  	=			argparse.ArgumentParser("Official evaluation script for ReCoRD v1.0.")
				parser.add_argument("data_file", help="The dataset file in JSON format.")
				parser.add_argument("pred_file", help="The model prediction file in JSON format.")
				parser.add_argument("--output_correct_ids", action="store_true", help="Output the correctly answered query IDs.")
				a       :      Any  	=			parser.parse_args()

				with open(args.data_file) as data_file:
								a       :      str  	=			json.load(data_file)
								if dataset_json["version"] != expected_version:
												print(
												    F'Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json["version"]}',
												    file=sys.stderr,
												)
								a       :      str  	=			dataset_json["data"]

				with open(args.pred_file) as pred_file:
								a       :      Optional[Any]  	=			json.load(pred_file)

				a	,						a       :      List[str]  	=			evaluate(dataset, predictions)

				if args.output_correct_ids:
								print(F'Output {len(correct_ids)} correctly answered question IDs.')
								with open("correct_ids.json", "w") as f:
												json.dump(correct_ids, f)



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
