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




def   _UpperCamelCase   (			snake_case__							)						->   int:

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

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

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

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

							return white_space_fix(remove_articles(remove_punc(lower(snake_case__							)							)							)							)




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Tuple:
							__UpperCAmelCase						:			Union[str, Any]         =   normalize_answer(snake_case__							).split()
							__UpperCAmelCase						:			str         =   normalize_answer(snake_case__							).split()
							__UpperCAmelCase						:			Any         =   Counter(snake_case__							) & Counter(snake_case__							)
							__UpperCAmelCase						:			List[Any]         =   sum(common.values()							)
							if num_same == 0:
														return 0
							__UpperCAmelCase						:			Optional[int]         =   1.0 * num_same / len(snake_case__							)
							__UpperCAmelCase						:			Optional[int]         =   1.0 * num_same / len(snake_case__							)
							__UpperCAmelCase						:			Dict         =   (2 * precision * recall) / (precision + recall)
							return fa




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Any:
							return normalize_answer(snake_case__							) == normalize_answer(snake_case__							)




def   _UpperCamelCase   (			snake_case__,    snake_case__,    snake_case__							)						->   Any:
							__UpperCAmelCase						:			Dict         =   []
							for ground_truth in ground_truths:
														__UpperCAmelCase						:			Dict         =   metric_fn(snake_case__,    snake_case__							)
														scores_for_ground_truths.append(snake_case__							)
							return max(snake_case__							)




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   Any:
							__UpperCAmelCase						:			Optional[Any]         =   0
							for article in dataset:
														for paragraph in article["paragraphs"]:
																					for qa in paragraph["qas"]:
																												total += 1
																												if qa["id"] not in predictions:
																																			__UpperCAmelCase						:			int         =   "Unanswered question " + qa["id"] + " will receive score 0."
																																			print(snake_case__,    file=sys.stderr							)
																																			continue
																												__UpperCAmelCase						:			Any         =   [x["text"] for x in qa["answers"]]
																												__UpperCAmelCase						:			Optional[int]         =   predictions[qa["id"]]
																												exact_match += metric_max_over_ground_truths(snake_case__,    snake_case__,    snake_case__							)
																												fa += metric_max_over_ground_truths(snake_case__,    snake_case__,    snake_case__							)

							__UpperCAmelCase						:			Union[str, Any]         =   100.0 * exact_match / total
							__UpperCAmelCase						:			Union[str, Any]         =   100.0 * fa / total

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


if __name__ == "__main__":
	_snake_case			=						'''1.1'''
	_snake_case			=						argparse.ArgumentParser(description='''Evaluation for SQuAD ''' + expected_version)
	parser.add_argument('''dataset_file''', help='''Dataset file''')
	parser.add_argument('''prediction_file''', help='''Prediction File''')
	_snake_case			=						parser.parse_args()
	with open(args.dataset_file) as dataset_file:
		_snake_case			=						json.load(dataset_file)
		if dataset_json["version"] != expected_version:
			print(
			    '''Evaluation expects v-''' + expected_version + ''', but got dataset with v-''' + dataset_json['''version'''],
			    file=sys.stderr,
			)
		_snake_case			=						dataset_json['''data''']
	with open(args.prediction_file) as prediction_file:
		_snake_case			=						json.load(prediction_file)
	print(json.dumps(evaluate(dataset, predictions)))





import io
import json
import subprocess


_snake_case			=						[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

_snake_case			=						8




def   _UpperCamelCase   (			snake_case__,    snake_case__							)						->   List[Any]:
							__UpperCAmelCase						:			int         =   {}
							for src, tgt in pairs:
														__UpperCAmelCase						:			Optional[Any]         =   f'''{src}-{tgt}'''
														__UpperCAmelCase						:			Union[str, Any]         =   f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
														__UpperCAmelCase						:			Any         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			Tuple         =   f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
														__UpperCAmelCase						:			List[Any]         =   subprocess.run(snake_case__,    stdout=subprocess.PIPE							).stdout.decode("utf-8"							).splitlines()
														__UpperCAmelCase						:			str         =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
							return text


_snake_case			=						get_all_data(pairs, n_objs)
_snake_case			=						'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
	_snake_case			=						json.dump(text, f, indent=2, ensure_ascii=False)




