


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

def  lowerCAmelCase__				(    a__:	Any )						->	Dict:


							'''simple docstring'''





							def remove_articles(a__:	int ):
														return re.sub(R'\b(a|an|the)\b'    ,						' '    ,						a__ )

							def white_space_fix(a__:	Union[str, Any] ):
														return " ".join(text.split() )

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

							def lower(a__:	Union[str, Any] ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(a__ ) ) ) )

def  lowerCAmelCase__				(    a__:	Dict    ,						a__:	List[str] )						->	List[Any]:


							'''simple docstring'''





							_UpperCAmelCase					=    normalize_answer(a__ ).split()
							_UpperCAmelCase					=    normalize_answer(a__ ).split()
							_UpperCAmelCase					=    Counter(a__ ) & Counter(a__ )
							_UpperCAmelCase					=    sum(common.values() )
							if num_same == 0:
														return 0
							_UpperCAmelCase					=    1.0 * num_same / len(a__ )
							_UpperCAmelCase					=    1.0 * num_same / len(a__ )
							_UpperCAmelCase					=    (2 * precision * recall) / (precision + recall)
							return fa

def  lowerCAmelCase__				(    a__:	Tuple    ,						a__:	Any )						->	List[str]:


							'''simple docstring'''





							return normalize_answer(a__ ) == normalize_answer(a__ )

def  lowerCAmelCase__				(    a__:	str    ,						a__:	str    ,						a__:	str )						->	Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=    []
							for ground_truth in ground_truths:
														_UpperCAmelCase					=    metric_fn(a__    ,						a__ )
														scores_for_ground_truths.append(a__ )
							return max(a__ )

def  lowerCAmelCase__				(    a__:	List[str]    ,						a__:	Optional[Any] )						->	str:


							'''simple docstring'''





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

																					_UpperCAmelCase					=    [x['text'] for x in qa['answers']]
																					_UpperCAmelCase					=    predictions[qa['id']]

																					_UpperCAmelCase					=    metric_max_over_ground_truths(a__    ,						a__    ,						a__ )
																					if int(_exact_match ) == 1:
																												correct_ids.append(qa['id'] )
																					exact_match += _exact_match

																					fa += metric_max_over_ground_truths(a__    ,						a__    ,						a__ )

							_UpperCAmelCase					=    exact_match / total
							_UpperCAmelCase					=    fa / total

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


if __name__ == "__main__":
			lowerCAmelCase__						:Optional[int]         =							'''1.0'''
			lowerCAmelCase__						: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.''')
			lowerCAmelCase__						:Optional[int]         =							parser.parse_args()

			with open(args.data_file) as data_file:
						lowerCAmelCase__						:Union[str, Any]         =							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,
									)
						lowerCAmelCase__						:Any         =							dataset_json['''data''']

			with open(args.pred_file) as pred_file:
						lowerCAmelCase__						:Union[str, Any]         =							json.load(pred_file)

			lowerCAmelCase__						: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)




import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
		from .tokenization_deberta_va import DebertaVaTokenizer
else:
		lowerCAmelCase__						:int         =							None

lowerCAmelCase__						:List[str]         =							logging.get_logger(__name__)

lowerCAmelCase__						:List[str]         =							{'''vocab_file''': '''spm.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase__						:List[str]         =							{
    '''vocab_file''': {
        '''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model''',
        '''microsoft/deberta-v2-xlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'''
        ),
        '''microsoft/deberta-v2-xxlarge-mnli''': (
            '''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'''
        ),
    }
}

lowerCAmelCase__						:List[str]         =							{
    '''microsoft/deberta-v2-xlarge''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge''': 5_1_2,
    '''microsoft/deberta-v2-xlarge-mnli''': 5_1_2,
    '''microsoft/deberta-v2-xxlarge-mnli''': 5_1_2,
}

lowerCAmelCase__						:Any         =							{
    '''microsoft/deberta-v2-xlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xlarge-mnli''': {'''do_lower_case''': False},
    '''microsoft/deberta-v2-xxlarge-mnli''': {'''do_lower_case''': False},
}

class 	__a   (       UpperCAmelCase    ):
		_a			:						Tuple    			=			VOCAB_FILES_NAMES
		_a			:						int    			=			PRETRAINED_VOCAB_FILES_MAP
		_a			:						Optional[Any]    			=			PRETRAINED_INIT_CONFIGURATION
		_a			:						str    			=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a			:						Optional[int]    			=			DebertaVaTokenizer
		def __init__(    self    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE=False    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[UNK]"    ,					_SCREAMING_SNAKE_CASE="[SEP]"    ,					_SCREAMING_SNAKE_CASE="[PAD]"    ,					_SCREAMING_SNAKE_CASE="[CLS]"    ,					_SCREAMING_SNAKE_CASE="[MASK]"    ,					**_SCREAMING_SNAKE_CASE    ,					)				-> None:




									"""simple docstring"""





									super().__init__(
									    _SCREAMING_SNAKE_CASE    ,					tokenizer_file=_SCREAMING_SNAKE_CASE    ,					do_lower_case=_SCREAMING_SNAKE_CASE    ,					bos_token=_SCREAMING_SNAKE_CASE    ,					eos_token=_SCREAMING_SNAKE_CASE    ,					unk_token=_SCREAMING_SNAKE_CASE    ,					sep_token=_SCREAMING_SNAKE_CASE    ,					pad_token=_SCREAMING_SNAKE_CASE    ,					cls_token=_SCREAMING_SNAKE_CASE    ,					mask_token=_SCREAMING_SNAKE_CASE    ,					split_by_punct=_SCREAMING_SNAKE_CASE    ,					**_SCREAMING_SNAKE_CASE    ,					)

									_UpperCAmelCase					=    do_lower_case
									_UpperCAmelCase					=    split_by_punct
									_UpperCAmelCase					=    vocab_file
									_UpperCAmelCase					=    False if not self.vocab_file else True
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> str:




									"""simple docstring"""





									if token_ids_a is None:
																return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									_UpperCAmelCase					=    [self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None    ,					_SCREAMING_SNAKE_CASE=False		)				-> Any:




									"""simple docstring"""





									if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=_SCREAMING_SNAKE_CASE    ,					token_ids_a=_SCREAMING_SNAKE_CASE    ,					already_has_special_tokens=_SCREAMING_SNAKE_CASE		)

									if token_ids_a is not None:
																return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
									return [1] + ([0] * len(_SCREAMING_SNAKE_CASE		)) + [1]
		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE=None		)				-> Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=    [self.sep_token_id]
									_UpperCAmelCase					=    [self.cls_token_id]
									if token_ids_a is None:
																return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep		) * [0] + len(token_ids_a + sep		) * [1]






		def 			UpperCAmelCase__       (    self    ,					_SCREAMING_SNAKE_CASE    ,					_SCREAMING_SNAKE_CASE = None		)				-> Tuple[str]:




									"""simple docstring"""





									if not self.can_save_slow_tokenizer:
																raise ValueError(
																    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																    'tokenizer.'		)

									if not os.path.isdir(_SCREAMING_SNAKE_CASE		):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
																return
									_UpperCAmelCase					=    os.path.join(
									    _SCREAMING_SNAKE_CASE    ,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']		)

									if os.path.abspath(self.vocab_file		) != os.path.abspath(_SCREAMING_SNAKE_CASE		):
																copyfile(self.vocab_file    ,					_SCREAMING_SNAKE_CASE		)

									return (out_vocab_file,)
