






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






def    UpperCAmelCase__	(						lowerCamelCase  : Union[str, Any]						):







   def remove_articles(lowerCamelCase  : List[str]						):
      return re.sub(R"""\b(a|an|the)\b""" ,""" """ ,lowerCamelCase						)

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

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

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

   return white_space_fix(remove_articles(remove_punc(lower(lowerCamelCase						)						)						)						)






def    UpperCAmelCase__	(						lowerCamelCase  : Any ,lowerCamelCase  : Optional[int]						):
   lowerCamelCase											=normalize_answer(lowerCamelCase						).split()
   lowerCamelCase											=normalize_answer(lowerCamelCase						).split()
   lowerCamelCase											=Counter(lowerCamelCase						) & Counter(lowerCamelCase						)
   lowerCamelCase											=sum(common.values()						)
   if num_same == 0:
      return 0
   lowerCamelCase											=1.0 * num_same / len(lowerCamelCase						)
   lowerCamelCase											=1.0 * num_same / len(lowerCamelCase						)
   lowerCamelCase											=(2 * precision * recall) / (precision + recall)
   return fa






def    UpperCAmelCase__	(						lowerCamelCase  : Tuple ,lowerCamelCase  : int						):
   return normalize_answer(lowerCamelCase						) == normalize_answer(lowerCamelCase						)






def    UpperCAmelCase__	(						lowerCamelCase  : Tuple ,lowerCamelCase  : Any ,lowerCamelCase  : Union[str, Any]						):
   lowerCamelCase											=[]
   for ground_truth in ground_truths:
      lowerCamelCase											=metric_fn(lowerCamelCase ,lowerCamelCase						)
      scores_for_ground_truths.append(lowerCamelCase						)
   return max(lowerCamelCase						)






def    UpperCAmelCase__	(						lowerCamelCase  : Tuple ,lowerCamelCase  : Union[str, Any]						):
   lowerCamelCase											=lowerCamelCase											=lowerCamelCase											=0
   lowerCamelCase											=[]
   for passage in dataset:
      for qa in passage["qas"]:
         total += 1
         if qa["id"] not in predictions:
            lowerCamelCase											=f'Unanswered question {qa["id"]} will receive score 0.'
            print(lowerCamelCase ,file=sys.stderr						)
            continue

         lowerCamelCase											=[x["""text"""] for x in qa["""answers"""]]
         lowerCamelCase											=predictions[qa["""id"""]]

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

         fa += metric_max_over_ground_truths(lowerCamelCase ,lowerCamelCase ,lowerCamelCase						)

   lowerCamelCase											=exact_match / total
   lowerCamelCase											=fa / total

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


if __name__ == "__main__":
   _UpperCAmelCase				:	List[Any]    						=    "1.0"
   _UpperCAmelCase				:	List[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.')
   _UpperCAmelCase				:	List[str]    						=    parser.parse_args()

   with open(args.data_file) as data_file:
      _UpperCAmelCase				:	Tuple    						=    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,
         )
      _UpperCAmelCase				:	int    						=    dataset_json["data"]

   with open(args.pred_file) as pred_file:
      _UpperCAmelCase				:	List[str]    						=    json.load(pred_file)

   _UpperCAmelCase				:	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 unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_UpperCAmelCase				:	Any    						=    get_tests_dir('fixtures/spiece.model')


@require_sentencepiece
@require_tokenizers
class 		lowerCAmelCase_							(     UpperCamelCase							,    unittest.TestCase   ):
     __lowerCamelCase					:						List[Any]     =    ErnieMTokenizer
     __lowerCamelCase					:						Optional[Any]     =    False
     __lowerCamelCase					:						Optional[int]     =    True
     __lowerCamelCase					:						str     =    False
     __lowerCamelCase					:						Any     =    False





     def 	lowerCamelCase       (  self     )		->							Any:
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowerCamelCase											=ErnieMTokenizer(_UpperCAmelCase       ,   unk_token="""<unk>"""       ,   pad_token="""<pad>"""     )
        tokenizer.save_pretrained(self.tmpdirname     )





     def 	lowerCamelCase       (  self       ,   _UpperCAmelCase     )		->							Union[str, Any]:
        lowerCamelCase											="""this is a test"""
        lowerCamelCase											="""this is a test"""
        return input_text, output_text





     def 	lowerCamelCase       (  self     )		->							List[Any]:
        lowerCamelCase											="""<pad>"""
        lowerCamelCase											=0

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase     )       ,   _UpperCAmelCase     )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase     )       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							List[str]:
        lowerCamelCase											=list(self.get_tokenizer().get_vocab().keys()     )

        self.assertEqual(vocab_keys[0]       ,   """<pad>"""     )
        self.assertEqual(vocab_keys[1]       ,   """<unk>"""     )
        self.assertEqual(vocab_keys[-1]       ,   """▁eloquent"""     )
        self.assertEqual(len(_UpperCAmelCase     )       ,   30000     )





     def 	lowerCamelCase       (  self     )		->							Any:
        self.assertEqual(self.get_tokenizer().vocab_size       ,   30000     )





     def 	lowerCamelCase       (  self     )		->							Any:
        if not self.test_rust_tokenizer:
           return

        lowerCamelCase											=self.get_tokenizer()
        lowerCamelCase											=self.get_rust_tokenizer()

        lowerCamelCase											="""I was born in 92000, and this is falsé."""

        lowerCamelCase											=tokenizer.tokenize(_UpperCAmelCase     )
        lowerCamelCase											=rust_tokenizer.tokenize(_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )

        lowerCamelCase											=tokenizer.encode(_UpperCAmelCase       ,   add_special_tokens=_UpperCAmelCase     )
        lowerCamelCase											=rust_tokenizer.encode(_UpperCAmelCase       ,   add_special_tokens=_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )

        lowerCamelCase											=self.get_rust_tokenizer()
        lowerCamelCase											=tokenizer.encode(_UpperCAmelCase     )
        lowerCamelCase											=rust_tokenizer.encode(_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   _UpperCAmelCase     )





     def 	lowerCamelCase       (  self     )		->							Union[str, Any]:
        lowerCamelCase											=ErnieMTokenizer(_UpperCAmelCase       ,   do_lower_case=_UpperCAmelCase       ,   unk_token="""<unk>"""       ,   pad_token="""<pad>"""     )

        lowerCamelCase											=tokenizer.tokenize("""This is a test"""     )
        self.assertListEqual(_UpperCAmelCase       ,   ["""▁this""", """▁is""", """▁a""", """▁test"""]     )

        self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )       ,   [48, 25, 21, 1289]     )

        lowerCamelCase											=tokenizer.tokenize("""I was born in 92000, and this is falsé."""     )
        # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
        self.assertListEqual(
            _UpperCAmelCase       ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]     )
        lowerCamelCase											=tokenizer.convert_tokens_to_ids(_UpperCAmelCase     )
        self.assertListEqual(_UpperCAmelCase       ,   [31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]     )

        lowerCamelCase											=tokenizer.convert_ids_to_tokens(_UpperCAmelCase     )
        self.assertListEqual(
            _UpperCAmelCase       ,   ["""▁i""", """▁was""", """▁born""", """▁in""", """9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]       ,   )





     def 	lowerCamelCase       (  self     )		->							List[str]:
        lowerCamelCase											=ErnieMTokenizer(_UpperCAmelCase       ,   unk_token="""<unk>"""       ,   pad_token="""<pad>"""     )

        lowerCamelCase											=tokenizer.encode("""sequence builders"""     )
        lowerCamelCase											=tokenizer.encode("""multi-sequence build"""     )

        lowerCamelCase											=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase     )
        lowerCamelCase											=tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase       ,   _UpperCAmelCase     )

        assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
        assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
            tokenizer.sep_token_id
        ] + text_a + [tokenizer.sep_token_id]





     @slow
     def 	lowerCamelCase       (  self     )		->							Dict:
        # fmt: off
        lowerCamelCase											={"""input_ids""": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=_UpperCAmelCase       ,   model_name="""susnato/ernie-m-base_pytorch"""       ,   sequences=[
                """Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides """
                """general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural """
                """Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained """
                """models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.""",
                """BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly """
                """conditioning on both left and right context in all layers.""",
                """The quick brown fox jumps over the lazy dog.""",
            ]       ,   )