





import argparse
import json
import re
import string
import sys
from collections import Counter
def 				__UpperCamelCase  (		snake_case     )  ->						int:


   '''simple docstring'''







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

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

   def remove_punc(snake_case     ):
      __A				 =       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(lowercase_     )     )     )     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						int:


   '''simple docstring'''







   __A				 =       normalize_answer(lowercase_     ).split()
   __A				 =       normalize_answer(lowercase_     ).split()
   __A				 =       Counter(lowercase_     ) & Counter(lowercase_     )
   __A				 =       sum(common.values()     )
   if num_same == 0:
      return 0
   __A				 =       1.0 * num_same / len(lowercase_     )
   __A				 =       1.0 * num_same / len(lowercase_     )
   __A				 =       (2 * precision * recall) / (precision + recall)
   return fa
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						str:


   '''simple docstring'''







   return normalize_answer(lowercase_     ) == normalize_answer(lowercase_     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case    ,	snake_case     )  ->						Optional[Any]:


   '''simple docstring'''







   __A				 =       []
   for ground_truth in ground_truths:
      __A				 =       metric_fn(lowercase_    ,	lowercase_     )
      scores_for_ground_truths.append(lowercase_     )
   return max(lowercase_     )
def 				__UpperCamelCase  (		snake_case    ,	snake_case     )  ->						Optional[int]:


   '''simple docstring'''







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

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

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

         fa += metric_max_over_ground_truths(lowercase_    ,	lowercase_    ,	lowercase_     )

   __A				 =       exact_match / total
   __A				 =       fa / total

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


if __name__ == "__main__":
    _UpperCamelCase   :							Union[str, Any]     =	"""1.0"""
    _UpperCamelCase   :							Tuple     =	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   :							Union[str, Any]     =	parser.parse_args()

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

    with open(args.pred_file) as pred_file:
        _UpperCamelCase   :							List[Any]     =	json.load(pred_file)

    _UpperCamelCase   ,  _UpperCamelCase   :							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 json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
_UpperCamelCase   :							str     =	"""stas/tiny-wmt19-en-ru"""







class 						_lowerCAmelCase(						_a		,   unittest.TestCase):

  """simple docstring"""







  lowerCamelCase__      					=   FSMTTokenizer
  lowerCamelCase__      					=   False

  def 		SCREAMING_SNAKE_CASE__	( self     )-> List[Any]:
     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     __A				 =       [
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     __A				 =       dict(zip(UpperCAmelCase  ,    range(len(UpperCAmelCase     )     )     )     )
     __A				 =       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     __A				 =       ['''en''', '''ru''']
     __A				 =       {
         '''langs''': self.langs,
         '''src_vocab_size''': 10,
         '''tgt_vocab_size''': 20,
     }

     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''src_vocab_file''']     )
     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''tgt_vocab_file''']     )
     __A				 =       os.path.join(self.tmpdirname  ,    '''tokenizer_config.json'''     )
     __A				 =       os.path.join(self.tmpdirname  ,    VOCAB_FILES_NAMES['''merges_file''']     )
     with open(self.src_vocab_file  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )
     with open(self.tgt_vocab_file  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )
     with open(self.merges_file  ,    '''w'''     ) as fp:
        fp.write('''\n'''.join(UpperCAmelCase     )     )
     with open(UpperCAmelCase  ,    '''w'''     ) as fp:
        fp.write(json.dumps(UpperCAmelCase     )     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''     )

  @cached_property
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     return FSMTTokenizer.from_pretrained('''facebook/wmt19-en-ru'''     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       FSMTTokenizer.from_pretrained(UpperCAmelCase     )
     self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]  ,    ['''en''', '''ru''']     )
     self.assertEqual(tokenizer.src_vocab_size  ,    21     )
     self.assertEqual(tokenizer.tgt_vocab_size  ,    21     )

  def 		SCREAMING_SNAKE_CASE__	( self     )-> Union[str, Any]:
     __A				 =       FSMTTokenizer(self.langs  ,    self.src_vocab_file  ,    self.tgt_vocab_file  ,    self.merges_file     )

     __A				 =       '''lower'''
     __A				 =       ['''low''', '''er</w>''']
     __A				 =       tokenizer.tokenize(UpperCAmelCase     )
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

     __A				 =       tokens + ['''<unk>''']
     __A				 =       [14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase     )  ,    UpperCAmelCase     )

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Any:
     __A				 =       self.tokenizer_ru_en

     __A				 =       tokenizer.encode('''sequence builders'''  ,    add_special_tokens=UpperCAmelCase     )
     __A				 =       tokenizer.encode('''multi-sequence build'''  ,    add_special_tokens=UpperCAmelCase     )

     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase     )
     __A				 =       tokenizer.build_inputs_with_special_tokens(UpperCAmelCase  ,    UpperCAmelCase     )

     assert encoded_sentence == text + [2]
     assert encoded_pair == text + [2] + text_a + [2]

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Dict:
     __A				 =       self.tokenizer_en_ru
     __A				 =       self.tokenizer_ru_en

     __A				 =       [
         [
             '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
             [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
         ],
         ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 2]],
     ]

     # if data needs to be recreated or added, run:
     # import torch
     # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
     # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

     for src_text, tgt_input_ids in targets:
        __A				 =       tokenizer_enc.encode(UpperCAmelCase  ,    return_tensors=UpperCAmelCase     )
        self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

        # and decode backward, using the reversed languages model
        __A				 =       tokenizer_dec.decode(UpperCAmelCase  ,    skip_special_tokens=UpperCAmelCase     )
        self.assertEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  @slow
  def 		SCREAMING_SNAKE_CASE__	( self     )-> str:
     __A				 =       FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''  ,    do_lower_case=UpperCAmelCase     )
     __A				 =       tokenizer.tokenize('''USA is United States of America'''     )
     __A				 =       ['''us''', '''a</w>''', '''is</w>''', '''un''', '''i''', '''ted</w>''', '''st''', '''ates</w>''', '''of</w>''', '''am''', '''er''', '''ica</w>''']
     self.assertListEqual(UpperCAmelCase  ,    UpperCAmelCase     )

  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[int]:
     pass





  @unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
  def 		SCREAMING_SNAKE_CASE__	( self     )-> Optional[Any]:
     pass

