


'''simple docstring'''







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







def _UpperCamelCase	(					__A   )							->		Any:


      '''simple docstring'''



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

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

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

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

      return white_space_fix(remove_articles(remove_punc(lower(__A   )   )   )   )







def _UpperCamelCase	(					__A				,      __A   )							->		str:


      '''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 _UpperCamelCase	(					__A				,      __A   )							->		str:


      '''simple docstring'''



      return normalize_answer(__A   ) == normalize_answer(__A   )







def _UpperCamelCase	(					__A				,      __A				,      __A   )							->		Optional[int]:


      '''simple docstring'''



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







def _UpperCamelCase	(					__A				,      __A   )							->		int:


      '''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__":
  a__  : Dict				=	'1.0'
  a__  : int				=	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__  : Optional[int]				=	parser.parse_args()

  with open(args.data_file) as data_file:
    a__  : int				=	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__  : Optional[Any]				=	dataset_json['data']

  with open(args.pred_file) as pred_file:
    a__  : str				=	json.load(pred_file)

  a__			,			a__  : Dict				=	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'''







import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
  from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

  from .test_feature_extraction_speechta import floats_list


a__  : int				=	get_tests_dir('fixtures/test_sentencepiece_bpe_char.model')
@require_torch
class  lowercase_					(   unittest.TestCase       ):


    def        __a							(					self   ):
          UpperCamelCase__  =    tempfile.mkdtemp()

          UpperCamelCase__  =    SpeechTaTokenizer(a   )
          tokenizer.save_pretrained(self.tmpdirname   )

          UpperCamelCase__  =    {
              "feature_size": 1,
              "padding_value": 0.0,
              "sampling_rate": 1_60_00,
              "do_normalize": False,
              "num_mel_bins": 80,
              "hop_length": 16,
              "win_length": 64,
              "win_function": "hann_window",
              "fmin": 80,
              "fmax": 76_00,
              "mel_floor": 1e-10,
              "reduction_factor": 2,
              "return_attention_mask": True,
          }

          UpperCamelCase__  =    os.path.join(self.tmpdirname  , a   )
          with open(self.feature_extraction_file  , "w"  , encoding="utf-8"   ) as fp:
                fp.write(json.dumps(a   ) + "\n"   )


    def        __a							(					self  , **a   ):
          return SpeechTaTokenizer.from_pretrained(self.tmpdirname  , **a   )


    def        __a							(					self  , **a   ):
          return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname  , **a   )


    def        __a							(					self   ):
          shutil.rmtree(self.tmpdirname   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_tokenizer()
          UpperCamelCase__  =    self.get_feature_extractor()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          processor.save_pretrained(self.tmpdirname   )
          UpperCamelCase__  =    SpeechTaProcessor.from_pretrained(self.tmpdirname   )

          self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer.get_vocab()   )
          self.assertIsInstance(processor.tokenizer  , a   )

          self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor.to_json_string()   )
          self.assertIsInstance(processor.feature_extractor  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=self.get_tokenizer()  , feature_extractor=self.get_feature_extractor()   )
          processor.save_pretrained(self.tmpdirname   )

          UpperCamelCase__  =    self.get_tokenizer(bos_token="(BOS)"  , eos_token="(EOS)"   )
          UpperCamelCase__  =    self.get_feature_extractor(do_normalize=a  , padding_value=1.0   )

          UpperCamelCase__  =    SpeechTaProcessor.from_pretrained(
              self.tmpdirname  , bos_token="(BOS)"  , eos_token="(EOS)"  , do_normalize=a  , padding_value=1.0   )

          self.assertEqual(processor.tokenizer.get_vocab()  , tokenizer_add_kwargs.get_vocab()   )
          self.assertIsInstance(processor.tokenizer  , a   )

          self.assertEqual(processor.feature_extractor.to_json_string()  , feature_extractor_add_kwargs.to_json_string()   )
          self.assertIsInstance(processor.feature_extractor  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    floats_list((3, 10_00)   )

          UpperCamelCase__  =    feature_extractor(audio=a  , return_tensors="np"   )
          UpperCamelCase__  =    processor(audio=a  , return_tensors="np"   )

          for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()  , input_processor[key].sum()  , delta=1e-2   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    floats_list((3, 10_00)   )

          UpperCamelCase__  =    feature_extractor(audio_target=a  , return_tensors="np"   )
          UpperCamelCase__  =    processor(audio_target=a  , return_tensors="np"   )

          for key in input_feat_extract.keys():
                self.assertAlmostEqual(input_feat_extract[key].sum()  , input_processor[key].sum()  , delta=1e-2   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    "This is a test string"

          UpperCamelCase__  =    processor(text=a   )
          UpperCamelCase__  =    tokenizer(a   )

          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]  , encoded_processor[key]   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    "This is a test string"

          UpperCamelCase__  =    processor(text_target=a   )
          UpperCamelCase__  =    tokenizer(a   )

          for key in encoded_tok.keys():
                self.assertListEqual(encoded_tok[key]  , encoded_processor[key]   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          UpperCamelCase__  =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase__  =    processor.batch_decode(a   )
          UpperCamelCase__  =    tokenizer.batch_decode(a   )

          self.assertListEqual(a  , a   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.get_feature_extractor()
          UpperCamelCase__  =    self.get_tokenizer()

          UpperCamelCase__  =    SpeechTaProcessor(tokenizer=a  , feature_extractor=a   )

          self.assertListEqual(
              processor.model_input_names  , feature_extractor.model_input_names  , msg="`processor` and `feature_extractor` model input names do not match"  , )





