"""simple docstring"""


import numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
  import librosa  # noqa: E402

  __UpperCamelCase       :    List[Any]         =		True
  __UpperCamelCase       :    str         =		''''''
except Exception as e:
  __UpperCamelCase       :    Tuple         =		False
  __UpperCamelCase       :    Optional[Any]         =		(
      f'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
  )


from PIL import Image  # noqa: E402
class   UpperCAmelCase_	(				snake_case_,    snake_case_   ):
    snake_case_											=      """mel_config.json"""
    @register_to_config
    def __init__(	self			:       Optional[int]   ,						_lowercase			:       List[Any] = 2_5_6   ,						_lowercase			:       Optional[Any] = 2_5_6   ,						_lowercase			:       Tuple = 2_2_0_5_0   ,						_lowercase			:       str = 2_0_4_8   ,						_lowercase			:       str = 5_1_2   ,						_lowercase			:       Optional[Any] = 8_0   ,						_lowercase			:       Optional[int] = 3_2   ,						)						->    Optional[Any]:
       _lowercase							     =       hop_length
       _lowercase							     =       sample_rate
       _lowercase							     =       n_fft
       _lowercase							     =       top_db
       _lowercase							     =       n_iter
       self.set_resolution(UpperCAmelCase__   ,						UpperCAmelCase__		)
       _lowercase							     =       None

       if not _librosa_can_be_imported:
          raise ValueError(_import_error		)
    def 		_lowerCamelCase      (	self			:       Dict   ,						_lowercase			:       Any   ,						_lowercase			:       Any		)						->    List[str]:
       _lowercase							     =       x_res
       _lowercase							     =       y_res
       _lowercase							     =       self.y_res
       _lowercase							     =       self.x_res * self.hop_length - 1
    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       Optional[int] = None   ,						_lowercase			:       List[Any] = None		)						->    Optional[int]:
       if audio_file is not None:
          _lowercase							     =       librosa.load(UpperCAmelCase__   ,						mono=UpperCAmelCase__   ,						sr=self.sr		)
       else:
          _lowercase							     =       raw_audio

       # Pad with silence if necessary.
       if len(self.audio		) < self.x_res * self.hop_length:
          _lowercase							     =       np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio		),)		)]		)
    def 		_lowerCamelCase      (	self			:       int		)						->    int:
       return len(self.audio		) // self.slice_size
    def 		_lowerCamelCase      (	self			:       Tuple   ,						_lowercase			:       Tuple = 0		)						->    np.ndarray:
       return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]
    def 		_lowerCamelCase      (	self			:       int		)						->    int:
       return self.sr
    def 		_lowerCamelCase      (	self			:       str   ,						_lowercase			:       Optional[Any]		)						->    Image.Image:
       _lowercase							     =       librosa.feature.melspectrogram(
           y=self.get_audio_slice(UpperCAmelCase__		)   ,						sr=self.sr   ,						n_fft=self.n_fft   ,						hop_length=self.hop_length   ,						n_mels=self.n_mels		)
       _lowercase							     =       librosa.power_to_db(UpperCAmelCase__   ,						ref=np.max   ,						top_db=self.top_db		)
       _lowercase							     =       (((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0   ,						2_5_5		) + 0.5).astype(np.uinta		)
       _lowercase							     =       Image.fromarray(UpperCAmelCase__		)
       return image




    def 		_lowerCamelCase      (	self			:       List[Any]   ,						_lowercase			:       Union[str, Any]		)						->    np.ndarray:
       _lowercase							     =       np.frombuffer(image.tobytes()   ,						dtype="uint8"		).reshape((image.height, image.width)		)
       _lowercase							     =       bytedata.astype("float"		) * self.top_db / 2_5_5 - self.top_db
       _lowercase							     =       librosa.db_to_power(UpperCAmelCase__		)
       _lowercase							     =       librosa.feature.inverse.mel_to_audio(
           UpperCAmelCase__   ,						sr=self.sr   ,						n_fft=self.n_fft   ,						hop_length=self.hop_length   ,						n_iter=self.n_iter		)
       return audio






'''simple docstring'''






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





def      lowercase  (  lowerCAmelCase :						int):



				"""simple docstring"""


				def remove_articles(lowerCAmelCase :						List[Any]):
								return re.sub(R'''\b(a|an|the)\b'''   , ''' '''   , lowerCAmelCase)

				def white_space_fix(lowerCAmelCase :						Tuple):
								return " ".join(text.split())

				def remove_punc(lowerCAmelCase :						str):
								_A :				List[Any]				=  set(string.punctuation)
								return "".join(ch for ch in text if ch not in exclude)

				def lower(lowerCAmelCase :						Any):
								return text.lower()

				return white_space_fix(remove_articles(remove_punc(lower(lowerCAmelCase))))





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Dict):



				"""simple docstring"""


				_A :				str				=  normalize_answer(lowerCAmelCase).split()
				_A :				Dict				=  normalize_answer(lowerCAmelCase).split()
				_A :				str				=  Counter(lowerCAmelCase) & Counter(lowerCAmelCase)
				_A :				List[Any]				=  sum(common.values())
				if num_same == 0:
								return 0
				_A :				Tuple				=  1.0 * num_same / len(lowerCAmelCase)
				_A :				List[Any]				=  1.0 * num_same / len(lowerCAmelCase)
				_A :				int				=  (2 * precision * recall) / (precision + recall)
				return fa





def      lowercase  (  lowerCAmelCase :						Any   , lowerCAmelCase :						str):



				"""simple docstring"""


				return normalize_answer(lowerCAmelCase) == normalize_answer(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Optional[int]   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				_A :				Optional[int]				=  []
				for ground_truth in ground_truths:
								_A :				Union[str, Any]				=  metric_fn(lowerCAmelCase   , lowerCAmelCase)
								scores_for_ground_truths.append(lowerCAmelCase)
				return max(lowerCAmelCase)





def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						str):



				"""simple docstring"""


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

												_A :				Optional[int]				=  [x['''text'''] for x in qa['''answers''']]
												_A :				Union[str, Any]				=  predictions[qa['''id''']]

												_A :				Optional[Any]				=  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)

				_A :				Dict				=  exact_match / total
				_A :				Dict				=  fa / total

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


if __name__ == "__main__":
	__UpperCamelCase      :   List[str]								=     '''1.0'''
	__UpperCamelCase      :   Union[str, 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      :   Optional[int]								=     parser.parse_args()

	with open(args.data_file) as data_file:
		__UpperCamelCase      :   Optional[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,
			)
		__UpperCamelCase      :   Optional[Any]								=     dataset_json['''data''']

	with open(args.pred_file) as pred_file:
		__UpperCamelCase      :   int								=     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)
