




import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF

import datasets


a_				=     "\\n@inproceedings{popovic-2015-chrf,\n    title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Tenth Workshop on Statistical Machine Translation\",\n    month = sep,\n    year = \"2015\",\n    address = \"Lisbon, Portugal\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W15-3049\",\n    doi = \"10.18653/v1/W15-3049\",\n    pages = \"392--395\",\n}\n@inproceedings{popovic-2017-chrf,\n    title = \"chr{F}++: words helping character n-grams\",\n    author = \"Popovi{\'c}, Maja\",\n    booktitle = \"Proceedings of the Second Conference on Machine Translation\",\n    month = sep,\n    year = \"2017\",\n    address = \"Copenhagen, Denmark\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/W17-4770\",\n    doi = \"10.18653/v1/W17-4770\",\n    pages = \"612--618\",\n}\n@inproceedings{post-2018-call,\n    title = \"A Call for Clarity in Reporting {BLEU} Scores\",\n    author = \"Post, Matt\",\n    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",\n    month = oct,\n    year = \"2018\",\n    address = \"Belgium, Brussels\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/W18-6319\",\n    pages = \"186--191\",\n}\n"

a_				=     "\\nChrF and ChrF++ are two MT evaluation metrics. They both use the F-score statistic for character n-gram matches,\nand ChrF++ adds word n-grams as well which correlates more strongly with direct assessment. We use the implementation\nthat is already present in sacrebleu.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#chrf--chrf for more information.\n"

a_				=     "\nProduces ChrF(++) scores for hypotheses given reference translations.\n\nArgs:\n    predictions (list of str): The predicted sentences.\n    references (list of list of str): The references. There should be one reference sub-list for each prediction sentence.\n    char_order (int): Character n-gram order. Defaults to `6`.\n    word_order (int): Word n-gram order. If equals to `2`, the metric is referred to as chrF++. Defaults to `0`.\n    beta (int): Determine the importance of recall w.r.t precision. Defaults to `2`.\n    lowercase (bool): if `True`, enables case-insensitivity. Defaults to `False`.\n    whitespace (bool): If `True`, include whitespaces when extracting character n-grams.\n    eps_smoothing (bool): If `True`, applies epsilon smoothing similar\n    to reference chrF++.py, NLTK and Moses implementations. If `False`,\n    it takes into account effective match order similar to sacreBLEU < 2.0.0. Defaults to `False`.\n\nReturns:\n    \'score\' (float): The chrF (chrF++) score,\n    \'char_order\' (int): The character n-gram order,\n    \'word_order\' (int): The word n-gram order. If equals to 2, the metric is referred to as chrF++,\n    \'beta\' (int): Determine the importance of recall w.r.t precision\n\nExamples:\n    Example 1--a simple example of calculating chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction, references=reference)\n        >>> print(results)\n        {\'score\': 84.64214891738334, \'char_order\': 6, \'word_order\': 0, \'beta\': 2}\n\n    Example 2--the same example, but with the argument word_order=2, to calculate chrF++ instead of chrF:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2)\n        >>> print(results)\n        {\'score\': 82.87263732906315, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n\n    Example 3--the same chrF++ example as above, but with `lowercase=True` to normalize all case:\n        >>> prediction = [\"The relationship between cats and dogs is not exactly friendly.\", \"a good bookshop is just a genteel black hole that knows how to read.\"]\n        >>> reference = [[\"The relationship between dogs and cats is not exactly friendly.\"], [\"A good bookshop is just a genteel Black Hole that knows how to read.\"]]\n        >>> chrf = datasets.load_metric(\"chrf\")\n        >>> results = chrf.compute(predictions=prediction,\n        ...                         references=reference,\n        ...                         word_order=2,\n        ...                         lowercase=True)\n        >>> print(results)\n        {\'score\': 92.12853119829202, \'char_order\': 6, \'word_order\': 2, \'beta\': 2}\n"

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION							,			_KWARGS_DESCRIPTION   )
class  _lowerCamelCase						(			datasets.Metric   ):

      """simple docstring"""

      def       snake_case	(     self					: str				):







          if version.parse(scb.__version__				) < version.parse('''1.4.12'''				):
              raise ImportWarning(
                  '''To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn\'t match this condition.\n'''
                  '''You can install it with `pip install \"sacrebleu>=1.4.12\"`.'''				)
          return datasets.MetricInfo(
              description=_DESCRIPTION ,       citation=_CITATION ,       homepage='''https://github.com/mjpost/sacreBLEU#chrf--chrf''' ,       inputs_description=_KWARGS_DESCRIPTION ,       features=datasets.Features(
                  {
                      '''predictions''': datasets.Value('''string''' ,       id='''sequence'''				),
                      '''references''': datasets.Sequence(datasets.Value('''string''' ,       id='''sequence'''				) ,       id='''references'''				),
                  }				) ,       codebase_urls=['''https://github.com/mjpost/sacreBLEU#chrf--chrf'''] ,       reference_urls=[
                  '''https://github.com/m-popovic/chrF''',
              ] ,       )







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: str ,       snake_case					: Any ,       snake_case					: Optional[Any] = CHRF.CHAR_ORDER ,       snake_case					: str = CHRF.WORD_ORDER ,       snake_case					: int = CHRF.BETA ,       snake_case					: Optional[int] = False ,       snake_case					: Optional[int] = False ,       snake_case					: List[str] = False ,       ):
          __UpperCamelCase  		=						len(references[0]				)
          if any(len(lowerCAmelCase_				) != references_per_prediction for refs in references				):
              raise ValueError('''Sacrebleu requires the same number of references for each prediction'''				)
          __UpperCamelCase  		=						[[refs[i] for refs in references] for i in range(lowerCAmelCase_				)]

          __UpperCamelCase  		=						CHRF(lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_ ,       lowerCAmelCase_				)
          __UpperCamelCase  		=						sb_chrf.corpus_score(lowerCAmelCase_ ,       lowerCAmelCase_				)

          return {
              "score": output.score,
              "char_order": output.char_order,
              "word_order": output.word_order,
              "beta": output.beta,
          }












import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a_   		=     logging.get_logger(__name__)

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      lowerCAmelCase__			: Dict    =					["input_features", "is_longer"]







      def __init__(     self					: Dict ,       snake_case					: int=64 ,       snake_case					: Dict=48000 ,       snake_case					: Tuple=480 ,       snake_case					: Optional[Any]=10 ,       snake_case					: Union[str, Any]=1024 ,       snake_case					: Union[str, Any]=0.0 ,       snake_case					: List[str]=False ,       snake_case					: float = 0 ,       snake_case					: float = 14000 ,       snake_case					: int = None ,       snake_case					: str = "fusion" ,       snake_case					: str = "repeatpad" ,       **snake_case					: int ,       ):
          super().__init__(
              feature_size=snake_case ,       sampling_rate=snake_case ,       padding_value=snake_case ,       return_attention_mask=snake_case ,       **snake_case ,       )
          __UpperCamelCase  		=						top_db
          __UpperCamelCase  		=						truncation
          __UpperCamelCase  		=						padding
          __UpperCamelCase  		=						fft_window_size
          __UpperCamelCase  		=						(fft_window_size >> 1) + 1
          __UpperCamelCase  		=						hop_length
          __UpperCamelCase  		=						max_length_s
          __UpperCamelCase  		=						max_length_s * sampling_rate
          __UpperCamelCase  		=						sampling_rate
          __UpperCamelCase  		=						frequency_min
          __UpperCamelCase  		=						frequency_max
          __UpperCamelCase  		=						mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case ,       min_frequency=snake_case ,       max_frequency=snake_case ,       sampling_rate=snake_case ,       norm=snake_case ,       mel_scale='''htk''' ,       )
          __UpperCamelCase  		=						mel_filter_bank(
              num_frequency_bins=self.nb_frequency_bins ,       num_mel_filters=snake_case ,       min_frequency=snake_case ,       max_frequency=snake_case ,       sampling_rate=snake_case ,       norm='''slaney''' ,       mel_scale='''slaney''' ,       )







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						copy.deepcopy(self.__dict__				)
          __UpperCamelCase  		=						self.__class__.__name__
          if "mel_filters" in output:
              del output["mel_filters"]
          if "mel_filters_slaney" in output:
              del output["mel_filters_slaney"]
          return output







      def       snake_case	(     self					: Tuple ,       snake_case					: np.array ,       snake_case					: Optional[np.array] = None				):
          __UpperCamelCase  		=						spectrogram(
              snake_case ,       window_function(self.fft_window_size ,       '''hann'''				) ,       frame_length=self.fft_window_size ,       hop_length=self.hop_length ,       power=2.0 ,       mel_filters=snake_case ,       log_mel='''dB''' ,       )
          return log_mel_spectrogram.T







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Any ,       snake_case					: List[Any] ,       snake_case					: int				):
          __UpperCamelCase  		=						np.array_split(list(range(0 ,       total_frames - chunk_frames + 1				)				) ,       3				)
          if len(ranges[1]				) == 0:
              # if the audio is too short, we just use the first chunk
              __UpperCamelCase  		=						[0]
          if len(ranges[2]				) == 0:
              # if the audio is too short, we just use the first chunk
              __UpperCamelCase  		=						[0]
          # randomly choose index for each part
          __UpperCamelCase  		=						np.random.choice(ranges[0]				)
          __UpperCamelCase  		=						np.random.choice(ranges[1]				)
          __UpperCamelCase  		=						np.random.choice(ranges[2]				)

          __UpperCamelCase  		=						mel[idx_front : idx_front + chunk_frames, :]
          __UpperCamelCase  		=						mel[idx_middle : idx_middle + chunk_frames, :]
          __UpperCamelCase  		=						mel[idx_back : idx_back + chunk_frames, :]

          __UpperCamelCase  		=						torch.tensor(mel[None, None, :]				)
          __UpperCamelCase  		=						torch.nn.functional.interpolate(
              snake_case ,       size=[chunk_frames, 64] ,       mode='''bilinear''' ,       align_corners=snake_case				)
          __UpperCamelCase  		=						mel_shrink[0][0].numpy()
          __UpperCamelCase  		=						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,       axis=0				)
          return mel_fusion







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: np.array ,       snake_case					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: str				):
          if waveform.shape[0] > max_length:
              if truncation == "rand_trunc":
                  __UpperCamelCase  		=						True
                  # random crop to max_length (for compatibility) -> this should be handled by self.pad
                  __UpperCamelCase  		=						len(snake_case				) - max_length
                  __UpperCamelCase  		=						np.random.randint(0 ,       overflow + 1				)
                  __UpperCamelCase  		=						waveform[idx : idx + max_length]
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters_slaney				)[None, :]
              elif truncation == "fusion":
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters				)
                  __UpperCamelCase  		=						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                  __UpperCamelCase  		=						mel.shape[0]
                  if chunk_frames == total_frames:
                      # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                      # In this case, we just use the whole audio.
                      __UpperCamelCase  		=						np.stack([mel, mel, mel, mel] ,       axis=0				)
                      __UpperCamelCase  		=						False
                  else:
                      __UpperCamelCase  		=						self._random_mel_fusion(snake_case ,       snake_case ,       snake_case				)
                      __UpperCamelCase  		=						True
              else:
                  raise NotImplementedError(F"data_truncating {truncation} not implemented"				)

          else:
              __UpperCamelCase  		=						False
              # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
              if waveform.shape[0] < max_length:
                  if padding == "repeat":
                      __UpperCamelCase  		=						int(max_length / len(snake_case				)				)
                      __UpperCamelCase  		=						np.stack(np.tile(snake_case ,       n_repeat + 1				)				)[:max_length]
                  if padding == "repeatpad":
                      __UpperCamelCase  		=						int(max_length / len(snake_case				)				)
                      __UpperCamelCase  		=						np.stack(np.tile(snake_case ,       snake_case				)				)
                  __UpperCamelCase  		=						np.pad(snake_case ,       (0, max_length - waveform.shape[0]) ,       mode='''constant''' ,       constant_values=0				)

              if truncation == "fusion":
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters				)
                  __UpperCamelCase  		=						np.stack([input_mel, input_mel, input_mel, input_mel] ,       axis=0				)
              else:
                  __UpperCamelCase  		=						self._np_extract_fbank_features(snake_case ,       self.mel_filters_slaney				)[None, :]

          return input_mel, longer







      def __call__(     self					: List[str] ,       snake_case					: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,       snake_case					: str = None ,       snake_case					: Optional[str] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[int] = None ,       snake_case					: Optional[Union[str, TensorType]] = None ,       **snake_case					: Any ,       ):
          __UpperCamelCase  		=						truncation if truncation is not None else self.truncation
          __UpperCamelCase  		=						padding if padding else self.padding

          if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                      F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                      F" was sampled with {self.sampling_rate} and not {sampling_rate}."				)
          else:
              logger.warning(
                  '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                  '''Failing to do so can result in silent errors that might be hard to debug.'''				)

          __UpperCamelCase  		=						isinstance(snake_case ,       np.ndarray				) and len(raw_speech.shape				) > 1
          if is_batched_numpy and len(raw_speech.shape				) > 2:
              raise ValueError(F"Only mono-channel audio is supported for input to {self}"				)
          __UpperCamelCase  		=						is_batched_numpy or (
              isinstance(snake_case ,       (list, tuple)				) and (isinstance(raw_speech[0] ,       (np.ndarray, tuple, list)				))
          )

          if is_batched:
              __UpperCamelCase  		=						[np.asarray(snake_case ,       dtype=np.floataa				) for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case ,       np.ndarray				):
              __UpperCamelCase  		=						np.asarray(snake_case ,       dtype=np.floataa				)
          elif isinstance(snake_case ,       np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
              __UpperCamelCase  		=						raw_speech.astype(np.floataa				)

          # always return batch
          if not is_batched:
              __UpperCamelCase  		=						[np.asarray(snake_case				)]

          # convert to mel spectrogram, truncate and pad if needed.
          __UpperCamelCase  		=						[
              self._get_input_mel(snake_case ,       max_length if max_length else self.nb_max_samples ,       snake_case ,       snake_case				)
              for waveform in raw_speech
          ]

          __UpperCamelCase  		=						[]
          __UpperCamelCase  		=						[]
          for mel, longer in padded_inputs:
              input_mel.append(snake_case				)
              is_longer.append(snake_case				)

          if truncation == "fusion" and sum(snake_case				) == 0:
              # if no audio is longer than 10s, then randomly select one audio to be longer
              __UpperCamelCase  		=						np.random.randint(0 ,       len(snake_case				)				)
              __UpperCamelCase  		=						True

          if isinstance(input_mel[0] ,       snake_case				):
              __UpperCamelCase  		=						[np.asarray(snake_case ,       dtype=np.floataa				) for feature in input_mel]

          # is_longer is a list of bool
          __UpperCamelCase  		=						[[longer] for longer in is_longer]

          __UpperCamelCase  		=						{'''input_features''': input_mel, '''is_longer''': is_longer}
          __UpperCamelCase  		=						BatchFeature(snake_case				)

          if return_tensors is not None:
              __UpperCamelCase  		=						input_features.convert_to_tensors(snake_case				)

          return input_features






