





import sacrebleu as scb
from packaging import version
from sacrebleu import TER

import datasets


SCREAMING_SNAKE_CASE__							:      List[Any]         =    """\
@inproceedings{snover-etal-2006-study,
    title = \"A Study of Translation Edit Rate with Targeted Human Annotation\",
    author = \"Snover, Matthew  and
      Dorr, Bonnie  and
      Schwartz, Rich  and
      Micciulla, Linnea  and
      Makhoul, John\",
    booktitle = \"Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers\",
    month = aug # \" 8-12\",
    year = \"2006\",
    address = \"Cambridge, Massachusetts, USA\",
    publisher = \"Association for Machine Translation in the Americas\",
    url = \"https://aclanthology.org/2006.amta-papers.25\",
    pages = \"223--231\",
}
@inproceedings{post-2018-call,
    title = \"A Call for Clarity in Reporting {BLEU} Scores\",
    author = \"Post, Matt\",
    booktitle = \"Proceedings of the Third Conference on Machine Translation: Research Papers\",
    month = oct,
    year = \"2018\",
    address = \"Belgium, Brussels\",
    publisher = \"Association for Computational Linguistics\",
    url = \"https://www.aclweb.org/anthology/W18-6319\",
    pages = \"186--191\",
}
"""

SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """\
TER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a
hypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu
(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found
here: https://github.com/jhclark/tercom.

The implementation here is slightly different from sacrebleu in terms of the required input format. The length of
the references and hypotheses lists need to be the same, so you may need to transpose your references compared to
sacrebleu's required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534

See the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.
"""

SCREAMING_SNAKE_CASE__							:      int         =    """
Produces TER scores alongside the number of edits and reference length.

Args:
    predictions (list of str): The system stream (a sequence of segments).
    references (list of list of str): A list of one or more reference streams (each a sequence of segments).
    normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.
    support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,
                                    as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.
                                    Only applies if `normalized = True`. Defaults to `False`.
    case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.

Returns:
    'score' (float): TER score (num_edits / sum_ref_lengths * 100)
    'num_edits' (int): The cumulative number of edits
    'ref_length' (float): The cumulative average reference length

Examples:
    Example 1:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\",
        ...                     \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 150.0, 'num_edits': 15, 'ref_length': 10.0}

    Example 2:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 62.5, 'num_edits': 5, 'ref_length': 8.0}

    Example 3:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         normalized=True,
        ...                         case_sensitive=True)
        >>> print(results)
        {'score': 57.14285714285714, 'num_edits': 6, 'ref_length': 10.5}

    Example 4:
        >>> predictions = [\"does this sentence match??\",
        ...                     \"what about this sentence?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 0.0, 'num_edits': 0, 'ref_length': 8.0}

    Example 5:
        >>> predictions = [\"does this sentence match??\",
        ...                    \"what about this sentence?\",
        ...                    \"What did the TER metric user say to the developer?\"]
        >>> references = [[\"does this sentence match\", \"does this sentence match!?!\"],
        ...             [\"wHaT aBoUt ThIs SeNtEnCe?\", \"wHaT aBoUt ThIs SeNtEnCe?\"],
        ...             [\"Your jokes are...\", \"...TERrible\"]]
        >>> ter = datasets.load_metric(\"ter\")
        >>> results = ter.compute(predictions=predictions,
        ...                         references=references,
        ...                         ignore_punct=True,
        ...                         case_sensitive=False)
        >>> print(results)
        {'score': 100.0, 'num_edits': 10, 'ref_length': 10.0}
"""







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





   def 						_snake_case			(			self   )					->	Optional[int]:
      """simple docstring"""





      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="http://www.cs.umd.edu/~snover/tercom/"       ,      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#ter"]       ,      reference_urls=[
              "https://github.com/jhclark/tercom",
          ]       ,      )





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      snake_case = False       ,      )					->	Optional[Any]:
      """simple docstring"""





      a__						:    int   		=	len(references[0]   )
      if any(len(snake_case   ) != references_per_prediction for refs in references   ):
         raise ValueError("Sacrebleu requires the same number of references for each prediction"   )
      a__						:    int   		=	[[refs[i] for refs in references] for i in range(snake_case   )]

      a__						:    Optional[Any]   		=	TER(
          normalized=snake_case       ,      no_punct=snake_case       ,      asian_support=snake_case       ,      case_sensitive=snake_case       ,      )
      a__						:    Union[str, Any]   		=	sb_ter.corpus_score(snake_case       ,      snake_case   )

      return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}








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

import numpy as np

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


SCREAMING_SNAKE_CASE__							:      Optional[int]         =    logging.get_logger(__name__)







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					["""input_values""", """attention_mask"""]





   def __init__(			self       ,      snake_case = 1       ,      snake_case = 16_000       ,      snake_case = 0.0       ,      snake_case = False       ,      snake_case = 80       ,      snake_case = 16       ,      snake_case = 64       ,      snake_case = "hann_window"       ,      snake_case = 1.0       ,      snake_case = 80       ,      snake_case = 7_600       ,      snake_case = 1E-10       ,      snake_case = 2       ,      snake_case = True       ,      **snake_case       ,      )					->	Dict:
      """simple docstring"""





      super().__init__(feature_size=snake_case       ,      sampling_rate=snake_case       ,      padding_value=snake_case       ,      **snake_case   )
      a__						:    Any   		=	do_normalize
      a__						:    List[str]   		=	return_attention_mask

      a__						:    List[Any]   		=	num_mel_bins
      a__						:    List[str]   		=	hop_length
      a__						:    int   		=	win_length
      a__						:    List[Any]   		=	win_function
      a__						:    List[str]   		=	frame_signal_scale
      a__						:    List[Any]   		=	fmin
      a__						:    Optional[Any]   		=	fmax
      a__						:    Union[str, Any]   		=	mel_floor
      a__						:    Union[str, Any]   		=	reduction_factor

      a__						:    List[str]   		=	win_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	hop_length * sampling_rate // 1_000
      a__						:    List[Any]   		=	optimal_fft_length(self.sample_size   )
      a__						:    Dict   		=	(self.n_fft // 2) + 1

      a__						:    str   		=	window_function(window_length=self.sample_size       ,      name=self.win_function       ,      periodic=snake_case   )

      a__						:    Tuple   		=	mel_filter_bank(
          num_frequency_bins=self.n_freqs       ,      num_mel_filters=self.num_mel_bins       ,      min_frequency=self.fmin       ,      max_frequency=self.fmax       ,      sampling_rate=self.sampling_rate       ,      norm="slaney"       ,      mel_scale="slaney"       ,      )

      if frame_signal_scale != 1.0:
         warnings.warn(
             "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )
      if reduction_factor != 2.0:
         warnings.warn(
             "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"       ,      snake_case       ,      )





   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def 						_snake_case			(			snake_case       ,      snake_case       ,      snake_case = 0.0   )					->	List[np.ndarray]:
      """simple docstring"""





      if attention_mask is not None:
         a__						:    Tuple   		=	np.array(snake_case       ,      np.intaa   )
         a__						:    List[str]   		=	[]

         for vector, length in zip(snake_case       ,      attention_mask.sum(-1   )   ):
            a__						:    List[Any]   		=	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7   )
            if length < normed_slice.shape[0]:
               a__						:    List[str]   		=	padding_value

            normed_input_values.append(snake_case   )
      else:
         a__						:    Any   		=	[(x - x.mean()) / np.sqrt(x.var() + 1E-7   ) for x in input_values]

      return normed_input_values





   def 						_snake_case			(			self       ,      snake_case       ,      )					->	np.ndarray:
      """simple docstring"""





      a__						:    str   		=	spectrogram(
          snake_case       ,      window=self.window       ,      frame_length=self.sample_size       ,      hop_length=self.sample_stride       ,      fft_length=self.n_fft       ,      mel_filters=self.mel_filters       ,      mel_floor=self.mel_floor       ,      log_mel="log10"       ,      )
      return log_mel_spec.T





   def __call__(			self       ,      snake_case = None       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





      if audio is None and audio_target is None:
         raise ValueError("You must provide either `audio` or `audio_target` values."   )

      if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
            raise ValueError(
                F"""The model corresponding to this feature extractor: {self} was trained using a sampling rate of"""
                F""" {self.sampling_rate}. Please make sure that the provided audio input was sampled with"""
                F""" {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."   )

      if audio is not None:
         a__						:    Dict   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )
      else:
         a__						:    Optional[int]   		=	None

      if audio_target is not None:
         a__						:    List[Any]   		=	self._process_audio(
             snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      snake_case       ,      **snake_case       ,      )

         if inputs is None:
            return inputs_target
         else:
            a__						:    Tuple   		=	inputs_target["input_values"]
            a__						:    Tuple   		=	inputs_target.get("attention_mask"   )
            if decoder_attention_mask is not None:
               a__						:    Tuple   		=	decoder_attention_mask

      return inputs





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = False       ,      snake_case = False       ,      snake_case = None       ,      snake_case = False       ,      snake_case = None       ,      snake_case = None       ,      snake_case = None       ,      **snake_case       ,      )					->	BatchFeature:
      """simple docstring"""





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

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

      # always return batch
      if not is_batched:
         a__						:    Union[str, Any]   		=	[speech]

      # needed to make pad() work on spectrogram inputs
      a__						:    Optional[Any]   		=	self.feature_size

      # convert into correct format for padding
      if is_target:
         a__						:    List[str]   		=	[self._extract_mel_features(snake_case   ) for waveform in speech]
         a__						:    Optional[Any]   		=	BatchFeature({"input_values": features}   )
         a__						:    str   		=	self.num_mel_bins
      else:
         a__						:    int   		=	BatchFeature({"input_values": speech}   )

      a__						:    int   		=	self.pad(
          snake_case       ,      padding=snake_case       ,      max_length=snake_case       ,      truncation=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_attention_mask=snake_case       ,      **snake_case       ,      )

      a__						:    Any   		=	feature_size_hack

      # convert input values to correct format
      a__						:    Tuple   		=	padded_inputs["input_values"]
      if not isinstance(input_values[0]       ,      np.ndarray   ):
         a__						:    int   		=	[np.asarray(snake_case       ,      dtype=np.floataa   ) for array in input_values]
      elif (
          not isinstance(snake_case       ,      np.ndarray   )
          and isinstance(input_values[0]       ,      np.ndarray   )
          and input_values[0].dtype is np.dtype(np.floataa   )
      ):
         a__						:    Union[str, Any]   		=	[array.astype(np.floataa   ) for array in input_values]
      elif isinstance(snake_case       ,      np.ndarray   ) and input_values.dtype is np.dtype(np.floataa   ):
         a__						:    Optional[int]   		=	input_values.astype(np.floataa   )

      # convert attention_mask to correct format
      a__						:    Optional[int]   		=	padded_inputs.get("attention_mask"   )
      if attention_mask is not None:
         a__						:    Tuple   		=	[np.asarray(snake_case       ,      dtype=np.intaa   ) for array in attention_mask]

      # zero-mean and unit-variance normalization
      if not is_target and self.do_normalize:
         a__						:    Any   		=	(
             attention_mask
             if self._get_padding_strategies(snake_case       ,      max_length=snake_case   ) is not PaddingStrategy.DO_NOT_PAD
             else None
         )
         a__						:    Optional[Any]   		=	self.zero_mean_unit_var_norm(
             padded_inputs["input_values"]       ,      attention_mask=snake_case       ,      padding_value=self.padding_value   )

      if return_tensors is not None:
         a__						:    int   		=	padded_inputs.convert_to_tensors(snake_case   )

      return padded_inputs





   def 						_snake_case			(			self   )					->	Dict[str, Any]:
      """simple docstring"""





      a__						:    int   		=	super().to_dict()

      # Don't serialize these as they are derived from the other properties.
      a__						:    str   		=	["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
      for name in names:
         if name in output:
            del output[name]

      return output

