"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


lowercase__					       =	logging.get_logger(__name__)




class    __snake_case       (     SCREAMING_SNAKE_CASE__				):
      a__        =		["input_features"]



      def __init__(						self    ,			lowercase=80    ,			lowercase=1_60_00    ,			lowercase=1_60    ,			lowercase=30    ,			lowercase=4_00    ,			lowercase=0.0    ,			lowercase=False    ,			**lowercase    ,			)	->			Tuple:





        '''simple docstring'''





        super().__init__(
            feature_size=lowerCamelCase__    ,			sampling_rate=lowerCamelCase__    ,			padding_value=lowerCamelCase__    ,			return_attention_mask=lowerCamelCase__    ,			**lowerCamelCase__    ,			)
        a__:     Optional[int]										=	n_fft
        a__:     str										=	hop_length
        a__:     Dict										=	chunk_length
        a__:     List[Any]										=	chunk_length * sampling_rate
        a__:     Any										=	self.n_samples // hop_length
        a__:     str										=	sampling_rate
        a__:     Dict										=	mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2    ,			num_mel_filters=lowerCamelCase__    ,			min_frequency=0.0    ,			max_frequency=8000.0    ,			sampling_rate=lowerCamelCase__    ,			norm='slaney'    ,			mel_scale='slaney'    ,			)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			Tuple:





        '''simple docstring'''





        a__:     Tuple										=	spectrogram(
            lowerCamelCase__    ,			window_function(self.n_fft    ,			'hann')    ,			frame_length=self.n_fft    ,			hop_length=self.hop_length    ,			power=2.0    ,			mel_filters=self.mel_filters    ,			log_mel='log10'    ,			)
        a__:     Optional[int]										=	log_spec[:, :-1]
        a__:     Union[str, Any]										=	np.maximum(lowerCamelCase__    ,			log_spec.max() - 8.0)
        a__:     Tuple										=	(log_spec + 4.0) / 4.0
        return log_spec



      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def   lowerCamelCase_   (						lowercase    ,			lowercase    ,			lowercase = 0.0)	->			str:





        '''simple docstring'''





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

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

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

        return normed_input_values



      def __call__(						self    ,			lowercase    ,			lowercase = True    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			lowercase = "max_length"    ,			lowercase = None    ,			lowercase = None    ,			lowercase = None    ,			**lowercase    ,			)	->			str:





        '''simple docstring'''





        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.')

        a__:     Optional[Any]										=	isinstance(lowerCamelCase__    ,			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}')
        a__:     Union[str, Any]										=	is_batched_numpy or (
            isinstance(lowerCamelCase__    ,			(list, tuple)) and (isinstance(raw_speech[0]    ,			(np.ndarray, tuple, list)))
        )

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

        # always return batch
        if not is_batched:
          a__:     Optional[Any]										=	[np.asarray([raw_speech]).T]

        a__:     Optional[Any]										=	BatchFeature({'input_features': raw_speech})

        # convert into correct format for padding

        a__:     Union[str, Any]										=	self.pad(
            lowerCamelCase__    ,			padding=lowerCamelCase__    ,			max_length=max_length if max_length else self.n_samples    ,			truncation=lowerCamelCase__    ,			pad_to_multiple_of=lowerCamelCase__    ,			return_attention_mask=return_attention_mask or do_normalize    ,			)

        # zero-mean and unit-variance normalization
        if do_normalize:
          a__:     List[str]										=	self.zero_mean_unit_var_norm(
              padded_inputs['input_features']    ,			attention_mask=padded_inputs['attention_mask']    ,			padding_value=self.padding_value    ,			)
          a__:     int										=	np.stack(padded_inputs['input_features']    ,			axis=0)

        # make sure list is in array format
        a__:     Optional[int]										=	padded_inputs.get('input_features').transpose(2    ,			0    ,			1)

        a__:     List[str]										=	[self._np_extract_fbank_features(lowerCamelCase__) for waveform in input_features[0]]

        if isinstance(input_features[0]    ,			lowerCamelCase__):
          a__:     Optional[int]										=	[np.asarray(lowerCamelCase__    ,			dtype=np.floataa) for feature in input_features]
        else:
          a__:     Dict										=	input_features

        if return_attention_mask:
          # rescale from sample (48000) to feature (3000)
          a__:     Optional[int]										=	padded_inputs["""attention_mask"""][:, :: self.hop_length]

        if return_tensors is not None:
          a__:     Optional[int]										=	padded_inputs.convert_to_tensors(lowerCamelCase__)

        return padded_inputs



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





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







from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


SCREAMING_SNAKE_CASE_:Any             =						[
    """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"""
    """ final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"""
    """ depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.""",
    """The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"""
    """ accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"""
    """ founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"""
    """ body.""",
    """Amnesty International releases its annual report on the death penalty. The report catalogs the use of"""
    """ state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"""
    """ world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"""
    """ punishment.""",
]

SCREAMING_SNAKE_CASE_:Optional[int]             =						[
    """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."""
    """ Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"""
    """ had informed his Lufthansa training school of an episode of severe depression, airline says .""",
    """Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."""
    """ Israel and the United States opposed the move, which could open the door to war crimes investigations against"""
    """ Israelis .""",
    """Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"""
    """ death . Organization claims that governments around the world are using the threat of terrorism to advance"""
    """ executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"""
    """ sentences up by 28% .""",
]




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2"""]		)
    assert (
        pd.DataFrame(no_aggregation["""rouge2"""]		).fmeasure.mean()
        == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]		).fmeasure.mean()
    )




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .""",
    ]
    A :       Optional[Any]          =   [
        """Margot Frank, died in 1945, a month earlier than previously thought.""",
        """Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"""
        """ the final seconds on board Flight 9525.""",
    ]
    assert calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		) == calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase		)




def 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" """
    ]
    A :       Union[str, Any]          =   [
        """ Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."""
    ]

    A :       int          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]      ,     newline_sep=_lowerCAmelCase		)["""rougeLsum"""]
    A :       Optional[Any]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     rouge_keys=["""rougeLsum"""]		)["""rougeLsum"""]
    assert new_score > prev_score




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


