



from math import ceil
from typing import List, Optional, Union

import numpy as np

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


A        =			logging.get_logger(__name__)


class 							__a							(  UpperCamelCase_						):





		'''simple docstring'''



		UpperCAmelCase__      :				Optional[int]				    =						["""audio_values""", """audio_mask"""]


		def __init__(						self ,    UpperCamelCase__=2048 ,    UpperCamelCase__=1 ,    UpperCamelCase__=[16, 16] ,    UpperCamelCase__=128 ,    UpperCamelCase__=44100 ,    UpperCamelCase__=86 ,    UpperCamelCase__=2048 ,    UpperCamelCase__=0.0 ,    **UpperCamelCase__ ,    ):
						super().__init__(
						    feature_size=UpperCamelCase__ ,    sampling_rate=UpperCamelCase__ ,    padding_value=UpperCamelCase__ ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   spectrogram_length
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   num_channels
						SCREAMING_SNAKE_CASE_   :	Any													=   patch_size
						SCREAMING_SNAKE_CASE_   :	Any													=   feature_size // self.patch_size[1]
						SCREAMING_SNAKE_CASE_   :	List[str]													=   n_fft
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   sampling_rate // hop_length_to_sampling_rate
						SCREAMING_SNAKE_CASE_   :	List[str]													=   sampling_rate
						SCREAMING_SNAKE_CASE_   :	Any													=   padding_value
						SCREAMING_SNAKE_CASE_   :	Dict													=   mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2 ,    num_mel_filters=UpperCamelCase__ ,    min_frequency=0.0 ,    max_frequency=2_20_50.0 ,    sampling_rate=UpperCamelCase__ ,    norm='slaney' ,    mel_scale='slaney' ,    ).T


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	str													=   spectrogram(
						    UpperCamelCase__ ,    window_function(self.n_fft ,    'hann'       ) ,    frame_length=self.n_fft ,    hop_length=self.hop_length ,    power=2.0 ,    mel_filters=self.mel_filters.T ,    log_mel='dB' ,    db_range=80.0 ,    )
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   log_spec[:, :-1]
						SCREAMING_SNAKE_CASE_   :	Any													=   log_spec - 20.0
						SCREAMING_SNAKE_CASE_   :	Any													=   np.clip(log_spec / 40.0 ,    -2.0 ,    0.0       ) + 1.0
						return log_spec






		def __call__(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = True ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False ,    UpperCamelCase__ = False ,    **UpperCamelCase__ ,    ):

						if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
														raise ValueError(
														    'This feature extractor is set to support sampling rate'
														    F''' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'''
														    F''' 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.'       )

						SCREAMING_SNAKE_CASE_   :	int													=   isinstance(UpperCamelCase__ ,    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}'''       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   is_batched_numpy or (
						    isinstance(UpperCamelCase__ ,    (list, tuple)       ) and (isinstance(raw_speech[0] ,    (np.ndarray, tuple, list)       ))
						)
						if is_batched:
										SCREAMING_SNAKE_CASE_   :	List[str]													=   [np.asarray([speech] ,    dtype=np.floataa       ).T for speech in raw_speech]
						elif not is_batched and not isinstance(UpperCamelCase__ ,    np.ndarray       ):
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   np.asarray(UpperCamelCase__ ,    dtype=np.floataa       )
						elif isinstance(UpperCamelCase__ ,    np.ndarray       ) and raw_speech.dtype is np.dtype(np.floataa       ):
										SCREAMING_SNAKE_CASE_   :	str													=   raw_speech.astype(np.floataa       )
						# always return batch
						if not is_batched:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   [np.asarray([raw_speech]       ).T]

						# Convert audio signals to log mel spectrograms, truncate by time axis
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   [
						    self._np_extract_fbank_features(waveform.squeeze()       ).T[: self.spectrogram_length] for waveform in raw_speech
						]
						if isinstance(audio_features[0] ,    UpperCamelCase__       ):
										SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   [np.asarray(UpperCamelCase__ ,    dtype=np.floataa       ) for feature in audio_features]

						# Create audio attention mask
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   max(
						    [ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len for feature in audio_features]       )  # The maximum number of audio patches in a batch
						if return_attention_mask:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [
										    (ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [1]
										    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]       ) * self.freq_len) * [0]
										    for feature in audio_features
										]
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   np.array(UpperCamelCase__       ).astype(np.floataa       )

						# convert into correct format for padding
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
						SCREAMING_SNAKE_CASE_   :	int													=   np.ones([len(UpperCamelCase__       ), 1, max_time_len, self.feature_size]       ).astype(np.floataa       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   padded_audio_features * self.padding_value
						for i in range(len(UpperCamelCase__       )       ):
										SCREAMING_SNAKE_CASE_   :	str													=   audio_features[i]
										SCREAMING_SNAKE_CASE_   :	str													=   feature

						# return as BatchFeature
						if return_attention_mask:
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   {'''audio_values''': padded_audio_features, '''audio_mask''': audio_mask}
						else:
										SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {'''audio_values''': padded_audio_features}

						SCREAMING_SNAKE_CASE_   :	Dict													=   BatchFeature(data=UpperCamelCase__ ,    tensor_type=UpperCamelCase__       )
						return encoded_inputs







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


A        =			datasets.logging.get_logger(__name__)


A        =			'\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n'

A        =			'\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project\'s README at https://github.com/google-research/bleurt#readme for more information.\n'

A        =			'\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    \'scores\': List of scores.\nExamples:\n\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> bleurt = datasets.load_metric("bleurt")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results["scores"]])\n    [1.03, 1.04]\n'

A        =			{
    'bleurt-tiny-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip',
    'bleurt-tiny-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip',
    'bleurt-base-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip',
    'bleurt-base-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip',
    'bleurt-large-128': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip',
    'bleurt-large-512': 'https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip',
    'BLEURT-20-D3': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip',
    'BLEURT-20-D6': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip',
    'BLEURT-20-D12': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip',
    'BLEURT-20': 'https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip',
}


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





		'''simple docstring'''



		def   __snake_case			(						self       ):
						return datasets.MetricInfo(
						    description=_DESCRIPTION ,    citation=_CITATION ,    homepage='https://github.com/google-research/bleurt' ,    inputs_description=_KWARGS_DESCRIPTION ,    features=datasets.Features(
						        {
						            'predictions': datasets.Value('string' ,    id='sequence'       ),
						            'references': datasets.Value('string' ,    id='sequence'       ),
						        }       ) ,    codebase_urls=['https://github.com/google-research/bleurt'] ,    reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696'] ,    )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						# check that config name specifies a valid BLEURT model
						if self.config_name == "default":
										logger.warning(
										    'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
										    'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').'       )
										SCREAMING_SNAKE_CASE_   :	List[Any]													=   'bleurt-base-128'

						if self.config_name.lower() in CHECKPOINT_URLS:
										SCREAMING_SNAKE_CASE_   :	Tuple													=   self.config_name.lower()

						elif self.config_name.upper() in CHECKPOINT_URLS:
										SCREAMING_SNAKE_CASE_   :	Any													=   self.config_name.upper()

						else:
										raise KeyError(
										    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''       )

						# download the model checkpoint specified by self.config_name and set up the scorer
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]       )
						SCREAMING_SNAKE_CASE_   :	int													=   score.BleurtScorer(os.path.join(UpperCamelCase__ ,    UpperCamelCase__       )       )






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Tuple													=   self.scorer.score(references=UpperCamelCase__ ,    candidates=UpperCamelCase__       )
						return {"scores": scores}