





'''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


_A:					List[Any]							=				logging.get_logger(__name__)






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Union[str, Any]						   =       ["""input_features"""]



    def __init__(  self     ,			__A=80     ,			__A=16_000     ,			__A=160     ,			__A=30     ,			__A=400     ,			__A=0.0     ,			__A=False     ,			**__A     ,			):
     super().__init__(
         feature_size=__A     ,			sampling_rate=__A     ,			padding_value=__A     ,			return_attention_mask=__A     ,			**__A     ,			)
     __UpperCAmelCase					=   n_fft
     __UpperCAmelCase					=   hop_length
     __UpperCAmelCase					=   chunk_length
     __UpperCAmelCase					=   chunk_length * sampling_rate
     __UpperCAmelCase					=   self.n_samples // hop_length
     __UpperCAmelCase					=   sampling_rate
     __UpperCAmelCase					=   mel_filter_bank(
         num_frequency_bins=1 + n_fft // 2     ,			num_mel_filters=__A     ,			min_frequency=0.0     ,			max_frequency=8_0_0_0.0     ,			sampling_rate=__A     ,			norm='slaney'     ,			mel_scale='slaney'     ,			)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   spectrogram(
         __A     ,			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'     ,			)
     __UpperCAmelCase					=   log_spec[:, :-1]
     __UpperCAmelCase					=   np.maximum(__A     ,			log_spec.max() - 8.0					)
     __UpperCAmelCase					=   (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						(  __A     ,			__A     ,			__A = 0.0					):
     if attention_mask is not None:
      __UpperCAmelCase					=   np.array(__A     ,			np.intaa					)
      __UpperCAmelCase					=   []

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

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

     return normed_input_values



    def __call__(  self     ,			__A     ,			__A = True     ,			__A = None     ,			__A = None     ,			__A = None     ,			__A = "max_length"     ,			__A = None     ,			__A = None     ,			__A = None     ,			**__A     ,			):

     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(__A     ,			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(__A     ,			(list, tuple)					) and (isinstance(raw_speech[0]     ,			(np.ndarray, tuple, list)					))
     )

     if is_batched:
      __UpperCAmelCase					=   [np.asarray([speech]     ,			dtype=np.floataa					).T for speech in raw_speech]
     elif not is_batched and not isinstance(__A     ,			np.ndarray					):
      __UpperCAmelCase					=   np.asarray(__A     ,			dtype=np.floataa					)
     elif isinstance(__A     ,			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([raw_speech]					).T]

     __UpperCAmelCase					=   BatchFeature({'input_features': raw_speech}					)

     # convert into correct format for padding

     __UpperCAmelCase					=   self.pad(
         __A     ,			padding=__A     ,			max_length=max_length if max_length else self.n_samples     ,			truncation=__A     ,			pad_to_multiple_of=__A     ,			return_attention_mask=return_attention_mask or do_normalize     ,			)

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

     # make sure list is in array format
     __UpperCAmelCase					=   padded_inputs.get('input_features'					).transpose(2     ,			0     ,			1					)

     __UpperCAmelCase					=   [self._np_extract_fbank_features(__A					) for waveform in input_features[0]]

     if isinstance(input_features[0]     ,			__A					):
      __UpperCAmelCase					=   [np.asarray(__A     ,			dtype=np.floataa					) for feature in input_features]
     else:
      __UpperCAmelCase					=   input_features

     if return_attention_mask:
      # rescale from sample (48000) to feature (3000)
      __UpperCAmelCase					=   padded_inputs['attention_mask'][:, :: self.hop_length]

     if return_tensors is not None:
      __UpperCAmelCase					=   padded_inputs.convert_to_tensors(__A					)

     return padded_inputs




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   copy.deepcopy(self.__dict__					)
     __UpperCAmelCase					=   self.__class__.__name__
     if "mel_filters" in output:
      del output["mel_filters"]
     return output












'''simple docstring'''




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_A:					Union[str, Any]							=				get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
    _A:					Optional[Any]							=				json.load(f)






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     # note: this test is not testing the best performance since it only evals a small batch
     # but it should be enough to detect a regression in the output quality
     __UpperCAmelCase					=   f'facebook/wmt19-{pair}'
     __UpperCAmelCase					=   self.get_tokenizer(__A					)
     __UpperCAmelCase					=   self.get_model(__A					)

     __UpperCAmelCase					=   bleu_data[pair]['src']
     __UpperCAmelCase					=   bleu_data[pair]['tgt']

     __UpperCAmelCase					=   tokenizer(__A     ,			return_tensors='pt'     ,			truncation=__A     ,			padding='longest'					).to(__A					)
     __UpperCAmelCase					=   model.generate(
         input_ids=batch.input_ids     ,			num_beams=8     ,			)
     __UpperCAmelCase					=   tokenizer.batch_decode(
         __A     ,			skip_special_tokens=__A     ,			clean_up_tokenization_spaces=__A					)
     __UpperCAmelCase					=   calculate_bleu(__A     ,			__A					)
     print(__A					)
     self.assertGreaterEqual(scores['bleu']     ,			__A					)





