


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







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    ["audio_values", "audio_mask"]





       def __init__(     self							:    List[str]      ,   __lowerCamelCase							:    Tuple=2048      ,   __lowerCamelCase							:    Union[str, Any]=1      ,   __lowerCamelCase							:    Optional[int]=[16, 16]      ,   __lowerCamelCase							:    List[Any]=128      ,   __lowerCamelCase							:    List[Any]=44100      ,   __lowerCamelCase							:    Any=86      ,   __lowerCamelCase							:    Dict=2048      ,   __lowerCamelCase							:    List[Any]=0.0      ,   **__lowerCamelCase							:    Optional[int]      ,   ):


             '''simple docstring'''



             super().__init__(
                 feature_size=__lowerCamelCase      ,   sampling_rate=__lowerCamelCase      ,   padding_value=__lowerCamelCase      ,   **__lowerCamelCase      ,   )

             lowerCamelCase__	:       Dict					=						spectrogram_length
             lowerCamelCase__	:       List[str]					=						num_channels
             lowerCamelCase__	:       str					=						patch_size
             lowerCamelCase__	:       Dict					=						feature_size // self.patch_size[1]
             lowerCamelCase__	:       List[Any]					=						n_fft
             lowerCamelCase__	:       str					=						sampling_rate // hop_length_to_sampling_rate
             lowerCamelCase__	:       Optional[int]					=						sampling_rate
             lowerCamelCase__	:       Tuple					=						padding_value
             lowerCamelCase__	:       Dict					=						mel_filter_bank(
                 num_frequency_bins=1 + n_fft // 2      ,   num_mel_filters=__lowerCamelCase      ,   min_frequency=0.0      ,   max_frequency=22050.0      ,   sampling_rate=__lowerCamelCase      ,   norm="slaney"      ,   mel_scale="slaney"      ,   ).T





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    np.array ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						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.T      ,   log_mel="dB"      ,   db_range=80.0      ,   )
             lowerCamelCase__	:       str					=						log_spec[:, :-1]
             lowerCamelCase__	:       str					=						log_spec - 20.0
             lowerCamelCase__	:       int					=						np.clip(log_spec / 40.0      ,   -2.0      ,   0.0 ) + 1.0
             return log_spec





       def __call__(     self							:    Optional[int]      ,   __lowerCamelCase							:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]      ,   __lowerCamelCase							:    Optional[Union[str, TensorType]] = None      ,   __lowerCamelCase							:    Optional[bool] = True      ,   __lowerCamelCase							:    Optional[int] = None      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    bool = False      ,   **__lowerCamelCase							:    Tuple      ,   ):


             '''simple docstring'''



             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." )

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

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

             # Create audio attention mask
             lowerCamelCase__	:       Any					=						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:
                   lowerCamelCase__	:       Optional[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
                   ]
                   lowerCamelCase__	:       Tuple					=						np.array(__lowerCamelCase ).astype(np.floataa )

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

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

             lowerCamelCase__	:       Any					=						BatchFeature(data=__lowerCamelCase      ,   tensor_type=__lowerCamelCase )
             return encoded_inputs






import unittest

from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
@require_sentencepiece
@slow  # see https://github.com/huggingface/transformers/issues/11457
class     _lowercase				(							lowercase__							,						unittest.TestCase):


       """simple docstring"""




       A__      		=    BarthezTokenizer
       A__      		=    BarthezTokenizerFast
       A__      		=    True
       A__      		=    True





       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__	:       List[str]					=						BarthezTokenizerFast.from_pretrained("moussaKam/mbarthez" )
             tokenizer.save_pretrained(self.tmpdirname )
             tokenizer.save_pretrained(self.tmpdirname      ,   legacy_format=__lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						tokenizer





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Any					=						"<pad>"
             lowerCamelCase__	:       Tuple					=						1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCamelCase )      ,   __lowerCamelCase )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCamelCase )      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]      ,   "<s>" )
             self.assertEqual(vocab_keys[1]      ,   "<pad>" )
             self.assertEqual(vocab_keys[-1]      ,   "<mask>" )
             self.assertEqual(len(__lowerCamelCase )      ,   101122 )





       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



             self.assertEqual(self.get_tokenizer().vocab_size      ,   101122 )





       @require_torch
       def 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						["A long paragraph for summarization.", "Another paragraph for summarization."]
             lowerCamelCase__	:       str					=						[0, 57, 3018, 70307, 91, 2]

             lowerCamelCase__	:       Tuple					=						self.tokenizer(
                 __lowerCamelCase      ,   max_length=len(__lowerCamelCase )      ,   padding=__lowerCamelCase      ,   truncation=__lowerCamelCase      ,   return_tensors="pt" )
             self.assertIsInstance(__lowerCamelCase      ,   __lowerCamelCase )

             self.assertEqual((2, 6)      ,   batch.input_ids.shape )
             self.assertEqual((2, 6)      ,   batch.attention_mask.shape )
             lowerCamelCase__	:       Any					=						batch.input_ids.tolist()[0]
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       def 		lowerCAmelCase  (     self							:    Any ):


             '''simple docstring'''



             if not self.test_rust_tokenizer:
                   return

             lowerCamelCase__	:       Any					=						self.get_tokenizer()
             lowerCamelCase__	:       Tuple					=						self.get_rust_tokenizer()

             lowerCamelCase__	:       Union[str, Any]					=						"I was born in 92000, and this is falsé."

             lowerCamelCase__	:       Dict					=						tokenizer.tokenize(__lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						rust_tokenizer.tokenize(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       Tuple					=						tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase      ,   add_special_tokens=__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       List[str]					=						self.get_rust_tokenizer()
             lowerCamelCase__	:       Optional[Any]					=						tokenizer.encode(__lowerCamelCase )
             lowerCamelCase__	:       List[Any]					=						rust_tokenizer.encode(__lowerCamelCase )
             self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase )





       @slow
       def 		lowerCAmelCase  (     self							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       int					=						{"input_ids": [[0, 490, 14328, 4507, 354, 47, 43669, 95, 25, 78117, 20215, 19779, 190, 22, 400, 4, 35343, 80310, 603, 86, 24937, 105, 33438, 94762, 196, 39642, 7, 15, 15933, 173, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 10534, 87, 25, 66, 3358, 196, 55289, 8, 82961, 81, 2204, 75203, 7, 15, 763, 12956, 216, 178, 14328, 9595, 1377, 69693, 7, 448, 71021, 196, 18106, 1437, 13974, 108, 9083, 4, 49315, 7, 39, 86, 1326, 2793, 46333, 4, 448, 196, 74588, 7, 49315, 7, 39, 21, 822, 38470, 74, 21, 66723, 62480, 8, 22050, 5, 2]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
             # fmt: on

             # moussaKam/mbarthez is a french model. So we also use french texts.
             lowerCamelCase__	:       List[str]					=						[
                 "Le transformeur est un modèle d'apprentissage profond introduit en 2017, "
                 "utilisé principalement dans le domaine du traitement automatique des langues (TAL).",
                 "À l'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus "
                 "pour gérer des données séquentielles, telles que le langage naturel, pour des tâches "
                 "telles que la traduction et la synthèse de texte.",
             ]

             self.tokenizer_integration_test_util(
                 expected_encoding=__lowerCamelCase      ,   model_name="moussaKam/mbarthez"      ,   revision="c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6"      ,   sequences=__lowerCamelCase      ,   )


