
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    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= BarthezTokenizer
				lowercase__       							= BarthezTokenizerFast
				lowercase__       							= True
				lowercase__       							= True




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								super().setUp()

								lowercase__     				=    BarthezTokenizerFast.from_pretrained('''moussaKam/mbarthez'''						)
								tokenizer.save_pretrained(self.tmpdirname						)
								tokenizer.save_pretrained(self.tmpdirname,						legacy_format=lowerCamelCase						)
								lowercase__     				=    tokenizer




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    '''<pad>'''
								lowercase__     				=    1

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




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

								'''simple docstring'''
								lowercase__     				=    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						),						101_122						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								self.assertEqual(self.get_tokenizer().vocab_size,						101_122						)




				@require_torch
				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								lowercase__     				=    [0, 57, 3_018, 70_307, 91, 2]

								lowercase__     				=    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						)
								lowercase__     				=    batch.input_ids.tolist()[0]
								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								if not self.test_rust_tokenizer:
												return

								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    self.get_rust_tokenizer()

								lowercase__     				=    '''I was born in 92000, and this is falsé.'''

								lowercase__     				=    tokenizer.tokenize(lowerCamelCase						)
								lowercase__     				=    rust_tokenizer.tokenize(lowerCamelCase						)
								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    tokenizer.encode(lowerCamelCase,						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    rust_tokenizer.encode(lowerCamelCase,						add_special_tokens=lowerCamelCase						)
								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    self.get_rust_tokenizer()
								lowercase__     				=    tokenizer.encode(lowerCamelCase						)
								lowercase__     				=    rust_tokenizer.encode(lowerCamelCase						)
								self.assertListEqual(lowerCamelCase,						lowerCamelCase						)




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    {'''input_ids''': [[0, 490, 14_328, 4_507, 354, 47, 43_669, 95, 25, 78_117, 20_215, 19_779, 190, 22, 400, 4, 35_343, 80_310, 603, 86, 24_937, 105, 33_438, 94_762, 196, 39_642, 7, 15, 15_933, 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, 10_534, 87, 25, 66, 3_358, 196, 55_289, 8, 82_961, 81, 2_204, 75_203, 7, 15, 763, 12_956, 216, 178, 14_328, 9_595, 1_377, 69_693, 7, 448, 71_021, 196, 18_106, 1_437, 13_974, 108, 9_083, 4, 49_315, 7, 39, 86, 1_326, 2_793, 46_333, 4, 448, 196, 74_588, 7, 49_315, 7, 39, 21, 822, 38_470, 74, 21, 66_723, 62_480, 8, 22_050, 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.
								lowercase__     				=    [
								    '''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,						)












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

import numpy as np
import torch

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__			:    Tuple         =				logging.get_logger(__name__)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""input_features""", """is_longer"""]




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					int=64,						lowerCamelCase		:					Union[str, Any]=48_000,						lowerCamelCase		:					str=480,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					List[Any]=1_024,						lowerCamelCase		:					Optional[int]=0.0,						lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					float = 0,						lowerCamelCase		:					float = 14_000,						lowerCamelCase		:					int = None,						lowerCamelCase		:					str = "fusion",						lowerCamelCase		:					str = "repeatpad",						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    feature_size=lowerCamelCase,						sampling_rate=lowerCamelCase,						padding_value=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    top_db
								lowercase__     				=    truncation
								lowercase__     				=    padding
								lowercase__     				=    fft_window_size
								lowercase__     				=    (fft_window_size >> 1) + 1
								lowercase__     				=    hop_length
								lowercase__     				=    max_length_s
								lowercase__     				=    max_length_s * sampling_rate
								lowercase__     				=    sampling_rate
								lowercase__     				=    frequency_min
								lowercase__     				=    frequency_max
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm=lowerCamelCase,						mel_scale='''htk''',						)
								lowercase__     				=    mel_filter_bank(
								    num_frequency_bins=self.nb_frequency_bins,						num_mel_filters=lowerCamelCase,						min_frequency=lowerCamelCase,						max_frequency=lowerCamelCase,						sampling_rate=lowerCamelCase,						norm='''slaney''',						mel_scale='''slaney''',						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    copy.deepcopy(self.__dict__						)
								lowercase__     				=    self.__class__.__name__
								if "mel_filters" in output:
												del output["mel_filters"]
								if "mel_filters_slaney" in output:
												del output["mel_filters_slaney"]
								return output




				def        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.array,						lowerCamelCase		:					Optional[np.array] = None						):

								'''simple docstring'''
								lowercase__     				=    spectrogram(
								    lowerCamelCase,						window_function(self.fft_window_size,						'''hann'''						),						frame_length=self.fft_window_size,						hop_length=self.hop_length,						power=2.0,						mel_filters=lowerCamelCase,						log_mel='''dB''',						)
								return log_mel_spectrogram.T




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    np.array_split(list(range(0,						total_frames - chunk_frames + 1						)						),						3						)
								if len(ranges[1]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								if len(ranges[2]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								# randomly choose index for each part
								lowercase__     				=    np.random.choice(ranges[0]						)
								lowercase__     				=    np.random.choice(ranges[1]						)
								lowercase__     				=    np.random.choice(ranges[2]						)

								lowercase__     				=    mel[idx_front : idx_front + chunk_frames, :]
								lowercase__     				=    mel[idx_middle : idx_middle + chunk_frames, :]
								lowercase__     				=    mel[idx_back : idx_back + chunk_frames, :]

								lowercase__     				=    torch.tensor(mel[None, None, :]						)
								lowercase__     				=    torch.nn.functional.interpolate(
								    lowerCamelCase,						size=[chunk_frames, 64],						mode='''bilinear''',						align_corners=lowerCamelCase						)
								lowercase__     				=    mel_shrink[0][0].numpy()
								lowercase__     				=    np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back],						axis=0						)
								return mel_fusion




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.array,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								if waveform.shape[0] > max_length:
												if truncation == "rand_trunc":
																lowercase__     				=    True
																# random crop to max_length (for compatibility) -> this should be handled by self.pad
																lowercase__     				=    len(lowerCamelCase						) - max_length
																lowercase__     				=    np.random.randint(0,						overflow + 1						)
																lowercase__     				=    waveform[idx : idx + max_length]
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]
												elif truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																lowercase__     				=    mel.shape[0]
																if chunk_frames == total_frames:
																				# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																				# In this case, we just use the whole audio.
																				lowercase__     				=    np.stack([mel, mel, mel, mel],						axis=0						)
																				lowercase__     				=    False
																else:
																				lowercase__     				=    self._random_mel_fusion(lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)
																				lowercase__     				=    True
												else:
																raise NotImplementedError(F"""data_truncating {truncation} not implemented"""						)

								else:
												lowercase__     				=    False
												# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
												if waveform.shape[0] < max_length:
																if padding == "repeat":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						n_repeat + 1						)						)[:max_length]
																if padding == "repeatpad":
																				lowercase__     				=    int(max_length / len(lowerCamelCase						)						)
																				lowercase__     				=    np.stack(np.tile(lowerCamelCase,						lowerCamelCase						)						)
																lowercase__     				=    np.pad(lowerCamelCase,						(0, max_length - waveform.shape[0]),						mode='''constant''',						constant_values=0						)

												if truncation == "fusion":
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters						)
																lowercase__     				=    np.stack([input_mel, input_mel, input_mel, input_mel],						axis=0						)
												else:
																lowercase__     				=    self._np_extract_fbank_features(lowerCamelCase,						self.mel_filters_slaney						)[None, :]

								return input_mel, longer




				def __call__(  self		:					Union[str, Any],						lowerCamelCase		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],						lowerCamelCase		:					str = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    truncation if truncation is not None else self.truncation
								lowercase__     				=    padding if padding else self.padding

								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.'''						)

								lowercase__     				=    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}"""						)
								lowercase__     				=    is_batched_numpy or (
								    isinstance(lowerCamelCase,						(list, tuple)						) and (isinstance(raw_speech[0],						(np.ndarray, tuple, list)						))
								)

								if is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for speech in raw_speech]
								elif not is_batched and not isinstance(lowerCamelCase,						np.ndarray						):
												lowercase__     				=    np.asarray(lowerCamelCase,						dtype=np.floataa						)
								elif isinstance(lowerCamelCase,						np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
												lowercase__     				=    raw_speech.astype(np.floataa						)

								# always return batch
								if not is_batched:
												lowercase__     				=    [np.asarray(lowerCamelCase						)]

								# convert to mel spectrogram, truncate and pad if needed.
								lowercase__     				=    [
								    self._get_input_mel(lowerCamelCase,						max_length if max_length else self.nb_max_samples,						lowerCamelCase,						lowerCamelCase						)
								    for waveform in raw_speech
								]

								lowercase__     				=    []
								lowercase__     				=    []
								for mel, longer in padded_inputs:
												input_mel.append(lowerCamelCase						)
												is_longer.append(lowerCamelCase						)

								if truncation == "fusion" and sum(lowerCamelCase						) == 0:
												# if no audio is longer than 10s, then randomly select one audio to be longer
												lowercase__     				=    np.random.randint(0,						len(lowerCamelCase						)						)
												lowercase__     				=    True

								if isinstance(input_mel[0],						lowerCamelCase						):
												lowercase__     				=    [np.asarray(lowerCamelCase,						dtype=np.floataa						) for feature in input_mel]

								# is_longer is a list of bool
								lowercase__     				=    [[longer] for longer in is_longer]

								lowercase__     				=    {'''input_features''': input_mel, '''is_longer''': is_longer}
								lowercase__     				=    BatchFeature(lowerCamelCase						)

								if return_tensors is not None:
												lowercase__     				=    input_features.convert_to_tensors(lowerCamelCase						)

								return input_features




