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


__lowercase										= logging.get_logger(__name__)


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						List[Any]      =				["""audio_values""", """audio_mask"""]







    def __init__(     self			,			__lowercase=2_048			,			__lowercase=1			,			__lowercase=[16, 16]			,			__lowercase=128			,			__lowercase=44_100			,			__lowercase=86			,			__lowercase=2_048			,			__lowercase=0.0			,			**__lowercase			,			)	->     List[Any]:
        super().__init__(
            feature_size=__lowercase			,			sampling_rate=__lowercase			,			padding_value=__lowercase			,			**__lowercase			,			)

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







    def       UpperCamelCase__       (     self			,			__lowercase)	->     np.ndarray:
        __UpperCamelCase					:int        =		spectrogram(
            __lowercase			,			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			,			)
        __UpperCamelCase					:Dict        =		log_spec[:, :-1]
        __UpperCamelCase					:List[str]        =		log_spec - 20.0
        __UpperCamelCase					:str        =		np.clip(log_spec / 40.0			,			-2.0			,			0.0) + 1.0
        return log_spec







    def __call__(     self			,			__lowercase			,			__lowercase = None			,			__lowercase = True			,			__lowercase = None			,			__lowercase = False			,			__lowercase = False			,			**__lowercase			,			)	->     BatchFeature:

        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.''')

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

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

        # Create audio attention mask
        __UpperCamelCase					: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:
            __UpperCamelCase					:List[str]        =		[
                (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
            ]
            __UpperCamelCase					:Union[str, Any]        =		np.array(__lowercase).astype(np.floataa)

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

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

        __UpperCamelCase					:Union[str, Any]        =		BatchFeature(data=__lowercase			,			tensor_type=__lowercase)
        return encoded_inputs






import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class      lowerCamelCase_					(       UpperCAmelCase_						,     unittest.TestCase			):






    '''simple docstring'''






    a__     :						List[Any]      =				PhobertTokenizer
    a__     :						Union[str, Any]      =				False







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __UpperCamelCase					:Union[str, Any]        =		['''T@@''', '''i''', '''I''', '''R@@''', '''r''', '''e@@''']
        __UpperCamelCase					:int        =		dict(zip(__lowercase			,			range(len(__lowercase))))
        __UpperCamelCase					:Dict        =		['''#version: 0.2''', '''l à</w>''']
        __UpperCamelCase					:Any        =		{'''unk_token''': '''<unk>'''}

        __UpperCamelCase					:int        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''vocab_file'''])
        __UpperCamelCase					:Optional[int]        =		os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['''merges_file'''])

        with open(self.vocab_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            for token in vocab_tokens:
                fp.write(f"""{token} {vocab_tokens[token]}\n""")
        with open(self.merges_file			,			'''w'''			,			encoding='''utf-8''') as fp:
            fp.write('''\n'''.join(__lowercase))







    def       UpperCamelCase__       (     self			,			**__lowercase)	->     Optional[Any]:
        kwargs.update(self.special_tokens_map)
        return PhobertTokenizer.from_pretrained(self.tmpdirname			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        __UpperCamelCase					:List[Any]        =		'''Tôi là VinAI Research'''
        __UpperCamelCase					:List[str]        =		'''T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
        return input_text, output_text







    def       UpperCamelCase__       (     self)	->     Optional[int]:
        __UpperCamelCase					:Dict        =		PhobertTokenizer(self.vocab_file			,			self.merges_file			,			**self.special_tokens_map)
        __UpperCamelCase					:List[Any]        =		'''Tôi là VinAI Research'''
        __UpperCamelCase					:List[str]        =		'''T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
        __UpperCamelCase					:int        =		tokenizer.tokenize(__lowercase)
        print(__lowercase)
        self.assertListEqual(__lowercase			,			__lowercase)

        __UpperCamelCase					:Dict        =		tokens + [tokenizer.unk_token]

        __UpperCamelCase					:Any        =		[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase)			,			__lowercase)





