

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


_UpperCAmelCase	:     int		       = logging.get_logger(__name__)







class 				lowercase       ( lowercase_    ):
				__SCREAMING_SNAKE_CASE      :				int     =    ['''input_features''', '''is_longer''']




				def __init__(      self   ,     snake_case=64   ,     snake_case=4_8000   ,     snake_case=480   ,     snake_case=10   ,     snake_case=1024   ,     snake_case=0.0   ,     snake_case=False   ,     snake_case = 0   ,     snake_case = 1_4000   ,     snake_case = None   ,     snake_case = "fusion"   ,     snake_case = "repeatpad"   ,     **snake_case   ,     ):
											super().__init__(
											    feature_size=snake_case   ,     sampling_rate=snake_case   ,     padding_value=snake_case   ,     return_attention_mask=snake_case   ,     **snake_case   ,     )
											snake_case_           =					top_db
											snake_case_           =					truncation
											snake_case_           =					padding
											snake_case_           =					fft_window_size
											snake_case_           =					(fft_window_size >> 1) + 1
											snake_case_           =					hop_length
											snake_case_           =					max_length_s
											snake_case_           =					max_length_s * sampling_rate
											snake_case_           =					sampling_rate
											snake_case_           =					frequency_min
											snake_case_           =					frequency_max
											snake_case_           =					mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins   ,     num_mel_filters=snake_case   ,     min_frequency=snake_case   ,     max_frequency=snake_case   ,     sampling_rate=snake_case   ,     norm=snake_case   ,     mel_scale='htk'   ,     )
											snake_case_           =					mel_filter_bank(
											    num_frequency_bins=self.nb_frequency_bins   ,     num_mel_filters=snake_case   ,     min_frequency=snake_case   ,     max_frequency=snake_case   ,     sampling_rate=snake_case   ,     norm='slaney'   ,     mel_scale='slaney'   ,     )




				def 					a							(      self ):
											snake_case_           =					copy.deepcopy(self.__dict__ )
											snake_case_           =					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 					a							(      self   ,     snake_case   ,     snake_case = None ):
											snake_case_           =					spectrogram(
											    snake_case   ,     window_function(self.fft_window_size   ,     'hann' )   ,     frame_length=self.fft_window_size   ,     hop_length=self.hop_length   ,     power=2.0   ,     mel_filters=snake_case   ,     log_mel='dB'   ,     )
											return log_mel_spectrogram.T




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					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
																		snake_case_           =					[0]
											if len(ranges[2] ) == 0:
																		# if the audio is too short, we just use the first chunk
																		snake_case_           =					[0]
											# randomly choose index for each part
											snake_case_           =					np.random.choice(ranges[0] )
											snake_case_           =					np.random.choice(ranges[1] )
											snake_case_           =					np.random.choice(ranges[2] )

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

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




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case   ,     snake_case ):
											if waveform.shape[0] > max_length:
																		if truncation == "rand_trunc":
																									snake_case_           =					True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									snake_case_           =					len(snake_case ) - max_length
																									snake_case_           =					np.random.randint(0   ,     overflow + 1 )
																									snake_case_           =					waveform[idx : idx + max_length]
																									snake_case_           =					self._np_extract_fbank_features(snake_case   ,     self.mel_filters_slaney )[None, :]
																		elif truncation == "fusion":
																									snake_case_           =					self._np_extract_fbank_features(snake_case   ,     self.mel_filters )
																									snake_case_           =					max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									snake_case_           =					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.
																																snake_case_           =					np.stack([mel, mel, mel, mel]   ,     axis=0 )
																																snake_case_           =					False
																									else:
																																snake_case_           =					self._random_mel_fusion(snake_case   ,     snake_case   ,     snake_case )
																																snake_case_           =					True
																		else:
																									raise NotImplementedError(F'''data_truncating {truncation} not implemented''' )

											else:
																		snake_case_           =					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":
																																snake_case_           =					int(max_length / len(snake_case ) )
																																snake_case_           =					np.stack(np.tile(snake_case   ,     n_repeat + 1 ) )[:max_length]
																									if padding == "repeatpad":
																																snake_case_           =					int(max_length / len(snake_case ) )
																																snake_case_           =					np.stack(np.tile(snake_case   ,     snake_case ) )
																									snake_case_           =					np.pad(snake_case   ,     (0, max_length - waveform.shape[0])   ,     mode='constant'   ,     constant_values=0 )

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

											return input_mel, longer




				def __call__(      self   ,     snake_case   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     snake_case = None   ,     **snake_case   ,     ):
											snake_case_           =					truncation if truncation is not None else self.truncation
											snake_case_           =					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.' )

											snake_case_           =					isinstance(snake_case   ,     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}''' )
											snake_case_           =					is_batched_numpy or (
											    isinstance(snake_case   ,     (list, tuple) ) and (isinstance(raw_speech[0]   ,     (np.ndarray, tuple, list) ))
											)

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

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

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

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

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

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

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

											snake_case_           =					{'input_features': input_mel, 'is_longer': is_longer}
											snake_case_           =					BatchFeature(snake_case )

											if return_tensors is not None:
																		snake_case_           =					input_features.convert_to_tensors(snake_case )

											return input_features







import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




				def 					a							(      self ):
											super().setUp()

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

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					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(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[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(snake_case )   ,     snake_case )




