"""simple docstring"""


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase									=			get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')




class  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      BartphoTokenizer
       SCREAMING_SNAKE_CASE_			=      False
       SCREAMING_SNAKE_CASE_			=      True







       def 		a_						( self)      ->      int:
            super().setUp()

            snake_case_					       =					['▁This', '▁is', '▁a', '▁t', 'est']
            snake_case_					       =					dict(zip(lowerCAmelCase__,							range(len(lowerCAmelCase__))))
            snake_case_					       =					{'unk_token': '<unk>'}

            snake_case_					       =					os.path.join(self.tmpdirname,							VOCAB_FILES_NAMES['monolingual_vocab_file'])
            with open(self.monolingual_vocab_file,							'w',							encoding='utf-8') as fp:
                 for token in vocab_tokens:
                      fp.write(f'{token} {vocab_tokens[token]}\n')

            snake_case_					       =					BartphoTokenizer(lowerCAmelCase__,							self.monolingual_vocab_file,							**self.special_tokens_map)
            tokenizer.save_pretrained(self.tmpdirname)







       def 		a_						( self,							**lowerCAmelCase__)      ->      Union[str, Any]:
            kwargs.update(self.special_tokens_map)
            return BartphoTokenizer.from_pretrained(self.tmpdirname,							**lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__)      ->      Union[str, Any]:
            snake_case_					       =					'This is a là test'
            snake_case_					       =					'This is a<unk><unk> test'
            return input_text, output_text







       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					BartphoTokenizer(lowerCAmelCase__,							self.monolingual_vocab_file,							**self.special_tokens_map)
            snake_case_					       =					'This is a là test'
            snake_case_					       =					'▁This ▁is ▁a ▁l à ▁t est'.split()
            snake_case_					       =					tokenizer.tokenize(lowerCAmelCase__)
            self.assertListEqual(lowerCAmelCase__,							lowerCAmelCase__)

            snake_case_					       =					tokens + [tokenizer.unk_token]
            snake_case_					       =					[4, 5, 6, 3, 3, 7, 8, 3]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__),							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


_UpperCAmelCase		:   Tuple										=       logging.get_logger(__name__)







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[Any]       			=			['''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




