



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__   :					Union[str, Any] 						=      logging.get_logger(__name__)



class    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   A     :	Tuple												=     ["input_features", "is_longer"]


   def __init__(    self	:      int      ,					lowerCAmelCase	:      Tuple=64      ,					lowerCAmelCase	:      str=4_80_00      ,					lowerCAmelCase	:      int=4_80      ,					lowerCAmelCase	:      List[Any]=10      ,					lowerCAmelCase	:      Any=10_24      ,					lowerCAmelCase	:      Union[str, Any]=0.0      ,					lowerCAmelCase	:      List[Any]=False      ,					lowerCAmelCase	:      float = 0      ,					lowerCAmelCase	:      float = 1_40_00      ,					lowerCAmelCase	:      int = None      ,					lowerCAmelCase	:      str = "fusion"      ,					lowerCAmelCase	:      str = "repeatpad"      ,					**lowerCAmelCase	:      Optional[Any]      ,					)						->					Union[str, Any]:




         """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       UpperCAmelCase		(    self	:      str)						->					Dict[str, Any]:




         """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       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      np.array      ,					lowerCAmelCase	:      Optional[np.array] = None)						->					np.ndarray:




         """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       UpperCAmelCase		(    self	:      int      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Any)						->					List[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       UpperCAmelCase		(    self	:      List[Any]      ,					lowerCAmelCase	:      np.array      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[str])						->					np.array:




         """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	:      Optional[int]      ,					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	:      Union[str, Any]      ,					)						->					BatchFeature:




         """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





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__   :					List[Any] 						=      get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Tuple												=     XLMProphetNetTokenizer
   A     :	List[str]												=     False
   A     :	Union[str, Any]												=     True


   def       UpperCAmelCase		(    self	:      Any)						->					int:




         """simple docstring"""

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'[PAD]'
         lowercase__  =	0

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


   def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]      ,					'[PAD]')
         self.assertEqual(vocab_keys[1]      ,					'[CLS]')
         self.assertEqual(vocab_keys[-1]      ,					'j')
         self.assertEqual(len(lowerCAmelCase)      ,					10_12)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					str:




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size      ,					10_12)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)

         lowercase__  =	tokenizer.tokenize('This is a test')
         self.assertListEqual(lowerCAmelCase      ,					['▁This', '▁is', '▁a', '▁t', 'est'])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,					)

         lowercase__  =	tokenizer.tokenize('I was born in 92000, and this is falsé.')
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]      ,					)
         lowercase__  =	tokenizer.convert_tokens_to_ids(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
             ]      ,					)

         lowercase__  =	tokenizer.convert_ids_to_tokens(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '[UNK]',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '[UNK]',
                 '.',
             ]      ,					)


   @cached_property
   def       UpperCAmelCase		(    self	:      str)						->					List[Any]:




         """simple docstring"""

         return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')


   @slow
   def       UpperCAmelCase		(    self	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	'Hello World!'
         lowercase__  =	[3_53_89, 66_72, 49, 2]
         self.assertListEqual(lowerCAmelCase      ,					self.big_tokenizer.encode(lowerCAmelCase))



   @slow
   def       UpperCAmelCase		(    self	:      Tuple)						->					str:




         """simple docstring"""

         lowercase__  =	{'input_ids': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]], '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, 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, 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, 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, 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, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCAmelCase      ,					model_name='microsoft/xprophetnet-large-wiki100-cased'      ,					revision='1acad1643ddd54a44df6a1b797ada8373685d90e'      ,					)
