




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


__lowerCAmelCase		:				Tuple       			=		logging.get_logger(__name__)




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							int							 =					["""audio_values""", """audio_mask"""]





				def __init__(      self				:     int					,      __lowerCamelCase				:     Optional[int]=20_48					,      __lowerCamelCase				:     List[str]=1					,      __lowerCamelCase				:     Union[str, Any]=[16, 16]					,      __lowerCamelCase				:     Union[str, Any]=1_28					,      __lowerCamelCase				:     Any=4_41_00					,      __lowerCamelCase				:     str=86					,      __lowerCamelCase				:     str=20_48					,      __lowerCamelCase				:     Tuple=0.0					,      **__lowerCamelCase				:     List[str]					,      )					->     List[Any]:
							super().__init__(
							    feature_size=__lowerCamelCase					,      sampling_rate=__lowerCamelCase					,      padding_value=__lowerCamelCase					,      **__lowerCamelCase					,      )

							a    =			spectrogram_length
							a    =			num_channels
							a    =			patch_size
							a    =			feature_size // self.patch_size[1]
							a    =			n_fft
							a    =			sampling_rate // hop_length_to_sampling_rate
							a    =			sampling_rate
							a    =			padding_value
							a    =			mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2					,      num_mel_filters=__lowerCamelCase					,      min_frequency=0.0					,      max_frequency=22_050.0					,      sampling_rate=__lowerCamelCase					,      norm="slaney"					,      mel_scale="slaney"					,      ).T





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     np.array							)					->     np.ndarray:
							a    =			spectrogram(
							    __lowerCamelCase					,      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					,      )
							a    =			log_spec[:, :-1]
							a    =			log_spec - 20.0
							a    =			np.clip(log_spec / 40.0					,      -2.0					,      0.0							) + 1.0
							return log_spec





				def __call__(      self				:     List[Any]					,      __lowerCamelCase				:     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,      __lowerCamelCase				:     Optional[Union[str, TensorType]] = None					,      __lowerCamelCase				:     Optional[bool] = True					,      __lowerCamelCase				:     Optional[int] = None					,      __lowerCamelCase				:     bool = False					,      __lowerCamelCase				:     bool = False					,      **__lowerCamelCase				:     List[str]					,      )					->     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."							)

							a    =			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}"""							)
							a    =			is_batched_numpy or (
							    isinstance(__lowerCamelCase					,      (list, tuple)							) and (isinstance(raw_speech[0]					,      (np.ndarray, tuple, list)							))
							)
							if is_batched:
										a    =			[np.asarray([speech]					,      dtype=np.floataa							).T for speech in raw_speech]
							elif not is_batched and not isinstance(__lowerCamelCase					,      np.ndarray							):
										a    =			np.asarray(__lowerCamelCase					,      dtype=np.floataa							)
							elif isinstance(__lowerCamelCase					,      np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
										a    =			raw_speech.astype(np.floataa							)
							# always return batch
							if not is_batched:
										a    =			[np.asarray([raw_speech]							).T]

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

							# Create audio attention mask
							a    =			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:
										a    =			[
										    (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
										]
										a    =			np.array(__lowerCamelCase							).astype(np.floataa							)

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

							# return as BatchFeature
							if return_attention_mask:
										a    =			{"audio_values": padded_audio_features, "audio_mask": audio_mask}
							else:
										a    =			{"audio_values": padded_audio_features}

							a    =			BatchFeature(data=__lowerCamelCase					,      tensor_type=__lowerCamelCase							)
							return encoded_inputs












import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase		:				str       			=		logging.get_logger(__name__)

__lowerCAmelCase		:				Dict       			=		{'vocab_file': 'spiece.model'}

__lowerCAmelCase		:				Optional[int]       			=		{
    'vocab_file': {
        'bert_for_seq_generation': (
            'https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'
        ),
    }
}

__lowerCAmelCase		:				Dict       			=		{'bert_for_seq_generation': 512}




class     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Any							 =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :							Union[str, Any]							 =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :							int							 =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :							List[int]							 =					[]
				SCREAMING_SNAKE_CASE_       :							Optional[Any]							 =					["""input_ids""", """attention_mask"""]





				def __init__(      self				:     List[Any]					,      __lowerCamelCase				:     Tuple					,      __lowerCamelCase				:     str="<s>"					,      __lowerCamelCase				:     Optional[int]="</s>"					,      __lowerCamelCase				:     int="<unk>"					,      __lowerCamelCase				:     List[str]="<pad>"					,      __lowerCamelCase				:     Union[str, Any]="<::::>"					,      __lowerCamelCase				:     Optional[Dict[str, Any]] = None					,      **__lowerCamelCase				:     List[str]					,      )					->     None:
							a    =			{} if sp_model_kwargs is None else sp_model_kwargs

							# Add extra_ids to the special token list
							super().__init__(
							    bos_token=__lowerCamelCase					,      eos_token=__lowerCamelCase					,      unk_token=__lowerCamelCase					,      pad_token=__lowerCamelCase					,      sep_token=__lowerCamelCase					,      sp_model_kwargs=self.sp_model_kwargs					,      **__lowerCamelCase					,      )

							a    =			vocab_file

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(__lowerCamelCase							)





				@property
				def 							__UpperCAmelCase							(      self				:     Dict							)					->     Dict:
							return self.sp_model.get_piece_size()





				def 							__UpperCAmelCase							(      self				:     Tuple							)					->     Optional[int]:
							a    =			{self.convert_ids_to_tokens(__lowerCamelCase							): i for i in range(self.vocab_size							)}
							vocab.update(self.added_tokens_encoder							)
							return vocab





				def __getstate__(      self				:     Dict							)					->     Optional[Any]:
							a    =			self.__dict__.copy()
							a    =			None
							return state





				def __setstate__(      self				:     Optional[Any]					,      __lowerCamelCase				:     Dict							)					->     Optional[Any]:
							a    =			d

							# for backward compatibility
							if not hasattr(self					,      "sp_model_kwargs"							):
										a    =			{}

							a    =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
							self.sp_model.Load(self.vocab_file							)





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     str							)					->     List[str]:
							return self.sp_model.encode(__lowerCamelCase					,      out_type=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     str					,      __lowerCamelCase				:     Union[str, Any]							)					->     int:
							return self.sp_model.piece_to_id(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[str]							)					->     Any:
							a    =			self.sp_model.IdToPiece(__lowerCamelCase							)
							return token





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     Dict							)					->     Any:
							a    =			[]
							a    =			""
							for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
													out_string += self.sp_model.decode(__lowerCamelCase							) + token
													a    =			[]
										else:
													current_sub_tokens.append(__lowerCamelCase							)
							out_string += self.sp_model.decode(__lowerCamelCase							)
							return out_string.strip()





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     str					,      __lowerCamelCase				:     Optional[str] = None							)					->     Tuple[str]:
							if not os.path.isdir(__lowerCamelCase							):
										logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
										return
							a    =			os.path.join(
							    __lowerCamelCase					,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

							if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowerCamelCase							) and os.path.isfile(self.vocab_file							):
										copyfile(self.vocab_file					,      __lowerCamelCase							)
							elif not os.path.isfile(self.vocab_file							):
										with open(__lowerCamelCase					,      "wb"							) as fi:
													a    =			self.sp_model.serialized_model_proto()
													fi.write(__lowerCamelCase							)

							return (out_vocab_file,)






