





import json
import os
from typing import Optional, Tuple

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


_snake_case    :       Tuple		       =    logging.get_logger(__name__)

_snake_case    :       int		       =    {'vocab_file': 'vocab.json'}

_snake_case    :       List[str]		       =    {
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_snake_case    :       Optional[int]		       =    {'mgp-str': 27}



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					VOCAB_FILES_NAMES
  a_              =					PRETRAINED_VOCAB_FILES_MAP
  a_              =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






  def __init__(							self					:						int   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						Dict="[GO]"   ,    lowerCAmelCase_					:						List[Any]="[GO]"   ,    lowerCAmelCase_					:						List[str]="[s]"   ,    lowerCAmelCase_					:						Dict="[GO]"   ,    **lowerCAmelCase_					:						Optional[Any]	)				->		List[Any]:
     super().__init__(
         unk_token=lowerCAmelCase_   ,    bos_token=lowerCAmelCase_   ,    eos_token=lowerCAmelCase_   ,    pad_token=lowerCAmelCase_   ,    **lowerCAmelCase_   ,    )

     with open(lowerCAmelCase_   ,    encoding='utf-8'	) as vocab_handle:
        __lowerCAmelCase				      =      json.load(lowerCAmelCase_	)
     __lowerCAmelCase				      =      {v: k for k, v in self.vocab.items()}






  @property
  def 							lowercase						(							self					:						str	)				->		Any:
     return len(self.vocab	)






  def 							lowercase						(							self					:						Optional[Any]	)				->		List[Any]:
     return dict(self.vocab   ,    **self.added_tokens_encoder	)






  def 							lowercase						(							self					:						Dict   ,    lowerCAmelCase_					:						Optional[int]	)				->		str:
     __lowerCAmelCase				      =      []
     for s in text:
        char_tokens.extend(lowerCAmelCase_	)
     return char_tokens






  def 							lowercase						(							self					:						str   ,    lowerCAmelCase_					:						str	)				->		Optional[int]:
     return self.vocab.get(lowerCAmelCase_   ,    self.vocab.get(self.unk_token	)	)






  def 							lowercase						(							self					:						Dict   ,    lowerCAmelCase_					:						Any	)				->		Dict:
     return self.decoder.get(lowerCAmelCase_	)






  def 							lowercase						(							self					:						str   ,    lowerCAmelCase_					:						str   ,    lowerCAmelCase_					:						Optional[str] = None	)				->		Tuple[str]:
     if not os.path.isdir(lowerCAmelCase_	):
        logger.error('Vocabulary path ({}) should be a directory'.format(lowerCAmelCase_	)	)
        return
     __lowerCAmelCase				      =      os.path.join(
         lowerCAmelCase_   ,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

     with open(lowerCAmelCase_   ,    'w'   ,    encoding='utf-8'	) as f:
        f.write(json.dumps(self.vocab   ,    indent=2   ,    sort_keys=lowerCAmelCase_   ,    ensure_ascii=lowerCAmelCase_	) + '\n'	)

     return (vocab_file,)





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


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

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







		def __init__( self		,      A=20_48		,      A=1		,      A=[16, 16]		,      A=1_28		,      A=4_41_00		,      A=86		,      A=20_48		,      A=0.0		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(
					    feature_size=A		,      sampling_rate=A		,      padding_value=A		,      **A		,      )

					lowerCamelCase    		=  spectrogram_length
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  feature_size // self.patch_size[1]
					lowerCamelCase    		=  n_fft
					lowerCamelCase    		=  sampling_rate // hop_length_to_sampling_rate
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=A		,      min_frequency=0.0		,      max_frequency=22050.0		,      sampling_rate=A		,      norm="""slaney"""		,      mel_scale="""slaney"""		,      ).T







		def 					__A   ( self		,      A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  spectrogram(
					    A		,      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		,      )
					lowerCamelCase    		=  log_spec[:, :-1]
					lowerCamelCase    		=  log_spec - 20.0
					lowerCamelCase    		=  np.clip(log_spec / 40.0		,      -2.0		,      0.0	) + 1.0
					return log_spec







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







					'''simple docstring'''







					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."""	)

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

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

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

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

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

					lowerCamelCase    		=  BatchFeature(data=A		,      tensor_type=A	)
					return encoded_inputs

