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


lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)


class 	__snake_case	( UpperCAmelCase__		):





      '''simple docstring'''






      _snake_case    		=       ["input_features", "is_longer"]



      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Optional[Any]=64    ,  _UpperCamelCase     :				int=4_8000    ,  _UpperCamelCase     :				str=480    ,  _UpperCamelCase     :				str=10    ,  _UpperCamelCase     :				List[str]=1024    ,  _UpperCamelCase     :				Any=0.0    ,  _UpperCamelCase     :				int=False    ,  _UpperCamelCase     :				float = 0    ,  _UpperCamelCase     :				float = 1_4000    ,  _UpperCamelCase     :				int = None    ,  _UpperCamelCase     :				str = "fusion"    ,  _UpperCamelCase     :				str = "repeatpad"    ,  **_UpperCamelCase     :				Optional[Any]    ,  )				->str:


          """simple docstring"""


          super().__init__(
              feature_size=lowerCamelCase__    ,  sampling_rate=lowerCamelCase__    ,  padding_value=lowerCamelCase__    ,  return_attention_mask=lowerCamelCase__    ,  **lowerCamelCase__    ,  )
          _lowerCamelCase     :		int 						=						top_db
          _lowerCamelCase     :		Any 						=						truncation
          _lowerCamelCase     :		Optional[Any] 						=						padding
          _lowerCamelCase     :		int 						=						fft_window_size
          _lowerCamelCase     :		Optional[int] 						=						(fft_window_size >> 1) + 1
          _lowerCamelCase     :		Optional[int] 						=						hop_length
          _lowerCamelCase     :		Union[str, Any] 						=						max_length_s
          _lowerCamelCase     :		Union[str, Any] 						=						max_length_s * sampling_rate
          _lowerCamelCase     :		List[str] 						=						sampling_rate
          _lowerCamelCase     :		Optional[int] 						=						frequency_min
          _lowerCamelCase     :		List[Any] 						=						frequency_max
          _lowerCamelCase     :		Union[str, Any] 						=						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"""    ,  )
          _lowerCamelCase     :		Tuple 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Dict[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						copy.deepcopy(self.__dict__)
          _lowerCamelCase     :		Optional[Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				np.array    ,  _UpperCamelCase     :				Optional[np.array] = None)				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				str)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						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
              _lowerCamelCase     :		int 						=						[0]
          if len(ranges[2]) == 0:
              # if the audio is too short, we just use the first chunk
              _lowerCamelCase     :		List[Any] 						=						[0]
          # randomly choose index for each part
          _lowerCamelCase     :		List[Any] 						=						np.random.choice(ranges[0])
          _lowerCamelCase     :		List[str] 						=						np.random.choice(ranges[1])
          _lowerCamelCase     :		List[str] 						=						np.random.choice(ranges[2])

          _lowerCamelCase     :		Dict 						=						mel[idx_front : idx_front + chunk_frames, :]
          _lowerCamelCase     :		int 						=						mel[idx_middle : idx_middle + chunk_frames, :]
          _lowerCamelCase     :		int 						=						mel[idx_back : idx_back + chunk_frames, :]

          _lowerCamelCase     :		Dict 						=						torch.tensor(mel[None, None, :])
          _lowerCamelCase     :		Any 						=						torch.nn.functional.interpolate(
              lowerCamelCase__    ,  size=[chunk_frames, 64]    ,  mode="""bilinear"""    ,  align_corners=lowerCamelCase__)
          _lowerCamelCase     :		List[str] 						=						mel_shrink[0][0].numpy()
          _lowerCamelCase     :		List[str] 						=						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,  axis=0)
          return mel_fusion



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				np.array    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				str)				->np.array:


          """simple docstring"""


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

          else:
              _lowerCamelCase     :		Union[str, Any] 						=						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":
                      _lowerCamelCase     :		Optional[int] 						=						int(max_length / len(lowerCamelCase__))
                      _lowerCamelCase     :		int 						=						np.stack(np.tile(lowerCamelCase__    ,  n_repeat + 1))[:max_length]
                  if padding == "repeatpad":
                      _lowerCamelCase     :		Optional[Any] 						=						int(max_length / len(lowerCamelCase__))
                      _lowerCamelCase     :		str 						=						np.stack(np.tile(lowerCamelCase__    ,  lowerCamelCase__))
                  _lowerCamelCase     :		Any 						=						np.pad(lowerCamelCase__    ,  (0, max_length - waveform.shape[0])    ,  mode="""constant"""    ,  constant_values=0)

              if truncation == "fusion":
                  _lowerCamelCase     :		List[Any] 						=						self._np_extract_fbank_features(lowerCamelCase__    ,  self.mel_filters)
                  _lowerCamelCase     :		Tuple 						=						np.stack([input_mel, input_mel, input_mel, input_mel]    ,  axis=0)
              else:
                  _lowerCamelCase     :		List[str] 						=						self._np_extract_fbank_features(lowerCamelCase__    ,  self.mel_filters_slaney)[None, :]

          return input_mel, longer




      def __call__(					self     :				str    ,  _UpperCamelCase     :				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,  _UpperCamelCase     :				str = None    ,  _UpperCamelCase     :				Optional[str] = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[int] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  **_UpperCamelCase     :				str    ,  )				->BatchFeature:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						truncation if truncation is not None else self.truncation
          _lowerCamelCase     :		Any 						=						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.""")

          _lowerCamelCase     :		Tuple 						=						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}""")
          _lowerCamelCase     :		int 						=						is_batched_numpy or (
              isinstance(lowerCamelCase__    ,  (list, tuple)) and (isinstance(raw_speech[0]    ,  (np.ndarray, tuple, list)))
          )

          if is_batched:
              _lowerCamelCase     :		Tuple 						=						[np.asarray(lowerCamelCase__    ,  dtype=np.floataa) for speech in raw_speech]
          elif not is_batched and not isinstance(lowerCamelCase__    ,  np.ndarray):
              _lowerCamelCase     :		int 						=						np.asarray(lowerCamelCase__    ,  dtype=np.floataa)
          elif isinstance(lowerCamelCase__    ,  np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
              _lowerCamelCase     :		str 						=						raw_speech.astype(np.floataa)

          # always return batch
          if not is_batched:
              _lowerCamelCase     :		int 						=						[np.asarray(lowerCamelCase__)]

          # convert to mel spectrogram, truncate and pad if needed.
          _lowerCamelCase     :		List[str] 						=						[
              self._get_input_mel(lowerCamelCase__    ,  max_length if max_length else self.nb_max_samples    ,  lowerCamelCase__    ,  lowerCamelCase__)
              for waveform in raw_speech
          ]

          _lowerCamelCase     :		Optional[int] 						=						[]
          _lowerCamelCase     :		Tuple 						=						[]
          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
              _lowerCamelCase     :		List[Any] 						=						np.random.randint(0    ,  len(lowerCamelCase__))
              _lowerCamelCase     :		List[str] 						=						True

          if isinstance(input_mel[0]    ,  lowerCamelCase__):
              _lowerCamelCase     :		Dict 						=						[np.asarray(lowerCamelCase__    ,  dtype=np.floataa) for feature in input_mel]

          # is_longer is a list of bool
          _lowerCamelCase     :		Any 						=						[[longer] for longer in is_longer]

          _lowerCamelCase     :		str 						=						{"input_features": input_mel, "is_longer": is_longer}
          _lowerCamelCase     :		Dict 						=						BatchFeature(lowerCamelCase__)

          if return_tensors is not None:
              _lowerCamelCase     :		Optional[int] 						=						input_features.convert_to_tensors(lowerCamelCase__)

          return input_features





import json
import os
from typing import Optional, Tuple

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


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

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

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

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

          return (vocab_file,)




