




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						:   Dict		=   logging.get_logger(__name__)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		["""input_features""", """is_longer"""]
      def __init__(			self   :List[str] ,      lowerCamelCase   :Any=64 ,      lowerCamelCase   :Optional[Any]=4_8000 ,      lowerCamelCase   :Optional[Any]=480 ,      lowerCamelCase   :Any=10 ,      lowerCamelCase   :Optional[Any]=1024 ,      lowerCamelCase   :Any=0.0 ,      lowerCamelCase   :Optional[Any]=False ,      lowerCamelCase   :float = 0 ,      lowerCamelCase   :float = 1_4000 ,      lowerCamelCase   :int = None ,      lowerCamelCase   :str = "fusion" ,      lowerCamelCase   :str = "repeatpad" ,      **lowerCamelCase   :Any ,      )     ->			Optional[Any]:
       super().__init__(
           feature_size=lowerCamelCase ,      sampling_rate=lowerCamelCase ,      padding_value=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      **lowerCamelCase ,      )
       UpperCAmelCase__							 =						top_db
       UpperCAmelCase__							 =						truncation
       UpperCAmelCase__							 =						padding
       UpperCAmelCase__							 =						fft_window_size
       UpperCAmelCase__							 =						(fft_window_size >> 1) + 1
       UpperCAmelCase__							 =						hop_length
       UpperCAmelCase__							 =						max_length_s
       UpperCAmelCase__							 =						max_length_s * sampling_rate
       UpperCAmelCase__							 =						sampling_rate
       UpperCAmelCase__							 =						frequency_min
       UpperCAmelCase__							 =						frequency_max
       UpperCAmelCase__							 =						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" ,      )
       UpperCAmelCase__							 =						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   :Optional[int]      )     ->			Dict[str, Any]:
       UpperCAmelCase__							 =						copy.deepcopy(self.__dict__      )
       UpperCAmelCase__							 =						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:
       UpperCAmelCase__							 =						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   :List[Any] ,      lowerCamelCase   :Optional[Any] ,      lowerCamelCase   :int ,      lowerCamelCase   :Union[str, Any]      )     ->			Union[str, Any]:
       UpperCAmelCase__							 =						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
        UpperCAmelCase__							 =						[0]
       if len(ranges[2]      ) == 0:
        # if the audio is too short, we just use the first chunk
        UpperCAmelCase__							 =						[0]
       # randomly choose index for each part
       UpperCAmelCase__							 =						np.random.choice(ranges[0]      )
       UpperCAmelCase__							 =						np.random.choice(ranges[1]      )
       UpperCAmelCase__							 =						np.random.choice(ranges[2]      )

       UpperCAmelCase__							 =						mel[idx_front : idx_front + chunk_frames, :]
       UpperCAmelCase__							 =						mel[idx_middle : idx_middle + chunk_frames, :]
       UpperCAmelCase__							 =						mel[idx_back : idx_back + chunk_frames, :]

       UpperCAmelCase__							 =						torch.tensor(mel[None, None, :]      )
       UpperCAmelCase__							 =						torch.nn.functional.interpolate(
           lowerCamelCase ,      size=[chunk_frames, 64] ,      mode="bilinear" ,      align_corners=lowerCamelCase      )
       UpperCAmelCase__							 =						mel_shrink[0][0].numpy()
       UpperCAmelCase__							 =						np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] ,      axis=0      )
       return mel_fusion
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :np.array ,      lowerCamelCase   :List[str] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :List[Any]      )     ->			np.array:
       if waveform.shape[0] > max_length:
        if truncation == "rand_trunc":
         UpperCAmelCase__							 =						True
         # random crop to max_length (for compatibility) -> this should be handled by self.pad
         UpperCAmelCase__							 =						len(lowerCamelCase      ) - max_length
         UpperCAmelCase__							 =						np.random.randint(0 ,      overflow + 1      )
         UpperCAmelCase__							 =						waveform[idx : idx + max_length]
         UpperCAmelCase__							 =						self._np_extract_fbank_features(lowerCamelCase ,      self.mel_filters_slaney      )[None, :]
        elif truncation == "fusion":
         UpperCAmelCase__							 =						self._np_extract_fbank_features(lowerCamelCase ,      self.mel_filters      )
         UpperCAmelCase__							 =						max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
         UpperCAmelCase__							 =						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.
          UpperCAmelCase__							 =						np.stack([mel, mel, mel, mel] ,      axis=0      )
          UpperCAmelCase__							 =						False
         else:
          UpperCAmelCase__							 =						self._random_mel_fusion(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase      )
          UpperCAmelCase__							 =						True
        else:
         raise NotImplementedError(f'''data_truncating {truncation} not implemented'''      )

       else:
        UpperCAmelCase__							 =						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":
          UpperCAmelCase__							 =						int(max_length / len(lowerCamelCase      )      )
          UpperCAmelCase__							 =						np.stack(np.tile(lowerCamelCase ,      n_repeat + 1      )      )[:max_length]
         if padding == "repeatpad":
          UpperCAmelCase__							 =						int(max_length / len(lowerCamelCase      )      )
          UpperCAmelCase__							 =						np.stack(np.tile(lowerCamelCase ,      lowerCamelCase      )      )
         UpperCAmelCase__							 =						np.pad(lowerCamelCase ,      (0, max_length - waveform.shape[0]) ,      mode="constant" ,      constant_values=0      )

        if truncation == "fusion":
         UpperCAmelCase__							 =						self._np_extract_fbank_features(lowerCamelCase ,      self.mel_filters      )
         UpperCAmelCase__							 =						np.stack([input_mel, input_mel, input_mel, input_mel] ,      axis=0      )
        else:
         UpperCAmelCase__							 =						self._np_extract_fbank_features(lowerCamelCase ,      self.mel_filters_slaney      )[None, :]

       return input_mel, longer







      def __call__(			self   :str ,      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   :Optional[Any] ,      )     ->			BatchFeature:
       UpperCAmelCase__							 =						truncation if truncation is not None else self.truncation
       UpperCAmelCase__							 =						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."      )

       UpperCAmelCase__							 =						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}'''      )
       UpperCAmelCase__							 =						is_batched_numpy or (
           isinstance(lowerCamelCase ,      (list, tuple)      ) and (isinstance(raw_speech[0] ,      (np.ndarray, tuple, list)      ))
       )

       if is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ) for speech in raw_speech]
       elif not is_batched and not isinstance(lowerCamelCase ,      np.ndarray      ):
        UpperCAmelCase__							 =						np.asarray(lowerCamelCase ,      dtype=np.floataa      )
       elif isinstance(lowerCamelCase ,      np.ndarray      ) and raw_speech.dtype is np.dtype(np.floataa      ):
        UpperCAmelCase__							 =						raw_speech.astype(np.floataa      )

       # always return batch
       if not is_batched:
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase      )]

       # convert to mel spectrogram, truncate and pad if needed.
       UpperCAmelCase__							 =						[
           self._get_input_mel(lowerCamelCase ,      max_length if max_length else self.nb_max_samples ,      lowerCamelCase ,      lowerCamelCase      )
           for waveform in raw_speech
       ]

       UpperCAmelCase__							 =						[]
       UpperCAmelCase__							 =						[]
       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
        UpperCAmelCase__							 =						np.random.randint(0 ,      len(lowerCamelCase      )      )
        UpperCAmelCase__							 =						True

       if isinstance(input_mel[0] ,      lowerCamelCase      ):
        UpperCAmelCase__							 =						[np.asarray(lowerCamelCase ,      dtype=np.floataa      ) for feature in input_mel]

       # is_longer is a list of bool
       UpperCAmelCase__							 =						[[longer] for longer in is_longer]

       UpperCAmelCase__							 =						{"input_features": input_mel, "is_longer": is_longer}
       UpperCAmelCase__							 =						BatchFeature(lowerCamelCase      )

       if return_tensors is not None:
        UpperCAmelCase__							 =						input_features.convert_to_tensors(lowerCamelCase      )

       return input_features











import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCAmelCase						:   Tuple		=   logging.get_logger(__name__)


_lowerCAmelCase						:   Union[str, Any]		=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase						:   str		=   {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase						:   List[Any]		=   {
    "allenai/led-base-16384": 1_6_3_8_4,
}
class 			_UpperCamelCase      (				lowerCAmelCase		):
      UpperCAmelCase_        =		VOCAB_FILES_NAMES
      UpperCAmelCase_        =		PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_        =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_        =		LEDTokenizer
      UpperCAmelCase_        =		["""input_ids""", """attention_mask"""]
      def __init__(			self   :Dict ,      lowerCamelCase   :Any=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :Dict=None ,      lowerCamelCase   :int="replace" ,      lowerCamelCase   :List[Any]="<s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :Optional[Any]="</s>" ,      lowerCamelCase   :int="<s>" ,      lowerCamelCase   :Optional[Any]="<unk>" ,      lowerCamelCase   :Dict="<pad>" ,      lowerCamelCase   :Tuple="<mask>" ,      lowerCamelCase   :str=False ,      lowerCamelCase   :Union[str, Any]=True ,      **lowerCamelCase   :List[str] ,      )     ->			Tuple:
       super().__init__(
           lowerCamelCase ,      lowerCamelCase ,      tokenizer_file=lowerCamelCase ,      errors=lowerCamelCase ,      bos_token=lowerCamelCase ,      eos_token=lowerCamelCase ,      sep_token=lowerCamelCase ,      cls_token=lowerCamelCase ,      unk_token=lowerCamelCase ,      pad_token=lowerCamelCase ,      mask_token=lowerCamelCase ,      add_prefix_space=lowerCamelCase ,      trim_offsets=lowerCamelCase ,      **lowerCamelCase ,      )

       UpperCAmelCase__							 =						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
       if pre_tok_state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
        UpperCAmelCase__							 =						getattr(lowerCamelCase ,      pre_tok_state.pop("type"      )      )
        UpperCAmelCase__							 =						add_prefix_space
        UpperCAmelCase__							 =						pre_tok_class(**lowerCamelCase      )

       UpperCAmelCase__							 =						add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCAmelCase__							 =						"post_processor"
       UpperCAmelCase__							 =						getattr(self.backend_tokenizer ,      lowerCamelCase ,      lowerCamelCase      )
       if tokenizer_component_instance:
        UpperCAmelCase__							 =						json.loads(tokenizer_component_instance.__getstate__()      )

        # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
        if "sep" in state:
         UpperCAmelCase__							 =						tuple(state["sep"]      )
        if "cls" in state:
         UpperCAmelCase__							 =						tuple(state["cls"]      )

        UpperCAmelCase__							 =						False

        if state.get("add_prefix_space" ,      lowerCamelCase      ) != add_prefix_space:
         UpperCAmelCase__							 =						add_prefix_space
         UpperCAmelCase__							 =						True

        if state.get("trim_offsets" ,      lowerCamelCase      ) != trim_offsets:
         UpperCAmelCase__							 =						trim_offsets
         UpperCAmelCase__							 =						True

        if changes_to_apply:
         UpperCAmelCase__							 =						getattr(lowerCamelCase ,      state.pop("type"      )      )
         UpperCAmelCase__							 =						component_class(**lowerCamelCase      )
         setattr(self.backend_tokenizer ,      lowerCamelCase ,      lowerCamelCase      )
      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def 	UpperCAmelCase_						(			self   :List[str]      )     ->			str:
       if self._mask_token is None:
        if self.verbose:
         logger.error("Using mask_token, but it is not set yet."      )
        return None
       return str(self._mask_token      )
      @mask_token.setter
      def 	UpperCAmelCase_						(			self   :Union[str, Any] ,      lowerCamelCase   :List[str]      )     ->			Dict:
       UpperCAmelCase__							 =						AddedToken(lowerCamelCase ,      lstrip=lowerCamelCase ,      rstrip=lowerCamelCase      ) if isinstance(lowerCamelCase ,      lowerCamelCase      ) else value
       UpperCAmelCase__							 =						value
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      *lowerCamelCase   :Any ,      **lowerCamelCase   :int      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						kwargs.get("is_split_into_words" ,      lowerCamelCase      )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."      )

       return super()._batch_encode_plus(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :int ,      *lowerCamelCase   :Optional[Any] ,      **lowerCamelCase   :Optional[int]      )     ->			BatchEncoding:
       UpperCAmelCase__							 =						kwargs.get("is_split_into_words" ,      lowerCamelCase      )

       if is_split_into_words and not self.add_prefix_space:
        raise ValueError(
            f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
            "to use it with pretokenized inputs."      )

       return super()._encode_plus(*lowerCamelCase ,      **lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Any ,      lowerCamelCase   :str ,      lowerCamelCase   :Optional[str] = None      )     ->			Tuple[str]:
       UpperCAmelCase__							 =						self._tokenizer.model.save(lowerCamelCase ,      name=lowerCamelCase      )
       return tuple(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :Optional[Any] ,      lowerCamelCase   :Optional[int] ,      lowerCamelCase   :Optional[Any]=None      )     ->			Dict:
       UpperCAmelCase__							 =						[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
      def 	UpperCAmelCase_						(			self   :Optional[int] ,      lowerCamelCase   :List[int] ,      lowerCamelCase   :Optional[List[int]] = None      )     ->			List[int]:
       UpperCAmelCase__							 =						[self.sep_token_id]
       UpperCAmelCase__							 =						[self.cls_token_id]

       if token_ids_a is None:
        return len(cls + token_ids_a + sep      ) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]







      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :Union[Dict[str, EncodedInput], BatchEncoding] ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :PaddingStrategy = PaddingStrategy.DO_NOT_PAD ,      lowerCamelCase   :Optional[int] = None ,      lowerCamelCase   :Optional[bool] = None ,      )     ->			dict:
       UpperCAmelCase__							 =						super()._pad(
           encoded_inputs=lowerCamelCase ,      max_length=lowerCamelCase ,      padding_strategy=lowerCamelCase ,      pad_to_multiple_of=lowerCamelCase ,      return_attention_mask=lowerCamelCase ,      )

       # Load from model defaults
       if return_attention_mask is None:
        UpperCAmelCase__							 =						"attention_mask" in self.model_input_names

       if return_attention_mask and "global_attention_mask" in encoded_inputs:
        UpperCAmelCase__							 =						encoded_inputs[self.model_input_names[0]]
        # `global_attention_mask` need to have the same length as other (sequential) inputs.
        UpperCAmelCase__							 =						len(encoded_inputs["global_attention_mask"]      ) != len(lowerCamelCase      )

        if needs_to_be_padded:
         UpperCAmelCase__							 =						len(lowerCamelCase      ) - len(encoded_inputs["global_attention_mask"]      )

         if self.padding_side == "right":
          # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
          UpperCAmelCase__							 =						(
              encoded_inputs["global_attention_mask"] + [-1] * difference
          )
         elif self.padding_side == "left":
          UpperCAmelCase__							 =						[-1] * difference + encoded_inputs[
              "global_attention_mask"
          ]
         else:
          raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

       return encoded_inputs





