



import logging

from transformers import PretrainedConfig


A_		:     Tuple        =     logging.getLogger(__name__)


A_		:     Dict        =     {
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''bertabs'''

   def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       int=3_0522							,   __lowerCAmelCase			:       Dict=512							,   __lowerCAmelCase			:       Dict=6							,   __lowerCAmelCase			:       Optional[Any]=512							,   __lowerCAmelCase			:       Union[str, Any]=8							,   __lowerCAmelCase			:       Any=512							,   __lowerCAmelCase			:       List[Any]=0.2							,   __lowerCAmelCase			:       List[str]=6							,   __lowerCAmelCase			:       Tuple=768							,   __lowerCAmelCase			:       int=8							,   __lowerCAmelCase			:       str=2048							,   __lowerCAmelCase			:       Any=0.2							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				Optional[int]:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase							)

    a						=		vocab_size
    a						=		max_pos

    a						=		enc_layers
    a						=		enc_hidden_size
    a						=		enc_heads
    a						=		enc_ff_size
    a						=		enc_dropout

    a						=		dec_layers
    a						=		dec_hidden_size
    a						=		dec_heads
    a						=		dec_ff_size
    a						=		dec_dropout






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


A_		:     List[str]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			['''audio_values''', '''audio_mask''']

   def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       Dict=2048							,   __lowerCAmelCase			:       List[Any]=1							,   __lowerCAmelCase			:       Dict=[16, 16]							,   __lowerCAmelCase			:       str=128							,   __lowerCAmelCase			:       Optional[int]=4_4100							,   __lowerCAmelCase			:       int=86							,   __lowerCAmelCase			:       Optional[Any]=2048							,   __lowerCAmelCase			:       str=0.0							,   **__lowerCAmelCase			:       Optional[int]							,   )       ->				Union[str, Any]:


    """simple docstring"""






    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=2_2_0_5_0.0							,   sampling_rate=__lowerCAmelCase							,   norm="slaney"							,   mel_scale="slaney"							,   ).T

   def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       np.array							)       ->				np.ndarray:


    """simple docstring"""






    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=8_0.0							,   )
    a						=		log_spec[:, :-1]
    a						=		log_spec - 2_0.0
    a						=		np.clip(log_spec / 4_0.0							,   -2.0							,   0.0							) + 1.0
    return log_spec




   def __call__(      self			:       Union[str, 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			:       Optional[int]							,   )       ->				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."							)

    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

