


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

class 		__a				(    UpperCAmelCase      ):
		_a     :  int           =     ['audio_values', 'audio_mask']
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=2048      ,   _SCREAMING_SNAKE_CASE=1      ,   _SCREAMING_SNAKE_CASE=[16, 16]      ,   _SCREAMING_SNAKE_CASE=128      ,   _SCREAMING_SNAKE_CASE=44100      ,   _SCREAMING_SNAKE_CASE=86      ,   _SCREAMING_SNAKE_CASE=2048      ,   _SCREAMING_SNAKE_CASE=0.0      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	Union[str, Any]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      spectrogram_length
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      patch_size
									_UpperCAmelCase					=      feature_size // self.patch_size[1]
									_UpperCAmelCase					=      n_fft
									_UpperCAmelCase					=      sampling_rate // hop_length_to_sampling_rate
									_UpperCAmelCase					=      sampling_rate
									_UpperCAmelCase					=      padding_value
									_UpperCAmelCase					=      mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2      ,   num_mel_filters=_SCREAMING_SNAKE_CASE      ,   min_frequency=0.0      ,   max_frequency=22050.0      ,   sampling_rate=_SCREAMING_SNAKE_CASE      ,   norm='slaney'      ,   mel_scale='slaney'      ,   ).T
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE						)			->	np.ndarray:




									"""simple docstring"""





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






		def __call__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = True      ,   _SCREAMING_SNAKE_CASE = None      ,   _SCREAMING_SNAKE_CASE = False      ,   _SCREAMING_SNAKE_CASE = False      ,   **_SCREAMING_SNAKE_CASE      ,   )			->	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.'						)

									_UpperCAmelCase					=      isinstance(_SCREAMING_SNAKE_CASE      ,   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(_SCREAMING_SNAKE_CASE      ,   (list, tuple)						) and (isinstance(raw_speech[0]      ,   (np.ndarray, tuple, list)						))
									)
									if is_batched:
																_UpperCAmelCase					=      [np.asarray([speech]      ,   dtype=np.floataa						).T for speech in raw_speech]
									elif not is_batched and not isinstance(_SCREAMING_SNAKE_CASE      ,   np.ndarray						):
																_UpperCAmelCase					=      np.asarray(_SCREAMING_SNAKE_CASE      ,   dtype=np.floataa						)
									elif isinstance(_SCREAMING_SNAKE_CASE      ,   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([raw_speech]						).T]

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

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

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

									# return as BatchFeature
									if return_attention_mask:
																_UpperCAmelCase					=      {'audio_values': padded_audio_features, 'audio_mask': audio_mask}
									else:
																_UpperCAmelCase					=      {'audio_values': padded_audio_features}

									_UpperCAmelCase					=      BatchFeature(data=_SCREAMING_SNAKE_CASE      ,   tensor_type=_SCREAMING_SNAKE_CASE						)
									return encoded_inputs




import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel

class 		__a				(    pl.LightningModule      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									super().__init__()
									_UpperCAmelCase					=      model
									_UpperCAmelCase					=      2
									_UpperCAmelCase					=      nn.Linear(self.model.config.hidden_size      ,   self.num_labels						)






		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									pass



def 	lowerCAmelCase__			(      a__:		str      ,  a__:		str      ,  a__:		str	)  ->		Tuple:


							'''simple docstring'''





							_UpperCAmelCase					=      LongformerModel.from_pretrained(a__	)
							_UpperCAmelCase					=      LightningModel(a__	)

							_UpperCAmelCase					=      torch.load(a__      ,  map_location=torch.device('cpu'	)	)
							lightning_model.load_state_dict(ckpt['state_dict']	)

							# init longformer question answering model
							_UpperCAmelCase					=      LongformerForQuestionAnswering.from_pretrained(a__	)

							# transfer weights
							longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()	)
							longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()	)
							longformer_for_qa.eval()

							# save model
							longformer_for_qa.save_pretrained(a__	)

							print(F'''Conversion successful. Model saved under {pytorch_dump_folder_path}'''	)


if __name__ == "__main__":
		lowerCAmelCase__  :Optional[int]             =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--longformer_model''',
		    default=None,
		    type=str,
		    required=True,
		    help='''model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.''',
		)
		parser.add_argument(
		    '''--longformer_question_answering_ckpt_path''',
		    default=None,
		    type=str,
		    required=True,
		    help='''Path the official PyTorch Lightning Checkpoint.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowerCAmelCase__  :Dict             =							parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)
