'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

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


__a: Dict          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    ["input_features"]

       def __init__(						self	,   __lowerCAmelCase=80	,   __lowerCAmelCase=16000	,   __lowerCAmelCase=160	,   __lowerCAmelCase=30	,   __lowerCAmelCase=400	,   __lowerCAmelCase=0.0	,   __lowerCAmelCase=False	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        super().__init__(
            feature_size=__lowerCAmelCase	,   sampling_rate=__lowerCAmelCase	,   padding_value=__lowerCAmelCase	,   return_attention_mask=__lowerCAmelCase	,   **__lowerCAmelCase	,   )
        lowercase__				:    Dict  =					n_fft
        lowercase__				:    Any  =					hop_length
        lowercase__				:    Any  =					chunk_length
        lowercase__				:    Tuple  =					chunk_length * sampling_rate
        lowercase__				:    Any  =					self.n_samples // hop_length
        lowercase__				:    List[Any]  =					sampling_rate
        lowercase__				:    Optional[int]  =					mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2	,   num_mel_filters=__lowerCAmelCase	,   min_frequency=0.0	,   max_frequency=8_0_0_0.0	,   sampling_rate=__lowerCAmelCase	,   norm='''slaney'''	,   mel_scale='''slaney'''	,   )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  np.ndarray:
        lowercase__				:    Any  =					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	,   log_mel='''log10'''	,   )
        lowercase__				:    Tuple  =					log_spec[:, :-1]
        lowercase__				:    Union[str, Any]  =					np.maximum(__lowerCAmelCase	,   log_spec.max() - 8.0 )
        lowercase__				:    List[Any]  =					(log_spec + 4.0) / 4.0
        return log_spec

       @staticmethod
       # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
       def _lowerCAmelCase(						__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = 0.0 )  ->  List[np.ndarray]:
        if attention_mask is not None:
         lowercase__				:    Dict  =					np.array(__lowerCAmelCase	,   np.intaa )
         lowercase__				:    int  =					[]

         for vector, length in zip(__lowerCAmelCase	,   attention_mask.sum(-1 ) ):
          lowercase__				:    Optional[Any]  =					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7 )
          if length < normed_slice.shape[0]:
           lowercase__				:    str  =					padding_value

          normed_input_values.append(__lowerCAmelCase )
        else:
         lowercase__				:    str  =					[(x - x.mean()) / np.sqrt(x.var() + 1E-7 ) for x in input_values]

        return normed_input_values

       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = "max_length"	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  BatchFeature:

        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.''' )

        lowercase__				:    Dict  =					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}""" )
        lowercase__				:    int  =					is_batched_numpy or (
            isinstance(__lowerCAmelCase	,   (list, tuple) ) and (isinstance(raw_speech[0]	,   (np.ndarray, tuple, list) ))
        )

        if is_batched:
         lowercase__				:    int  =					[np.asarray([speech]	,   dtype=np.floataa ).T for speech in raw_speech]
        elif not is_batched and not isinstance(__lowerCAmelCase	,   np.ndarray ):
         lowercase__				:    Optional[Any]  =					np.asarray(__lowerCAmelCase	,   dtype=np.floataa )
        elif isinstance(__lowerCAmelCase	,   np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
         lowercase__				:    str  =					raw_speech.astype(np.floataa )

        # always return batch
        if not is_batched:
         lowercase__				:    Optional[int]  =					[np.asarray([raw_speech] ).T]

        lowercase__				:    str  =					BatchFeature({'''input_features''': raw_speech} )

        # convert into correct format for padding

        lowercase__				:    str  =					self.pad(
            __lowerCAmelCase	,   padding=__lowerCAmelCase	,   max_length=max_length if max_length else self.n_samples	,   truncation=__lowerCAmelCase	,   pad_to_multiple_of=__lowerCAmelCase	,   return_attention_mask=return_attention_mask or do_normalize	,   )

        # zero-mean and unit-variance normalization
        if do_normalize:
         lowercase__				:    Any  =					self.zero_mean_unit_var_norm(
             padded_inputs['''input_features''']	,   attention_mask=padded_inputs['''attention_mask''']	,   padding_value=self.padding_value	,   )
         lowercase__				:    Union[str, Any]  =					np.stack(padded_inputs['''input_features''']	,   axis=0 )

        # make sure list is in array format
        lowercase__				:    Optional[int]  =					padded_inputs.get('''input_features''' ).transpose(2	,   0	,   1 )

        lowercase__				:    Any  =					[self._np_extract_fbank_features(__lowerCAmelCase ) for waveform in input_features[0]]

        if isinstance(input_features[0]	,   __lowerCAmelCase ):
         lowercase__				:    Any  =					[np.asarray(__lowerCAmelCase	,   dtype=np.floataa ) for feature in input_features]
        else:
         lowercase__				:    Any  =					input_features

        if return_attention_mask:
         # rescale from sample (48000) to feature (3000)
         lowercase__				:    Any  =					padded_inputs['''attention_mask'''][:, :: self.hop_length]

        if return_tensors is not None:
         lowercase__				:    Union[str, Any]  =					padded_inputs.convert_to_tensors(__lowerCAmelCase )

        return padded_inputs


       def _lowerCAmelCase(						self )  ->  Dict[str, Any]:
        lowercase__				:    Tuple  =					copy.deepcopy(self.__dict__ )
        lowercase__				:    List[str]  =					self.__class__.__name__
        if "mel_filters" in output:
         del output["mel_filters"]
        return output


'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel



class 			UpperCAmelCase  (						pl.LightningModule					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[str]:
        super().__init__()
        lowercase__				:    List[str]  =					model
        lowercase__				:    Dict  =					2
        lowercase__				:    Any  =					nn.Linear(self.model.config.hidden_size	,   self.num_labels )


       def _lowerCAmelCase(						self )  ->  str:
        pass


def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 # load longformer model from model identifier
 lowercase__				:    Dict  =					LongformerModel.from_pretrained(UpperCAmelCase     )
 lowercase__				:    List[str]  =					LightningModel(UpperCAmelCase     )

 lowercase__				:    List[Any]  =					torch.load(UpperCAmelCase		,     map_location=torch.device('''cpu'''     )     )
 lightning_model.load_state_dict(ckpt['''state_dict''']     )

 # init longformer question answering model
 lowercase__				:    Optional[int]  =					LongformerForQuestionAnswering.from_pretrained(UpperCAmelCase     )

 # 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(UpperCAmelCase     )

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


if __name__ == "__main__":
  __a: List[Any]          =   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."""
  )
  __a: Tuple          =   parser.parse_args()
  convert_longformer_qa_checkpoint_to_pytorch(
      args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
  )

