



"""simple docstring"""
import warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCAmelCase    :Tuple     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									=['''input_values''', '''attention_mask''']





     def __init__(					self       ,	A = 1       ,	A = 1_6_0_0_0       ,	A = 0.0       ,	A = False       ,	A = 8_0       ,	A = 1_6       ,	A = 6_4       ,	A = "hann_window"       ,	A = 1.0       ,	A = 8_0       ,	A = 7_6_0_0       ,	A = 1E-10       ,	A = 2       ,	A = True       ,	**A       ,	)							->					Any:
         super().__init__(feature_size=A       ,	sampling_rate=A       ,	padding_value=A       ,	**A		)
         _UpperCAmelCase							:  str											=    do_normalize
         _UpperCAmelCase							:  Optional[int]											=    return_attention_mask

         _UpperCAmelCase							:  List[str]											=    num_mel_bins
         _UpperCAmelCase							:  str											=    hop_length
         _UpperCAmelCase							:  Dict											=    win_length
         _UpperCAmelCase							:  Tuple											=    win_function
         _UpperCAmelCase							:  Optional[int]											=    frame_signal_scale
         _UpperCAmelCase							:  Union[str, Any]											=    fmin
         _UpperCAmelCase							:  int											=    fmax
         _UpperCAmelCase							:  Any											=    mel_floor
         _UpperCAmelCase							:  Any											=    reduction_factor

         _UpperCAmelCase							:  List[Any]											=    win_length * sampling_rate // 1_0_0_0
         _UpperCAmelCase							:  Any											=    hop_length * sampling_rate // 1_0_0_0
         _UpperCAmelCase							:  Union[str, Any]											=    optimal_fft_length(self.sample_size		)
         _UpperCAmelCase							:  List[Any]											=    (self.n_fft // 2) + 1

         _UpperCAmelCase							:  Optional[int]											=    window_function(window_length=self.sample_size       ,	name=self.win_function       ,	periodic=A		)

         _UpperCAmelCase							:  str											=    mel_filter_bank(
             num_frequency_bins=self.n_freqs       ,	num_mel_filters=self.num_mel_bins       ,	min_frequency=self.fmin       ,	max_frequency=self.fmax       ,	sampling_rate=self.sampling_rate       ,	norm='''slaney'''       ,	mel_scale='''slaney'''       ,	)

         if frame_signal_scale != 1.0:
             warnings.warn(
                 '''The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers'''       ,	A       ,	)
         if reduction_factor != 2.0:
             warnings.warn(
                 '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''       ,	A       ,	)





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

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

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

         return normed_input_values





     def  __lowerCAmelCase	(					self       ,	A       ,	)							->					np.ndarray:
         _UpperCAmelCase							:  Any											=    spectrogram(
             A       ,	window=self.window       ,	frame_length=self.sample_size       ,	hop_length=self.sample_stride       ,	fft_length=self.n_fft       ,	mel_filters=self.mel_filters       ,	mel_floor=self.mel_floor       ,	log_mel='''log10'''       ,	)
         return log_mel_spec.T





     def __call__(					self       ,	A = None       ,	A = None       ,	A = False       ,	A = None       ,	A = False       ,	A = None       ,	A = None       ,	A = None       ,	A = None       ,	**A       ,	)							->					BatchFeature:
         if audio is None and audio_target is None:
             raise ValueError('''You must provide either `audio` or `audio_target` values.'''		)

         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     f' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     f' {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.'''		)

         if audio is not None:
             _UpperCAmelCase							:  Optional[int]											=    self._process_audio(
                 A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	**A       ,	)
         else:
             _UpperCAmelCase							:  Optional[Any]											=    None

         if audio_target is not None:
             _UpperCAmelCase							:  Optional[Any]											=    self._process_audio(
                 A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	**A       ,	)

             if inputs is None:
                 return inputs_target
             else:
                 _UpperCAmelCase							:  Optional[Any]											=    inputs_target['''input_values''']
                 _UpperCAmelCase							:  str											=    inputs_target.get('''attention_mask'''		)
                 if decoder_attention_mask is not None:
                     _UpperCAmelCase							:  Optional[int]											=    decoder_attention_mask

         return inputs





     def  __lowerCAmelCase	(					self       ,	A       ,	A = False       ,	A = False       ,	A = None       ,	A = False       ,	A = None       ,	A = None       ,	A = None       ,	**A       ,	)							->					BatchFeature:
         _UpperCAmelCase							:  int											=    isinstance(A       ,	np.ndarray		) and len(speech.shape		) > 1
         if is_batched_numpy and len(speech.shape		) > 2:
             raise ValueError(f'Only mono-channel audio is supported for input to {self}'		)
         _UpperCAmelCase							:  Optional[Any]											=    is_batched_numpy or (
             isinstance(A       ,	(list, tuple)		) and (isinstance(speech[0]       ,	(np.ndarray, tuple, list)		))
         )

         if is_batched:
             _UpperCAmelCase							:  Optional[Any]											=    [np.asarray(A       ,	dtype=np.floataa		) for speech in speech]
         elif not is_batched and not isinstance(A       ,	np.ndarray		):
             _UpperCAmelCase							:  List[Any]											=    np.asarray(A       ,	dtype=np.floataa		)
         elif isinstance(A       ,	np.ndarray		) and speech.dtype is np.dtype(np.floataa		):
             _UpperCAmelCase							:  Tuple											=    speech.astype(np.floataa		)

         # always return batch
         if not is_batched:
             _UpperCAmelCase							:  Union[str, Any]											=    [speech]

         # needed to make pad() work on spectrogram inputs
         _UpperCAmelCase							:  Optional[Any]											=    self.feature_size

         # convert into correct format for padding
         if is_target:
             _UpperCAmelCase							:  List[Any]											=    [self._extract_mel_features(A		) for waveform in speech]
             _UpperCAmelCase							:  Optional[Any]											=    BatchFeature({'''input_values''': features}		)
             _UpperCAmelCase							:  Any											=    self.num_mel_bins
         else:
             _UpperCAmelCase							:  Union[str, Any]											=    BatchFeature({'''input_values''': speech}		)

         _UpperCAmelCase							:  Tuple											=    self.pad(
             A       ,	padding=A       ,	max_length=A       ,	truncation=A       ,	pad_to_multiple_of=A       ,	return_attention_mask=A       ,	**A       ,	)

         _UpperCAmelCase							:  Optional[Any]											=    feature_size_hack

         # convert input values to correct format
         _UpperCAmelCase							:  Optional[int]											=    padded_inputs['''input_values''']
         if not isinstance(input_values[0]       ,	np.ndarray		):
             _UpperCAmelCase							:  int											=    [np.asarray(A       ,	dtype=np.floataa		) for array in input_values]
         elif (
             not isinstance(A       ,	np.ndarray		)
             and isinstance(input_values[0]       ,	np.ndarray		)
             and input_values[0].dtype is np.dtype(np.floataa		)
         ):
             _UpperCAmelCase							:  Optional[Any]											=    [array.astype(np.floataa		) for array in input_values]
         elif isinstance(A       ,	np.ndarray		) and input_values.dtype is np.dtype(np.floataa		):
             _UpperCAmelCase							:  int											=    input_values.astype(np.floataa		)

         # convert attention_mask to correct format
         _UpperCAmelCase							:  Union[str, Any]											=    padded_inputs.get('''attention_mask'''		)
         if attention_mask is not None:
             _UpperCAmelCase							:  List[Any]											=    [np.asarray(A       ,	dtype=np.intaa		) for array in attention_mask]

         # zero-mean and unit-variance normalization
         if not is_target and self.do_normalize:
             _UpperCAmelCase							:  Optional[int]											=    (
                 attention_mask
                 if self._get_padding_strategies(A       ,	max_length=A		) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             _UpperCAmelCase							:  str											=    self.zero_mean_unit_var_norm(
                 padded_inputs['''input_values''']       ,	attention_mask=A       ,	padding_value=self.padding_value		)

         if return_tensors is not None:
             _UpperCAmelCase							:  Optional[Any]											=    padded_inputs.convert_to_tensors(A		)

         return padded_inputs





     def  __lowerCAmelCase	(					self		)							->					Dict[str, Any]:
         _UpperCAmelCase							:  List[Any]											=    super().to_dict()

         # Don't serialize these as they are derived from the other properties.
         _UpperCAmelCase							:  Tuple											=    ['''window''', '''mel_filters''', '''sample_size''', '''sample_stride''', '''n_fft''', '''n_freqs''']
         for name in names:
             if name in output:
                 del output[name]

         return output







"""simple docstring"""
import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		Optional[Any]     ,       UpperCamelCase__		:		Union[str, Any]     ,       UpperCamelCase__		:		int     ,       UpperCamelCase__		:		Dict     ,       UpperCamelCase__		:		Optional[Any]							):
    # Load configuration defined in the metadata file
    with open(UpperCamelCase__							) as metadata_file:
        _UpperCAmelCase							:  Dict											=    json.load(UpperCamelCase__							)
    _UpperCAmelCase							:  List[Any]											=    LukeConfig(use_entity_aware_attention=UpperCamelCase__     ,       **metadata['''model_config''']							)

    # Load in the weights from the checkpoint_path
    _UpperCAmelCase							:  List[Any]											=    torch.load(UpperCamelCase__     ,       map_location='''cpu'''							)

    # Load the entity vocab file
    _UpperCAmelCase							:  Optional[int]											=    load_entity_vocab(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    RobertaTokenizer.from_pretrained(metadata['''model_config''']['''bert_model_name''']							)

    # Add special tokens to the token vocabulary for downstream tasks
    _UpperCAmelCase							:  int											=    AddedToken('''<ent>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    _UpperCAmelCase							:  Optional[Any]											=    AddedToken('''<ent2>'''     ,       lstrip=UpperCamelCase__     ,       rstrip=UpperCamelCase__							)
    tokenizer.add_special_tokens({'''additional_special_tokens''': [entity_token_a, entity_token_a]}							)
    config.vocab_size += 2

    print(F'Saving tokenizer to {pytorch_dump_folder_path}'							)
    tokenizer.save_pretrained(UpperCamelCase__							)
    with open(os.path.join(UpperCamelCase__     ,       LukeTokenizer.vocab_files_names['''entity_vocab_file''']							)     ,       '''w'''							) as f:
        json.dump(UpperCamelCase__     ,       UpperCamelCase__							)

    _UpperCAmelCase							:  Any											=    LukeTokenizer.from_pretrained(UpperCamelCase__							)

    # Initialize the embeddings of the special tokens
    _UpperCAmelCase							:  str											=    state_dict['''embeddings.word_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    word_emb[tokenizer.convert_tokens_to_ids(['''@''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Union[str, Any]											=    word_emb[tokenizer.convert_tokens_to_ids(['''#''']							)[0]].unsqueeze(0							)
    _UpperCAmelCase							:  Tuple											=    torch.cat([word_emb, ent_emb, enta_emb]							)

    # Initialize the query layers of the entity-aware self-attention mechanism
    for layer_index in range(config.num_hidden_layers							):
        for matrix_name in ["query.weight", "query.bias"]:
            _UpperCAmelCase							:  List[Any]											=    F'encoder.layer.{layer_index}.attention.self.'
            _UpperCAmelCase							:  Optional[Any]											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  Tuple											=    state_dict[prefix + matrix_name]
            _UpperCAmelCase							:  str											=    state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
    _UpperCAmelCase							:  Any											=    state_dict['''entity_embeddings.entity_embeddings.weight''']
    _UpperCAmelCase							:  Dict											=    entity_emb[entity_vocab['''[MASK]''']]

    _UpperCAmelCase							:  Optional[int]											=    LukeModel(config=UpperCamelCase__							).eval()

    _UpperCAmelCase							,				_UpperCAmelCase							:  int											=    model.load_state_dict(UpperCamelCase__     ,       strict=UpperCamelCase__							)
    if not (len(UpperCamelCase__							) == 1 and missing_keys[0] == "embeddings.position_ids"):
        raise ValueError(F'Missing keys {", ".join(UpperCamelCase__							)}. Expected only missing embeddings.position_ids'							)
    if not (all(key.startswith('''entity_predictions'''							) or key.startswith('''lm_head'''							) for key in unexpected_keys							)):
        raise ValueError(
            '''Unexpected keys'''
            F' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"							) or key.startswith("lm_head"							))]							)}'							)

    # Check outputs
    _UpperCAmelCase							:  Optional[int]											=    LukeTokenizer.from_pretrained(UpperCamelCase__     ,       task='''entity_classification'''							)

    _UpperCAmelCase							:  List[str]											=    (
        '''Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'''
        ''' new world number one avoid a humiliating second- round exit at Wimbledon .'''
    )
    _UpperCAmelCase							:  Dict											=    (39, 42)
    _UpperCAmelCase							:  Any											=    tokenizer(UpperCamelCase__     ,       entity_spans=[span]     ,       add_prefix_space=UpperCamelCase__     ,       return_tensors='''pt'''							)

    _UpperCAmelCase							:  List[Any]											=    model(**UpperCamelCase__							)

    # Verify word hidden states
    if model_size == "large":
        _UpperCAmelCase							:  str											=    torch.Size((1, 42, 1024)							)
        _UpperCAmelCase							:  Union[str, Any]											=    torch.tensor(
            [[0.0133, 0.0865, 0.0095], [0.3093, -0.2576, -0.7418], [-0.1720, -0.2117, -0.2869]]							)
    else:  # base
        _UpperCAmelCase							:  Optional[Any]											=    torch.Size((1, 42, 768)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0037, 0.1368, -0.0091], [0.1099, 0.3329, -0.1095], [0.0765, 0.5335, 0.1179]]							)

    if not (outputs.last_hidden_state.shape == expected_shape):
        raise ValueError(
            F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'							)
    if not torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Verify entity hidden states
    if model_size == "large":
        _UpperCAmelCase							:  int											=    torch.Size((1, 1, 1024)							)
        _UpperCAmelCase							:  str											=    torch.tensor([[0.0466, -0.0106, -0.0179]]							)
    else:  # base
        _UpperCAmelCase							:  List[str]											=    torch.Size((1, 1, 768)							)
        _UpperCAmelCase							:  List[Any]											=    torch.tensor([[0.1457, 0.1044, 0.0174]]							)

    if not (outputs.entity_last_hidden_state.shape != expected_shape):
        raise ValueError(
            F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
            F' {expected_shape}'							)
    if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]     ,       UpperCamelCase__     ,       atol=1E-4							):
        raise ValueError

    # Finally, save our PyTorch model and tokenizer
    print('''Saving PyTorch model to {}'''.format(UpperCamelCase__							)							)
    model.save_pretrained(UpperCamelCase__							)





def  lowerCamelCase_						(UpperCamelCase__		:		Union[str, Any]							):
    _UpperCAmelCase							:  Any											=    {}
    with open(UpperCamelCase__     ,       '''r'''     ,       encoding='''utf-8'''							) as f:
        for index, line in enumerate(UpperCamelCase__							):
            _UpperCAmelCase							,				_UpperCAmelCase							:  Any											=    line.rstrip().split('''\t'''							)
            _UpperCAmelCase							:  Tuple											=    index

    return entity_vocab


if __name__ == "__main__":
  _lowerCAmelCase    :List[Any]     =					argparse.ArgumentParser()
  # Required parameters
  parser.add_argument('--checkpoint_path', type=str, help='Path to a pytorch_model.bin file.')
  parser.add_argument(
      '--metadata_path', default=None, type=str, help='Path to a metadata.json file, defining the configuration.'
  )
  parser.add_argument(
      '--entity_vocab_path',
      default=None,
      type=str,
      help='Path to an entity_vocab.tsv file, containing the entity vocabulary.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path', default=None, type=str, help='Path to where to dump the output PyTorch model.'
  )
  parser.add_argument(
      '--model_size', default='base', type=str, choices=['base', 'large'], help='Size of the model to be converted.'
  )
  _lowerCAmelCase    :Any     =					parser.parse_args()
  convert_luke_checkpoint(
      args.checkpoint_path,
      args.metadata_path,
      args.entity_vocab_path,
      args.pytorch_dump_folder_path,
      args.model_size,
  )


