



"""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


lowercase__    :  Dict							    =	logging.get_logger(__name__)




class        _UpperCAmelCase       (			lowerCAmelCase__):
 _lowerCAmelCase   :			int							=		["""input_values""", """attention_mask"""]

 def __init__(     self		:       List[str]  ,     lowercase_		:       int = 1  ,     lowercase_		:       int = 16000  ,     lowercase_		:       float = 0.0  ,     lowercase_		:       bool = False  ,     lowercase_		:       int = 80  ,     lowercase_		:       int = 16  ,     lowercase_		:       int = 64  ,     lowercase_		:       str = "hann_window"  ,     lowercase_		:       float = 1.0  ,     lowercase_		:       float = 80  ,     lowercase_		:       float = 7600  ,     lowercase_		:       float = 1E-10  ,     lowercase_		:       int = 2  ,     lowercase_		:       bool = True  ,     **lowercase_		:       Dict  ,     ):
     super().__init__(feature_size=lowercase_  ,     sampling_rate=lowercase_  ,     padding_value=lowercase_  ,     **lowercase_				)
     snake_case_ :     int				=	do_normalize
     snake_case_ :     List[Any]				=	return_attention_mask

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

     snake_case_ :     List[str]				=	win_length * sampling_rate // 1000
     snake_case_ :     Any				=	hop_length * sampling_rate // 1000
     snake_case_ :     List[str]				=	optimal_fft_length(self.sample_size				)
     snake_case_ :     List[Any]				=	(self.n_fft // 2) + 1

     snake_case_ :     str				=	window_function(window_length=self.sample_size  ,     name=self.win_function  ,     periodic=lowercase_				)

     snake_case_ :     List[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'''  ,     lowercase_  ,     )
     if reduction_factor != 2.0:
         warnings.warn(
             '''The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers'''  ,     lowercase_  ,     )

 @staticmethod
 # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
 def 						_snake_case					(     lowercase_		:       List[np.ndarray]  ,     lowercase_		:       List[np.ndarray]  ,     lowercase_		:       float = 0.0				):
     if attention_mask is not None:
         snake_case_ :     str				=	np.array(lowercase_  ,     np.intaa				)
         snake_case_ :     Optional[Any]				=	[]

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

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

     return normed_input_values

 def 						_snake_case					(     self		:       Optional[Any]  ,     lowercase_		:       np.ndarray  ,     ):
     snake_case_ :     Union[str, Any]				=	spectrogram(
         lowercase_  ,     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		:       str  ,     lowercase_		:       Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None  ,     lowercase_		:       Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None  ,     lowercase_		:       Union[bool, str, PaddingStrategy] = False  ,     lowercase_		:       Optional[int] = None  ,     lowercase_		:       bool = False  ,     lowercase_		:       Optional[int] = None  ,     lowercase_		:       Optional[bool] = None  ,     lowercase_		:       Optional[Union[str, TensorType]] = None  ,     lowercase_		:       Optional[int] = None  ,     **lowercase_		:       List[str]  ,     ):
     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:
         snake_case_ :     str				=	self._process_audio(
             lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     **lowercase_  ,     )
     else:
         snake_case_ :     List[str]				=	None

     if audio_target is not None:
         snake_case_ :     int				=	self._process_audio(
             lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_  ,     **lowercase_  ,     )

         if inputs is None:
             return inputs_target
         else:
             snake_case_ :     Tuple				=	inputs_target['''input_values''']
             snake_case_ :     List[Any]				=	inputs_target.get('''attention_mask'''				)
             if decoder_attention_mask is not None:
                 snake_case_ :     Optional[Any]				=	decoder_attention_mask

     return inputs

 def 						_snake_case					(     self		:       Union[str, Any]  ,     lowercase_		:       Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]  ,     lowercase_		:       bool = False  ,     lowercase_		:       Union[bool, str, PaddingStrategy] = False  ,     lowercase_		:       Optional[int] = None  ,     lowercase_		:       bool = False  ,     lowercase_		:       Optional[int] = None  ,     lowercase_		:       Optional[bool] = None  ,     lowercase_		:       Optional[Union[str, TensorType]] = None  ,     **lowercase_		:       Dict  ,     ):
     snake_case_ :     Dict				=	isinstance(lowercase_  ,     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}"				)
     snake_case_ :     Union[str, Any]				=	is_batched_numpy or (
         isinstance(lowercase_  ,     (list, tuple)				) and (isinstance(speech[0]  ,     (np.ndarray, tuple, list)				))
     )

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

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

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

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

     snake_case_ :     int				=	self.pad(
         lowercase_  ,     padding=lowercase_  ,     max_length=lowercase_  ,     truncation=lowercase_  ,     pad_to_multiple_of=lowercase_  ,     return_attention_mask=lowercase_  ,     **lowercase_  ,     )

     snake_case_ :     Dict				=	feature_size_hack

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

     # convert attention_mask to correct format
     snake_case_ :     Tuple				=	padded_inputs.get('''attention_mask'''				)
     if attention_mask is not None:
         snake_case_ :     Optional[Any]				=	[np.asarray(lowercase_  ,     dtype=np.intaa				) for array in attention_mask]

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

     if return_tensors is not None:
         snake_case_ :     List[str]				=	padded_inputs.convert_to_tensors(lowercase_				)

     return padded_inputs






 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Optional[Any]				=	super().to_dict()

     # Don't serialize these as they are derived from the other properties.
     snake_case_ :     Optional[int]				=	['''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 numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def __lowercase			(							_a		,			_a    ):
    # Load checkpoint
    snake_case_ :     Optional[Any]				=	torch.load(_a		,			map_location='''cpu'''    )

    snake_case_ :     Union[str, Any]				=	chkpt['''model''']

    # We have the base model one level deeper than the original XLM repository
    snake_case_ :     Dict				=	{}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            snake_case_ :     Union[str, Any]				=	v
        else:
            snake_case_ :     Dict				=	v

    snake_case_ :     Union[str, Any]				=	chkpt['''params''']
    snake_case_ :     int				=	{n: v for n, v in config.items() if not isinstance(_a		,			(torch.FloatTensor, numpy.ndarray)    )}

    snake_case_ :     int				=	chkpt['''dico_word2id''']
    snake_case_ :     str				=	{s + '''</w>''' if s.find('''@@'''    ) == -1 and i > 13 else s.replace('''@@'''		,			''''''    ): i for s, i in vocab.items()}

    # Save pytorch-model
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    snake_case_ :     Union[str, Any]				=	pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    snake_case_ :     Any				=	pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

    print(f"Save PyTorch model to {pytorch_weights_dump_path}"    )
    torch.save(_a		,			_a    )

    print(f"Save configuration file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )

    print(f"Save vocab file to {pytorch_config_dump_path}"    )
    with open(_a		,			'''w'''		,			encoding='''utf-8'''    ) as f:
        f.write(json.dumps(_a		,			indent=2    ) + '''\n'''    )


if __name__ == "__main__":
     lowercase__    :  Optional[Any]							    =	argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--xlm_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     lowercase__    :  List[str]							    =	parser.parse_args()
     convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





