





"""simple docstring"""




from typing import 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 ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


lowerCamelCase_     :						Tuple						=					logging.get_logger(__name__)

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""input_features""", """attention_mask"""]

      def __init__(     self					,      snake_case_=8_0					,      snake_case_=1_6_0_0_0					,      snake_case_=0.0					,      snake_case_=1_0					,      snake_case_=2_5					,      snake_case_="hamming_window"					,      snake_case_=3_27_68.0					,      snake_case_=0.97					,      snake_case_=1.0					,      snake_case_=True					,      snake_case_=True					,      snake_case_=False					,      **snake_case_					,      ):

        """simple docstring"""

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

        A_  :       List[Any]										=  feature_size
        A_  :       str										=  sampling_rate
        A_  :       List[str]										=  padding_value
        A_  :       List[Any]										=  hop_length
        A_  :       Optional[int]										=  win_length
        A_  :       str										=  frame_signal_scale
        A_  :       str										=  preemphasis_coeff
        A_  :       Union[str, Any]										=  mel_floor
        A_  :       Any										=  normalize_means
        A_  :       Union[str, Any]										=  normalize_vars
        A_  :       int										=  win_function
        A_  :       Tuple										=  return_attention_mask

        A_  :       Tuple										=  win_length * sampling_rate // 1_0_0_0
        A_  :       Tuple										=  hop_length * sampling_rate // 1_0_0_0

        A_  :       List[Any]										=  optimal_fft_length(self.sample_size  )
        A_  :       Union[str, Any]										=  (self.n_fft // 2) + 1

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if self.win_function == "hamming_window":
          A_  :       List[str]										=  window_function(window_length=self.sample_size					,      name=self.win_function					,      periodic=snake_case_  )
        else:
          A_  :       Optional[int]										=  window_function(window_length=self.sample_size					,      name=self.win_function  )

        A_  :       int										=  mel_filter_bank(
            num_frequency_bins=self.n_freqs					,      num_mel_filters=self.feature_size					,      min_frequency=0.0					,      max_frequency=self.sampling_rate / 2.0					,      sampling_rate=self.sampling_rate					,      )

        A_  :       Any										=  spectrogram(
            one_waveform * self.frame_signal_scale					,      window=snake_case_					,      frame_length=self.sample_size					,      hop_length=self.sample_stride					,      fft_length=self.n_fft					,      center=snake_case_					,      preemphasis=self.preemphasis_coeff					,      mel_filters=snake_case_					,      mel_floor=self.mel_floor					,      log_mel='log'					,      )
        return msfc_features.T

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        if self.normalize_means:
          A_  :       str										=  x[:input_length].mean(axis=0  )
          A_  :       List[Any]										=  np.subtract(snake_case_					,      snake_case_  )
        if self.normalize_vars:
          A_  :       str										=  x[:input_length].std(axis=0  )
          A_  :       Tuple										=  np.divide(snake_case_					,      snake_case_  )

        if input_length < x.shape[0]:
          A_  :       List[Any]										=  padding_value

        # make sure array is in float32
        A_  :       Union[str, Any]										=  x.astype(np.floataa  )

        return x

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_ = None  ):

        """simple docstring"""

        A_  :       Any										=  attention_mask.sum(-1  ) if attention_mask is not None else [x.shape[0] for x in input_features]
        return [self._normalize_one(snake_case_					,      snake_case_					,      self.padding_value  ) for x, n in zip(snake_case_					,      snake_case_  )]

      def __call__(     self					,      snake_case_					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        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 `raw_speech` 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.'  )

        A_  :       Optional[int]										=  isinstance(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}"""  )
        A_  :       List[Any]										=  is_batched_numpy or (
            isinstance(snake_case_					,      (list, tuple)  ) and (isinstance(raw_speech[0]					,      (np.ndarray, tuple, list)  ))
        )

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

        # always return batch
        if not is_batched:
          A_  :       Dict										=  [raw_speech]

        # extract fbank features
        A_  :       int										=  [self._extract_mfsc_features(snake_case_  ) for one_waveform in raw_speech]

        # convert into correct format for padding
        A_  :       Any										=  BatchFeature({'input_features': features}  )

        A_  :       Tuple										=  self.pad(
            snake_case_					,      padding=snake_case_					,      max_length=snake_case_					,      truncation=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_attention_mask=snake_case_					,      **snake_case_					,      )
        # make sure list is in array format
        A_  :       Optional[Any]										=  padded_inputs.get('input_features'  )
        if isinstance(input_features[0]					,      snake_case_  ):
          A_  :       List[str]										=  [np.asarray(snake_case_					,      dtype=np.floataa  ) for feature in input_features]

        A_  :       List[Any]										=  padded_inputs.get('attention_mask'  )
        if attention_mask is not None:
          A_  :       Any										=  [np.asarray(snake_case_					,      dtype=np.intaa  ) for array in attention_mask]

        if self.normalize_means or self.normalize_vars:
          A_  :       str										=  (
              np.array(snake_case_					,      dtype=np.intaa  )
              if self._get_padding_strategies(snake_case_					,      max_length=snake_case_  ) is not PaddingStrategy.DO_NOT_PAD
              and padding
              else None
          )
          A_  :       Any										=  self.normalize(
              padded_inputs['input_features']					,      attention_mask=snake_case_  )

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

        return padded_inputs






"""simple docstring"""




from arguments import InitializationArguments

from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser


# Configuration
lowerCamelCase_     :						Any						=					HfArgumentParser(InitializationArguments)
lowerCamelCase_     :						Union[str, Any]						=					parser.parse_args()

# Load codeparrot tokenizer trained for Python code tokenization
lowerCamelCase_     :						List[Any]						=					AutoTokenizer.from_pretrained(args.tokenizer_name)

# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
lowerCamelCase_     :						Tuple						=					{
    'vocab_size': len(tokenizer),
    'scale_attn_by_inverse_layer_idx': True,
    'reorder_and_upcast_attn': True,
}

# Load model config (GPT-2 large in this case)
lowerCamelCase_     :						int						=					AutoConfig.from_pretrained(args.config_name, **config_kwargs)

# Initialize new model with config
lowerCamelCase_     :						Any						=					AutoModelForCausalLM.from_config(config)

# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)