



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


SCREAMING_SNAKE_CASE_:List[Any]             =						logging.get_logger(__name__)






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      ["input_values", "attention_mask"]






    def __init__(       self,		lowerCamelCase__ = 1,		lowerCamelCase__ = 1_6000,		lowerCamelCase__ = 0.0,		lowerCamelCase__ = False,		lowerCamelCase__ = 80,		lowerCamelCase__ = 16,		lowerCamelCase__ = 64,		lowerCamelCase__ = "hann_window",		lowerCamelCase__ = 1.0,		lowerCamelCase__ = 80,		lowerCamelCase__ = 7600,		lowerCamelCase__ = 1e-10,		lowerCamelCase__ = 2,		lowerCamelCase__ = True,		**lowerCamelCase__,		):
        super().__init__(feature_size=lowerCamelCase__,		sampling_rate=lowerCamelCase__,		padding_value=lowerCamelCase__,		**lowerCamelCase__		)
        A :       Tuple          =   do_normalize
        A :       Dict          =   return_attention_mask

        A :       str          =   num_mel_bins
        A :       Optional[int]          =   hop_length
        A :       List[str]          =   win_length
        A :       List[str]          =   win_function
        A :       str          =   frame_signal_scale
        A :       Dict          =   fmin
        A :       Any          =   fmax
        A :       List[Any]          =   mel_floor
        A :       Optional[Any]          =   reduction_factor

        A :       List[str]          =   win_length * sampling_rate // 1000
        A :       List[str]          =   hop_length * sampling_rate // 1000
        A :       Dict          =   optimal_fft_length(self.sample_size		)
        A :       Optional[int]          =   (self.n_fft // 2) + 1

        A :       int          =   window_function(window_length=self.sample_size,		name=self.win_function,		periodic=lowerCamelCase__		)

        A :       Union[str, Any]          =   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""",		lowerCamelCase__,		)
        if reduction_factor != 2.0:
            warnings.warn(
                """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers""",		lowerCamelCase__,		)






    @staticmethod
    # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
    def 			_lowerCAmelCase					(       lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 0.0		):
        if attention_mask is not None:
            A :       int          =   np.array(lowerCamelCase__,		np.intaa		)
            A :       Any          =   []

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

                normed_input_values.append(lowerCamelCase__		)
        else:
            A :       Optional[int]          =   [(x - x.mean()) / np.sqrt(x.var() + 1e-7		) for x in input_values]

        return normed_input_values






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		):
        A :       List[Any]          =   spectrogram(
            lowerCamelCase__,		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,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        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:
            A :       Dict          =   self._process_audio(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__,		)
        else:
            A :       List[Any]          =   None

        if audio_target is not None:
            A :       str          =   self._process_audio(
                lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__,		)

            if inputs is None:
                return inputs_target
            else:
                A :       Optional[int]          =   inputs_target["""input_values"""]
                A :       Dict          =   inputs_target.get("""attention_mask"""		)
                if decoder_attention_mask is not None:
                    A :       int          =   decoder_attention_mask

        return inputs






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__ = False,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = False,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		lowerCamelCase__ = None,		**lowerCamelCase__,		):
        A :       Tuple          =   isinstance(lowerCamelCase__,		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}'''		)
        A :       Any          =   is_batched_numpy or (
            isinstance(lowerCamelCase__,		(list, tuple)		) and (isinstance(speech[0],		(np.ndarray, tuple, list)		))
        )

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

        # always return batch
        if not is_batched:
            A :       List[Any]          =   [speech]

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

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

        A :       List[Any]          =   self.pad(
            lowerCamelCase__,		padding=lowerCamelCase__,		max_length=lowerCamelCase__,		truncation=lowerCamelCase__,		pad_to_multiple_of=lowerCamelCase__,		return_attention_mask=lowerCamelCase__,		**lowerCamelCase__,		)

        A :       Tuple          =   feature_size_hack

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

        # convert attention_mask to correct format
        A :       int          =   padded_inputs.get("""attention_mask"""		)
        if attention_mask is not None:
            A :       int          =   [np.asarray(lowerCamelCase__,		dtype=np.intaa		) for array in attention_mask]

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

        if return_tensors is not None:
            A :       Tuple          =   padded_inputs.convert_to_tensors(lowerCamelCase__		)

        return padded_inputs






    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   super().to_dict()

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

        return output







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


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

SCREAMING_SNAKE_CASE_:Dict             =						{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      "camembert"






    def __init__(       self,		lowerCamelCase__=3_0522,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=512,		lowerCamelCase__=2,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=2,		lowerCamelCase__="absolute",		lowerCamelCase__=True,		lowerCamelCase__=None,		**lowerCamelCase__,		):
        super().__init__(pad_token_id=lowerCamelCase__,		bos_token_id=lowerCamelCase__,		eos_token_id=lowerCamelCase__,		**lowerCamelCase__		)

        A :       List[Any]          =   vocab_size
        A :       Dict          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       List[Any]          =   num_attention_heads
        A :       List[str]          =   hidden_act
        A :       Tuple          =   intermediate_size
        A :       Tuple          =   hidden_dropout_prob
        A :       List[Any]          =   attention_probs_dropout_prob
        A :       Optional[int]          =   max_position_embeddings
        A :       Tuple          =   type_vocab_size
        A :       List[Any]          =   initializer_range
        A :       str          =   layer_norm_eps
        A :       Tuple          =   position_embedding_type
        A :       str          =   use_cache
        A :       Any          =   classifier_dropout






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @property
    def 			_lowerCAmelCase					(       self		):
        if self.task == "multiple-choice":
            A :       int          =   {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            A :       Tuple          =   {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]		)


