
"""simple docstring"""


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

import numpy as np
import torch

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


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					List[str]													=['''input_features''', '''is_longer''']

     def __init__(	self:						Optional[Any]     ,		__a:						str=64     ,		__a:						List[str]=48_000     ,		__a:						List[Any]=480     ,		__a:						Dict=10     ,		__a:						List[Any]=1_024     ,		__a:						str=0.0     ,		__a:						int=False     ,		__a:						float = 0     ,		__a:						float = 14_000     ,		__a:						int = None     ,		__a:						str = "fusion"     ,		__a:						str = "repeatpad"     ,		**__a:						Union[str, Any]     ,		)->    Dict:
           super().__init__(
               feature_size=__a     ,		sampling_rate=__a     ,		padding_value=__a     ,		return_attention_mask=__a     ,		**__a     ,		)
           lowerCamelCase					:					List[Any]        =	top_db
           lowerCamelCase					:					Optional[int]        =	truncation
           lowerCamelCase					:					Any        =	padding
           lowerCamelCase					:					Dict        =	fft_window_size
           lowerCamelCase					:					Tuple        =	(fft_window_size >> 1) + 1
           lowerCamelCase					:					Dict        =	hop_length
           lowerCamelCase					:					Tuple        =	max_length_s
           lowerCamelCase					:					Any        =	max_length_s * sampling_rate
           lowerCamelCase					:					Optional[int]        =	sampling_rate
           lowerCamelCase					:					str        =	frequency_min
           lowerCamelCase					:					Optional[int]        =	frequency_max
           lowerCamelCase					:					str        =	mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins     ,		num_mel_filters=__a     ,		min_frequency=__a     ,		max_frequency=__a     ,		sampling_rate=__a     ,		norm=__a     ,		mel_scale="""htk"""     ,		)
           lowerCamelCase					:					Optional[Any]        =	mel_filter_bank(
               num_frequency_bins=self.nb_frequency_bins     ,		num_mel_filters=__a     ,		min_frequency=__a     ,		max_frequency=__a     ,		sampling_rate=__a     ,		norm="""slaney"""     ,		mel_scale="""slaney"""     ,		)

     def 		a__						(	self:						Any	)->    Dict[str, Any]:
           lowerCamelCase					:					Union[str, Any]        =	copy.deepcopy(self.__dict__	)
           lowerCamelCase					:					Optional[Any]        =	self.__class__.__name__
           if "mel_filters" in output:
                 del output["mel_filters"]
           if "mel_filters_slaney" in output:
                 del output["mel_filters_slaney"]
           return output

     def 		a__						(	self:						Any     ,		__a:						np.array     ,		__a:						Optional[np.array] = None	)->    np.ndarray:
           lowerCamelCase					:					Optional[Any]        =	spectrogram(
               __a     ,		window_function(self.fft_window_size     ,		"""hann"""	)     ,		frame_length=self.fft_window_size     ,		hop_length=self.hop_length     ,		power=2.0     ,		mel_filters=__a     ,		log_mel="""dB"""     ,		)
           return log_mel_spectrogram.T

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Union[str, Any]     ,		__a:						Optional[Any]     ,		__a:						Any	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	np.array_split(list(range(0     ,		total_frames - chunk_frames + 1	)	)     ,		3	)
           if len(ranges[1]	) == 0:
                 # if the audio is too short, we just use the first chunk
                 lowerCamelCase					:					Any        =	[0]
           if len(ranges[2]	) == 0:
                 # if the audio is too short, we just use the first chunk
                 lowerCamelCase					:					List[Any]        =	[0]
           # randomly choose index for each part
           lowerCamelCase					:					int        =	np.random.choice(ranges[0]	)
           lowerCamelCase					:					List[str]        =	np.random.choice(ranges[1]	)
           lowerCamelCase					:					str        =	np.random.choice(ranges[2]	)

           lowerCamelCase					:					Tuple        =	mel[idx_front : idx_front + chunk_frames, :]
           lowerCamelCase					:					str        =	mel[idx_middle : idx_middle + chunk_frames, :]
           lowerCamelCase					:					str        =	mel[idx_back : idx_back + chunk_frames, :]

           lowerCamelCase					:					Union[str, Any]        =	torch.tensor(mel[None, None, :]	)
           lowerCamelCase					:					Dict        =	torch.nn.functional.interpolate(
               __a     ,		size=[chunk_frames, 64]     ,		mode="""bilinear"""     ,		align_corners=__a	)
           lowerCamelCase					:					str        =	mel_shrink[0][0].numpy()
           lowerCamelCase					:					Optional[int]        =	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]     ,		axis=0	)
           return mel_fusion

     def 		a__						(	self:						Union[str, Any]     ,		__a:						np.array     ,		__a:						List[Any]     ,		__a:						str     ,		__a:						List[Any]	)->    np.array:
           if waveform.shape[0] > max_length:
                 if truncation == "rand_trunc":
                       lowerCamelCase					:					int        =	True
                       # random crop to max_length (for compatibility) -> this should be handled by self.pad
                       lowerCamelCase					:					str        =	len(__a	) - max_length
                       lowerCamelCase					:					Optional[Any]        =	np.random.randint(0     ,		overflow + 1	)
                       lowerCamelCase					:					Union[str, Any]        =	waveform[idx : idx + max_length]
                       lowerCamelCase					:					List[Any]        =	self._np_extract_fbank_features(__a     ,		self.mel_filters_slaney	)[None, :]
                 elif truncation == "fusion":
                       lowerCamelCase					:					Optional[Any]        =	self._np_extract_fbank_features(__a     ,		self.mel_filters	)
                       lowerCamelCase					:					Tuple        =	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
                       lowerCamelCase					:					str        =	mel.shape[0]
                       if chunk_frames == total_frames:
                             # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
                             # In this case, we just use the whole audio.
                             lowerCamelCase					:					Any        =	np.stack([mel, mel, mel, mel]     ,		axis=0	)
                             lowerCamelCase					:					List[str]        =	False
                       else:
                             lowerCamelCase					:					Any        =	self._random_mel_fusion(__a     ,		__a     ,		__a	)
                             lowerCamelCase					:					Optional[Any]        =	True
                 else:
                       raise NotImplementedError(f'data_truncating {truncation} not implemented'	)

           else:
                 lowerCamelCase					:					Dict        =	False
                 # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
                 if waveform.shape[0] < max_length:
                       if padding == "repeat":
                             lowerCamelCase					:					Tuple        =	int(max_length / len(__a	)	)
                             lowerCamelCase					:					Dict        =	np.stack(np.tile(__a     ,		n_repeat + 1	)	)[:max_length]
                       if padding == "repeatpad":
                             lowerCamelCase					:					Optional[Any]        =	int(max_length / len(__a	)	)
                             lowerCamelCase					:					Optional[Any]        =	np.stack(np.tile(__a     ,		__a	)	)
                       lowerCamelCase					:					Optional[Any]        =	np.pad(__a     ,		(0, max_length - waveform.shape[0])     ,		mode="""constant"""     ,		constant_values=0	)

                 if truncation == "fusion":
                       lowerCamelCase					:					str        =	self._np_extract_fbank_features(__a     ,		self.mel_filters	)
                       lowerCamelCase					:					Optional[Any]        =	np.stack([input_mel, input_mel, input_mel, input_mel]     ,		axis=0	)
                 else:
                       lowerCamelCase					:					Dict        =	self._np_extract_fbank_features(__a     ,		self.mel_filters_slaney	)[None, :]

           return input_mel, longer







     def __call__(	self:						List[Any]     ,		__a:						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]     ,		__a:						str = None     ,		__a:						Optional[str] = None     ,		__a:						Optional[int] = None     ,		__a:						Optional[int] = None     ,		__a:						Optional[Union[str, TensorType]] = None     ,		**__a:						str     ,		)->    BatchFeature:
           lowerCamelCase					:					Optional[Any]        =	truncation if truncation is not None else self.truncation
           lowerCamelCase					:					str        =	padding if padding else self.padding

           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."""	)

           lowerCamelCase					:					int        =	isinstance(__a     ,		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}'	)
           lowerCamelCase					:					str        =	is_batched_numpy or (
               isinstance(__a     ,		(list, tuple)	) and (isinstance(raw_speech[0]     ,		(np.ndarray, tuple, list)	))
           )

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

           # always return batch
           if not is_batched:
                 lowerCamelCase					:					List[Any]        =	[np.asarray(__a	)]

           # convert to mel spectrogram, truncate and pad if needed.
           lowerCamelCase					:					Optional[Any]        =	[
               self._get_input_mel(__a     ,		max_length if max_length else self.nb_max_samples     ,		__a     ,		__a	)
               for waveform in raw_speech
           ]

           lowerCamelCase					:					Optional[int]        =	[]
           lowerCamelCase					:					Union[str, Any]        =	[]
           for mel, longer in padded_inputs:
                 input_mel.append(__a	)
                 is_longer.append(__a	)

           if truncation == "fusion" and sum(__a	) == 0:
                 # if no audio is longer than 10s, then randomly select one audio to be longer
                 lowerCamelCase					:					Optional[Any]        =	np.random.randint(0     ,		len(__a	)	)
                 lowerCamelCase					:					Union[str, Any]        =	True

           if isinstance(input_mel[0]     ,		__a	):
                 lowerCamelCase					:					List[str]        =	[np.asarray(__a     ,		dtype=np.floataa	) for feature in input_mel]

           # is_longer is a list of bool
           lowerCamelCase					:					Tuple        =	[[longer] for longer in is_longer]

           lowerCamelCase					:					Union[str, Any]        =	{"""input_features""": input_mel, """is_longer""": is_longer}
           lowerCamelCase					:					Any        =	BatchFeature(__a	)

           if return_tensors is not None:
                 lowerCamelCase					:					str        =	input_features.convert_to_tensors(__a	)

           return input_features








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__lowerCamelCase      :str	    =		{}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		['GPTSw3Tokenizer']


if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_swa import GPTSwaTokenizer

else:
    import sys

    __lowerCamelCase      :Tuple	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




