




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




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						["""input_features""", """is_longer"""]

      def __init__(				self    ,				A=6_4    ,				A=4_8_0_0_0    ,				A=4_8_0    ,				A=1_0    ,				A=1_0_2_4    ,				A=0.0    ,				A=False    ,				A = 0    ,				A = 1_4_0_0_0    ,				A = None    ,				A = "fusion"    ,				A = "repeatpad"    ,				**A    ,				)		->    Any:
         super().__init__(
             feature_size=A    ,				sampling_rate=A    ,				padding_value=A    ,				return_attention_mask=A    ,				**A    ,				)
         snake_case				:    Optional[int]           =	top_db
         snake_case				:    List[str]           =	truncation
         snake_case				:    int           =	padding
         snake_case				:    int           =	fft_window_size
         snake_case				:    Union[str, Any]           =	(fft_window_size >> 1) + 1
         snake_case				:    List[Any]           =	hop_length
         snake_case				:    Dict           =	max_length_s
         snake_case				:    Tuple           =	max_length_s * sampling_rate
         snake_case				:    Dict           =	sampling_rate
         snake_case				:    Optional[int]           =	frequency_min
         snake_case				:    Tuple           =	frequency_max
         snake_case				:    Union[str, 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=A    ,				mel_scale="""htk"""    ,				)
         snake_case				:    Optional[int]           =	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      UpperCAmelCase				(				self     )		->    Dict[str, Any]:
         snake_case				:    Optional[Any]           =	copy.deepcopy(self.__dict__     )
         snake_case				:    Dict           =	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      UpperCAmelCase				(				self    ,				A    ,				A = None     )		->    np.ndarray:
         snake_case				:    List[str]           =	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      UpperCAmelCase				(				self    ,				A    ,				A    ,				A     )		->    Dict:
         snake_case				:    Optional[int]           =	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
            snake_case				:    List[str]           =	[0]
         if len(ranges[2]     ) == 0:
            # if the audio is too short, we just use the first chunk
            snake_case				:    List[Any]           =	[0]
         # randomly choose index for each part
         snake_case				:    Optional[int]           =	np.random.choice(ranges[0]     )
         snake_case				:    Dict           =	np.random.choice(ranges[1]     )
         snake_case				:    List[str]           =	np.random.choice(ranges[2]     )

         snake_case				:    Dict           =	mel[idx_front : idx_front + chunk_frames, :]
         snake_case				:    Optional[Any]           =	mel[idx_middle : idx_middle + chunk_frames, :]
         snake_case				:    List[Any]           =	mel[idx_back : idx_back + chunk_frames, :]

         snake_case				:    int           =	torch.tensor(mel[None, None, :]     )
         snake_case				:    Any           =	torch.nn.functional.interpolate(
             A    ,				size=[chunk_frames, 6_4]    ,				mode="""bilinear"""    ,				align_corners=A     )
         snake_case				:    Dict           =	mel_shrink[0][0].numpy()
         snake_case				:    Dict           =	np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,				axis=0     )
         return mel_fusion

      def      UpperCAmelCase				(				self    ,				A    ,				A    ,				A    ,				A     )		->    np.array:
         if waveform.shape[0] > max_length:
            if truncation == "rand_trunc":
               snake_case				:    Optional[int]           =	True
               # random crop to max_length (for compatibility) -> this should be handled by self.pad
               snake_case				:    str           =	len(A     ) - max_length
               snake_case				:    str           =	np.random.randint(0    ,				overflow + 1     )
               snake_case				:    Union[str, Any]           =	waveform[idx : idx + max_length]
               snake_case				:    Union[str, Any]           =	self._np_extract_fbank_features(A    ,				self.mel_filters_slaney     )[None, :]
            elif truncation == "fusion":
               snake_case				:    Dict           =	self._np_extract_fbank_features(A    ,				self.mel_filters     )
               snake_case				:    List[Any]           =	max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
               snake_case				:    List[Any]           =	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.
                  snake_case				:    Optional[int]           =	np.stack([mel, mel, mel, mel]    ,				axis=0     )
                  snake_case				:    List[str]           =	False
               else:
                  snake_case				:    str           =	self._random_mel_fusion(A    ,				A    ,				A     )
                  snake_case				:    Optional[Any]           =	True
            else:
               raise NotImplementedError(f"""data_truncating {truncation} not implemented"""     )

         else:
            snake_case				:    int           =	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":
                  snake_case				:    Optional[Any]           =	int(max_length / len(A     )     )
                  snake_case				:    List[str]           =	np.stack(np.tile(A    ,				n_repeat + 1     )     )[:max_length]
               if padding == "repeatpad":
                  snake_case				:    str           =	int(max_length / len(A     )     )
                  snake_case				:    List[str]           =	np.stack(np.tile(A    ,				A     )     )
               snake_case				:    List[Any]           =	np.pad(A    ,				(0, max_length - waveform.shape[0])    ,				mode="""constant"""    ,				constant_values=0     )

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

         return input_mel, longer

      def __call__(				self    ,				A    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				A = None    ,				**A    ,				)		->    BatchFeature:
         snake_case				:    Optional[int]           =	truncation if truncation is not None else self.truncation
         snake_case				:    Optional[int]           =	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."""     )

         snake_case				:    Tuple           =	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}"""     )
         snake_case				:    Union[str, Any]           =	is_batched_numpy or (
             isinstance(A    ,				(list, tuple)     ) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)     ))
         )

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

         # always return batch
         if not is_batched:
            snake_case				:    str           =	[np.asarray(A     )]

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

         snake_case				:    str           =	[]
         snake_case				:    Dict           =	[]
         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
            snake_case				:    str           =	np.random.randint(0    ,				len(A     )     )
            snake_case				:    str           =	True

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

         # is_longer is a list of bool
         snake_case				:    List[Any]           =	[[longer] for longer in is_longer]

         snake_case				:    Optional[int]           =	{"""input_features""": input_mel, """is_longer""": is_longer}
         snake_case				:    Optional[int]           =	BatchFeature(A     )

         if return_tensors is not None:
            snake_case				:    Any           =	input_features.convert_to_tensors(A     )

         return input_features











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase	:	Optional[Any]				=   {
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Tuple				=   ['RobertaTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	List[str]				=   [
           'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'RobertaForCausalLM',
           'RobertaForMaskedLM',
           'RobertaForMultipleChoice',
           'RobertaForQuestionAnswering',
           'RobertaForSequenceClassification',
           'RobertaForTokenClassification',
           'RobertaModel',
           'RobertaPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'TFRobertaForCausalLM',
           'TFRobertaForMaskedLM',
           'TFRobertaForMultipleChoice',
           'TFRobertaForQuestionAnswering',
           'TFRobertaForSequenceClassification',
           'TFRobertaForTokenClassification',
           'TFRobertaMainLayer',
           'TFRobertaModel',
           'TFRobertaPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Any				=   [
           'FlaxRobertaForCausalLM',
           'FlaxRobertaForMaskedLM',
           'FlaxRobertaForMultipleChoice',
           'FlaxRobertaForQuestionAnswering',
           'FlaxRobertaForSequenceClassification',
           'FlaxRobertaForTokenClassification',
           'FlaxRobertaModel',
           'FlaxRobertaPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
       from .tokenization_roberta import RobertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_roberta_fast import RobertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_roberta import (
                  ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  RobertaForCausalLM,
                  RobertaForMaskedLM,
                  RobertaForMultipleChoice,
                  RobertaForQuestionAnswering,
                  RobertaForSequenceClassification,
                  RobertaForTokenClassification,
                  RobertaModel,
                  RobertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_roberta import (
                  TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFRobertaForCausalLM,
                  TFRobertaForMaskedLM,
                  TFRobertaForMultipleChoice,
                  TFRobertaForQuestionAnswering,
                  TFRobertaForSequenceClassification,
                  TFRobertaForTokenClassification,
                  TFRobertaMainLayer,
                  TFRobertaModel,
                  TFRobertaPreTrainedModel,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_roberta import (
                  FlaxRobertaForCausalLM,
                  FlaxRobertaForMaskedLM,
                  FlaxRobertaForMultipleChoice,
                  FlaxRobertaForQuestionAnswering,
                  FlaxRobertaForSequenceClassification,
                  FlaxRobertaForTokenClassification,
                  FlaxRobertaModel,
                  FlaxRobertaPreTrainedModel,
              )

else:
       import sys

       lowerCamelCase	:	str				=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





