"""simple docstring"""






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

import numpy as np

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


_A        =     logging.get_logger(__name__)

class lowerCamelCase		(_SCREAMING_SNAKE_CASE   ):






					'''simple docstring'''


					a 						=		["input_features"]



					def __init__(    self      :					Union[str, Any] ,			_snake_case      :					str=80 ,			_snake_case      :					Any=16000 ,			_snake_case      :					int=160 ,			_snake_case      :					Optional[Any]=30 ,			_snake_case      :					List[str]=400 ,			_snake_case      :					Any=0.0 ,			_snake_case      :					Any=False ,			**_snake_case      :					int ,			)				->							str:
									super().__init__(
									    feature_size=_snake_case ,			sampling_rate=_snake_case ,			padding_value=_snake_case ,			return_attention_mask=_snake_case ,			**_snake_case ,			)
									SCREAMING_SNAKE_CASE__			=					n_fft
									SCREAMING_SNAKE_CASE__			=					hop_length
									SCREAMING_SNAKE_CASE__			=					chunk_length
									SCREAMING_SNAKE_CASE__			=					chunk_length * sampling_rate
									SCREAMING_SNAKE_CASE__			=					self.n_samples // hop_length
									SCREAMING_SNAKE_CASE__			=					sampling_rate
									SCREAMING_SNAKE_CASE__			=					mel_filter_bank(
									    num_frequency_bins=1 + n_fft // 2 ,			num_mel_filters=_snake_case ,			min_frequency=0.0 ,			max_frequency=8000.0 ,			sampling_rate=_snake_case ,			norm="slaney" ,			mel_scale="slaney" ,			)



					def       lowerCAmelCase_       (    self      :					List[Any] ,			_snake_case      :					np.array		)				->							np.ndarray:
									SCREAMING_SNAKE_CASE__			=					spectrogram(
									    _snake_case ,			window_function(self.n_fft ,			"hann"		) ,			frame_length=self.n_fft ,			hop_length=self.hop_length ,			power=2.0 ,			mel_filters=self.mel_filters ,			log_mel="log10" ,			)
									SCREAMING_SNAKE_CASE__			=					log_spec[:, :-1]
									SCREAMING_SNAKE_CASE__			=					np.maximum(_snake_case ,			log_spec.max() - 8.0		)
									SCREAMING_SNAKE_CASE__			=					(log_spec + 4.0) / 4.0
									return log_spec



					@staticmethod
					# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
					def       lowerCAmelCase_       (    _snake_case      :					List[np.ndarray] ,			_snake_case      :					List[np.ndarray] ,			_snake_case      :					float = 0.0		)				->							List[np.ndarray]:
									if attention_mask is not None:
													SCREAMING_SNAKE_CASE__			=					np.array(_snake_case ,			np.intaa		)
													SCREAMING_SNAKE_CASE__			=					[]

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

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

									return normed_input_values



					def __call__(    self      :					Optional[Any] ,			_snake_case      :					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,			_snake_case      :					bool = True ,			_snake_case      :					Optional[int] = None ,			_snake_case      :					Optional[Union[str, TensorType]] = None ,			_snake_case      :					Optional[bool] = None ,			_snake_case      :					Optional[str] = "max_length" ,			_snake_case      :					Optional[int] = None ,			_snake_case      :					Optional[int] = None ,			_snake_case      :					Optional[bool] = None ,			**_snake_case      :					Union[str, Any] ,			)				->							BatchFeature:

									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."		)

									SCREAMING_SNAKE_CASE__			=					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}"""		)
									SCREAMING_SNAKE_CASE__			=					is_batched_numpy or (
									    isinstance(_snake_case ,			(list, tuple)		) and (isinstance(raw_speech[0] ,			(np.ndarray, tuple, list)		))
									)

									if is_batched:
													SCREAMING_SNAKE_CASE__			=					[np.asarray([speech] ,			dtype=np.floataa		).T for speech in raw_speech]
									elif not is_batched and not isinstance(_snake_case ,			np.ndarray		):
													SCREAMING_SNAKE_CASE__			=					np.asarray(_snake_case ,			dtype=np.floataa		)
									elif isinstance(_snake_case ,			np.ndarray		) and raw_speech.dtype is np.dtype(np.floataa		):
													SCREAMING_SNAKE_CASE__			=					raw_speech.astype(np.floataa		)

									# always return batch
									if not is_batched:
													SCREAMING_SNAKE_CASE__			=					[np.asarray([raw_speech]		).T]

									SCREAMING_SNAKE_CASE__			=					BatchFeature({"input_features": raw_speech}		)

									# convert into correct format for padding

									SCREAMING_SNAKE_CASE__			=					self.pad(
									    _snake_case ,			padding=_snake_case ,			max_length=max_length if max_length else self.n_samples ,			truncation=_snake_case ,			pad_to_multiple_of=_snake_case ,			return_attention_mask=return_attention_mask or do_normalize ,			)

									# zero-mean and unit-variance normalization
									if do_normalize:
													SCREAMING_SNAKE_CASE__			=					self.zero_mean_unit_var_norm(
													    padded_inputs["input_features"] ,			attention_mask=padded_inputs["attention_mask"] ,			padding_value=self.padding_value ,			)
													SCREAMING_SNAKE_CASE__			=					np.stack(padded_inputs["input_features"] ,			axis=0		)

									# make sure list is in array format
									SCREAMING_SNAKE_CASE__			=					padded_inputs.get("input_features"		).transpose(2 ,			0 ,			1		)

									SCREAMING_SNAKE_CASE__			=					[self._np_extract_fbank_features(_snake_case		) for waveform in input_features[0]]

									if isinstance(input_features[0] ,			_snake_case		):
													SCREAMING_SNAKE_CASE__			=					[np.asarray(_snake_case ,			dtype=np.floataa		) for feature in input_features]
									else:
													SCREAMING_SNAKE_CASE__			=					input_features

									if return_attention_mask:
													# rescale from sample (48000) to feature (3000)
													SCREAMING_SNAKE_CASE__			=					padded_inputs["attention_mask"][:, :: self.hop_length]

									if return_tensors is not None:
													SCREAMING_SNAKE_CASE__			=					padded_inputs.convert_to_tensors(_snake_case		)

									return padded_inputs



					def       lowerCAmelCase_       (    self      :					Any		)				->							Dict[str, Any]:
									SCREAMING_SNAKE_CASE__			=					copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=					self.__class__.__name__
									if "mel_filters" in output:
													del output["mel_filters"]
									return output












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a:     int   					=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Dict   					=					['''MvpTokenizerFast''']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a:     Tuple   					=					[
      '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MvpForCausalLM''',
      '''MvpForConditionalGeneration''',
      '''MvpForQuestionAnswering''',
      '''MvpForSequenceClassification''',
      '''MvpModel''',
      '''MvpPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
  from .tokenization_mvp import MvpTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_mvp_fast import MvpTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mvp import (
        MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
        MvpForCausalLM,
        MvpForConditionalGeneration,
        MvpForQuestionAnswering,
        MvpForSequenceClassification,
        MvpModel,
        MvpPreTrainedModel,
    )

else:
  import sys

  __a:     Optional[Any]   					=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)