




"""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


lowercase__  		=		logging.get_logger(__name__)






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    ["""input_features"""]
			def __init__(						self						,   lowercase=80						,   lowercase=16000						,   lowercase=160						,   lowercase=30						,   lowercase=400						,   lowercase=0.0						,   lowercase=False						,   **lowercase						,   ):
							super().__init__(
							    feature_size=lowercase						,   sampling_rate=lowercase						,   padding_value=lowercase						,   return_attention_mask=lowercase						,   **lowercase						,   )
							_lowerCamelCase  :    List[Any]					=					n_fft
							_lowerCamelCase  :    Optional[Any]					=					hop_length
							_lowerCamelCase  :    List[str]					=					chunk_length
							_lowerCamelCase  :    Optional[Any]					=					chunk_length * sampling_rate
							_lowerCamelCase  :    Any					=					self.n_samples // hop_length
							_lowerCamelCase  :    Union[str, Any]					=					sampling_rate
							_lowerCamelCase  :    Union[str, Any]					=					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2						,   num_mel_filters=lowercase						,   min_frequency=0.0						,   max_frequency=8000.0						,   sampling_rate=lowercase						,   norm='slaney'						,   mel_scale='slaney'						,   )
			def       A_					(						self						,   lowercase						):
							_lowerCamelCase  :    Union[str, Any]					=					spectrogram(
							    lowercase						,   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'						,   )
							_lowerCamelCase  :    Optional[Any]					=					log_spec[:, :-1]
							_lowerCamelCase  :    List[Any]					=					np.maximum(lowercase						,   log_spec.max() - 8.0						)
							_lowerCamelCase  :    List[Any]					=					(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       A_					(						lowercase						,   lowercase						,   lowercase = 0.0						):
							if attention_mask is not None:
											_lowerCamelCase  :    Union[str, Any]					=					np.array(lowercase						,   np.intaa						)
											_lowerCamelCase  :    Tuple					=					[]

											for vector, length in zip(lowercase						,   attention_mask.sum(-1						)						):
															_lowerCamelCase  :    Dict					=					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7						)
															if length < normed_slice.shape[0]:
																			_lowerCamelCase  :    List[str]					=					padding_value

															normed_input_values.append(lowercase						)
							else:
											_lowerCamelCase  :    Dict					=					[(x - x.mean()) / np.sqrt(x.var() + 1E-7						) for x in input_values]

							return normed_input_values
			def __call__(						self						,   lowercase						,   lowercase = True						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   lowercase = "max_length"						,   lowercase = None						,   lowercase = None						,   lowercase = None						,   **lowercase						,   ):

							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  :    List[Any]					=					isinstance(lowercase						,   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  :    List[Any]					=					is_batched_numpy or (
							    isinstance(lowercase						,   (list, tuple)						) and (isinstance(raw_speech[0]						,   (np.ndarray, tuple, list)						))
							)

							if is_batched:
											_lowerCamelCase  :    Any					=					[np.asarray([speech]						,   dtype=np.floataa						).T for speech in raw_speech]
							elif not is_batched and not isinstance(lowercase						,   np.ndarray						):
											_lowerCamelCase  :    Dict					=					np.asarray(lowercase						,   dtype=np.floataa						)
							elif isinstance(lowercase						,   np.ndarray						) and raw_speech.dtype is np.dtype(np.floataa						):
											_lowerCamelCase  :    int					=					raw_speech.astype(np.floataa						)

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

							_lowerCamelCase  :    Optional[Any]					=					BatchFeature({'input_features': raw_speech}						)

							# convert into correct format for padding

							_lowerCamelCase  :    Optional[int]					=					self.pad(
							    lowercase						,   padding=lowercase						,   max_length=max_length if max_length else self.n_samples						,   truncation=lowercase						,   pad_to_multiple_of=lowercase						,   return_attention_mask=return_attention_mask or do_normalize						,   )

							# zero-mean and unit-variance normalization
							if do_normalize:
											_lowerCamelCase  :    Optional[int]					=					self.zero_mean_unit_var_norm(
											    padded_inputs['input_features']						,   attention_mask=padded_inputs['attention_mask']						,   padding_value=self.padding_value						,   )
											_lowerCamelCase  :    List[str]					=					np.stack(padded_inputs['input_features']						,   axis=0						)

							# make sure list is in array format
							_lowerCamelCase  :    Optional[Any]					=					padded_inputs.get('input_features'						).transpose(2						,   0						,   1						)

							_lowerCamelCase  :    Optional[int]					=					[self._np_extract_fbank_features(lowercase						) for waveform in input_features[0]]

							if isinstance(input_features[0]						,   lowercase						):
											_lowerCamelCase  :    Tuple					=					[np.asarray(lowercase						,   dtype=np.floataa						) for feature in input_features]
							else:
											_lowerCamelCase  :    str					=					input_features

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

							if return_tensors is not None:
											_lowerCamelCase  :    Tuple					=					padded_inputs.convert_to_tensors(lowercase						)

							return padded_inputs






			def       A_					(						self						):
							_lowerCamelCase  :    Optional[Any]					=					copy.deepcopy(self.__dict__						)
							_lowerCamelCase  :    Optional[Any]					=					self.__class__.__name__
							if "mel_filters" in output:
											del output["mel_filters"]
							return output





"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """facebook/deit-base-distilled-patch16-224""": (
        """https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"""
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """deit"""
			def __init__(						self						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase="gelu"						,   lowercase=0.0						,   lowercase=0.0						,   lowercase=0.02						,   lowercase=1E-12						,   lowercase=224						,   lowercase=16						,   lowercase=3						,   lowercase=True						,   lowercase=16						,   **lowercase						,   ):
							super().__init__(**lowercase						)

							_lowerCamelCase  :    Optional[Any]					=					hidden_size
							_lowerCamelCase  :    List[str]					=					num_hidden_layers
							_lowerCamelCase  :    Tuple					=					num_attention_heads
							_lowerCamelCase  :    Any					=					intermediate_size
							_lowerCamelCase  :    int					=					hidden_act
							_lowerCamelCase  :    Any					=					hidden_dropout_prob
							_lowerCamelCase  :    str					=					attention_probs_dropout_prob
							_lowerCamelCase  :    str					=					initializer_range
							_lowerCamelCase  :    Union[str, Any]					=					layer_norm_eps
							_lowerCamelCase  :    Optional[Any]					=					image_size
							_lowerCamelCase  :    Optional[int]					=					patch_size
							_lowerCamelCase  :    Dict					=					num_channels
							_lowerCamelCase  :    Dict					=					qkv_bias
							_lowerCamelCase  :    Dict					=					encoder_stride






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    version.parse("""1.11"""		)
			@property
			def       A_					(						self						):
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]						)






			@property
			def       A_					(						self						):
							return 1E-4