



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


snake_case    :						str  =						logging.get_logger(__name__)




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Tuple           =						['input_features', 'is_longer']







					def __init__(       self      :     Optional[Any]			,		_A      :     Any=64			,		_A      :     List[Any]=4_8000			,		_A      :     Dict=480			,		_A      :     Optional[Any]=10			,		_A      :     List[str]=1024			,		_A      :     int=0.0			,		_A      :     Union[str, Any]=False			,		_A      :     float = 0			,		_A      :     float = 1_4000			,		_A      :     int = None			,		_A      :     str = "fusion"			,		_A      :     str = "repeatpad"			,		**_A      :     Any			,		):
											super().__init__(
											    feature_size=_A			,		sampling_rate=_A			,		padding_value=_A			,		return_attention_mask=_A			,		**_A			,		)
											A__	:							Union[str, Any]						    = top_db
											A__	:							Union[str, Any]						    = truncation
											A__	:							Union[str, Any]						    = padding
											A__	:							int						    = fft_window_size
											A__	:							Tuple						    = (fft_window_size >> 1) + 1
											A__	:							List[Any]						    = hop_length
											A__	:							Optional[int]						    = max_length_s
											A__	:							Union[str, Any]						    = max_length_s * sampling_rate
											A__	:							Dict						    = sampling_rate
											A__	:							str						    = frequency_min
											A__	:							Dict						    = frequency_max
											A__	:							List[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"			,		)
											A__	:							Tuple						    = 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  _lowercase			(       self      :     Any):
											A__	:							str						    = copy.deepcopy(self.__dict__)
											A__	:							Tuple						    = 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  _lowercase			(       self      :     Union[str, Any]			,		_A      :     np.array			,		_A      :     Optional[np.array] = None):
											A__	:							Tuple						    = 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  _lowercase			(       self      :     Dict			,		_A      :     Dict			,		_A      :     Optional[int]			,		_A      :     Optional[Any]):
											A__	:							Optional[Any]						    = 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
																	A__	:							List[Any]						    = [0]
											if len(ranges[2]) == 0:
																	# if the audio is too short, we just use the first chunk
																	A__	:							List[str]						    = [0]
											# randomly choose index for each part
											A__	:							Union[str, Any]						    = np.random.choice(ranges[0])
											A__	:							Optional[int]						    = np.random.choice(ranges[1])
											A__	:							Union[str, Any]						    = np.random.choice(ranges[2])

											A__	:							Optional[Any]						    = mel[idx_front : idx_front + chunk_frames, :]
											A__	:							Tuple						    = mel[idx_middle : idx_middle + chunk_frames, :]
											A__	:							Tuple						    = mel[idx_back : idx_back + chunk_frames, :]

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







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

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

																	if truncation == "fusion":
																							A__	:							str						    = self._np_extract_fbank_features(_A			,		self.mel_filters)
																							A__	:							List[Any]						    = np.stack([input_mel, input_mel, input_mel, input_mel]			,		axis=0)
																	else:
																							A__	:							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      :     Tuple			,		):
											A__	:							Optional[int]						    = truncation if truncation is not None else self.truncation
											A__	:							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.")

											A__	:							Union[str, Any]						    = 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}')
											A__	:							List[Any]						    = is_batched_numpy or (
											    isinstance(_A			,		(list, tuple)) and (isinstance(raw_speech[0]			,		(np.ndarray, tuple, list)))
											)

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

											# always return batch
											if not is_batched:
																	A__	:							Optional[Any]						    = [np.asarray(_A)]

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

											A__	:							str						    = []
											A__	:							Optional[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
																	A__	:							str						    = np.random.randint(0			,		len(_A))
																	A__	:							Any						    = True

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

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

											A__	:							List[str]						    = {"input_features": input_mel, "is_longer": is_longer}
											A__	:							int						    = BatchFeature(_A)

											if return_tensors is not None:
																	A__	:							Optional[Any]						    = input_features.convert_to_tensors(_A)

											return input_features




from collections import OrderedDict
from typing import Mapping

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


snake_case    :						Optional[int]  =						logging.get_logger(__name__)

snake_case    :						Union[str, Any]  =						{
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Optional[Any]           =						'data2vec-text'







					def __init__(       self      :     Optional[Any]			,		_A      :     List[str]=3_0522			,		_A      :     Optional[int]=768			,		_A      :     Union[str, Any]=12			,		_A      :     str=12			,		_A      :     Optional[int]=3072			,		_A      :     Dict="gelu"			,		_A      :     List[str]=0.1			,		_A      :     Any=0.1			,		_A      :     str=512			,		_A      :     Any=2			,		_A      :     str=0.02			,		_A      :     List[str]=1e-12			,		_A      :     Optional[int]=1			,		_A      :     Any=0			,		_A      :     Any=2			,		_A      :     List[Any]="absolute"			,		_A      :     Tuple=True			,		_A      :     str=None			,		**_A      :     List[Any]			,		):
											super().__init__(pad_token_id=_A			,		bos_token_id=_A			,		eos_token_id=_A			,		**_A)

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




class 							lowerCAmelCase__  (	UpperCamelCase						):







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