

from typing import List, Optional, Union

import numpy as np

from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			["input_features", "attention_mask"]


						def __init__(       self						,					A_=80						,					A_=16000						,					A_=0.0						,					A_=10						,					A_=25						,					A_="hamming_window"						,					A_=3_27_68.0						,					A_=0.97						,					A_=1.0						,					A_=True						,					A_=True						,					A_=False						,					**A_						,					):







										'''simple docstring'''


										super().__init__(feature_size=A_						,					sampling_rate=A_						,					padding_value=A_						,					**A_	)

										_UpperCAmelCase       :		Tuple 		=    feature_size
										_UpperCAmelCase       :		List[str] 		=    sampling_rate
										_UpperCAmelCase       :		Optional[int] 		=    padding_value
										_UpperCAmelCase       :		Union[str, Any] 		=    hop_length
										_UpperCAmelCase       :		Union[str, Any] 		=    win_length
										_UpperCAmelCase       :		Dict 		=    frame_signal_scale
										_UpperCAmelCase       :		Any 		=    preemphasis_coeff
										_UpperCAmelCase       :		Union[str, Any] 		=    mel_floor
										_UpperCAmelCase       :		List[Any] 		=    normalize_means
										_UpperCAmelCase       :		Any 		=    normalize_vars
										_UpperCAmelCase       :		Optional[Any] 		=    win_function
										_UpperCAmelCase       :		Union[str, Any] 		=    return_attention_mask

										_UpperCAmelCase       :		Tuple 		=    win_length * sampling_rate // 1000
										_UpperCAmelCase       :		Tuple 		=    hop_length * sampling_rate // 1000

										_UpperCAmelCase       :		Any 		=    optimal_fft_length(self.sample_size	)
										_UpperCAmelCase       :		Optional[Any] 		=    (self.n_fft // 2) + 1


						def        _UpperCAmelCase				(       self						,					A_	):







										'''simple docstring'''


										if self.win_function == "hamming_window":
														_UpperCAmelCase       :		Optional[int] 		=    window_function(window_length=self.sample_size						,					name=self.win_function						,					periodic=A_	)
										else:
														_UpperCAmelCase       :		str 		=    window_function(window_length=self.sample_size						,					name=self.win_function	)

										_UpperCAmelCase       :		Any 		=    mel_filter_bank(
										    num_frequency_bins=self.n_freqs						,					num_mel_filters=self.feature_size						,					min_frequency=0.0						,					max_frequency=self.sampling_rate / 2.0						,					sampling_rate=self.sampling_rate						,					)

										_UpperCAmelCase       :		int 		=    spectrogram(
										    one_waveform * self.frame_signal_scale						,					window=A_						,					frame_length=self.sample_size						,					hop_length=self.sample_stride						,					fft_length=self.n_fft						,					center=A_						,					preemphasis=self.preemphasis_coeff						,					mel_filters=A_						,					mel_floor=self.mel_floor						,					log_mel="log"						,					)
										return msfc_features.T


						def        _UpperCAmelCase				(       self						,					A_						,					A_						,					A_	):







										'''simple docstring'''


										if self.normalize_means:
														_UpperCAmelCase       :		Dict 		=    x[:input_length].mean(axis=0	)
														_UpperCAmelCase       :		Union[str, Any] 		=    np.subtract(A_						,					A_	)
										if self.normalize_vars:
														_UpperCAmelCase       :		int 		=    x[:input_length].std(axis=0	)
														_UpperCAmelCase       :		Any 		=    np.divide(A_						,					A_	)

										if input_length < x.shape[0]:
														_UpperCAmelCase       :		Optional[Any] 		=    padding_value

										# make sure array is in float32
										_UpperCAmelCase       :		Tuple 		=    x.astype(np.floataa	)

										return x


						def        _UpperCAmelCase				(       self						,					A_						,					A_ = None	):







										'''simple docstring'''


										_UpperCAmelCase       :		Dict 		=    attention_mask.sum(-1	) if attention_mask is not None else [x.shape[0] for x in input_features]
										return [self._normalize_one(A_						,					A_						,					self.padding_value	) for x, n in zip(A_						,					A_	)]





						def __call__(       self						,					A_						,					A_ = False						,					A_ = None						,					A_ = False						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = None						,					**A_						,					):







										'''simple docstring'''


										if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																		raise ValueError(
																		    f'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																		    f' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																		    f' {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."	)

										_UpperCAmelCase       :		Optional[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}'	)
										_UpperCAmelCase       :		Optional[Any] 		=    is_batched_numpy or (
										    isinstance(A_						,					(list, tuple)	) and (isinstance(raw_speech[0]						,					(np.ndarray, tuple, list)	))
										)

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

										# always return batch
										if not is_batched:
														_UpperCAmelCase       :		str 		=    [raw_speech]

										# extract fbank features
										_UpperCAmelCase       :		str 		=    [self._extract_mfsc_features(A_	) for one_waveform in raw_speech]

										# convert into correct format for padding
										_UpperCAmelCase       :		Union[str, Any] 		=    BatchFeature({"input_features": features}	)

										_UpperCAmelCase       :		List[Any] 		=    self.pad(
										    A_						,					padding=A_						,					max_length=A_						,					truncation=A_						,					pad_to_multiple_of=A_						,					return_attention_mask=A_						,					**A_						,					)
										# make sure list is in array format
										_UpperCAmelCase       :		Dict 		=    padded_inputs.get("input_features"	)
										if isinstance(input_features[0]						,					A_	):
														_UpperCAmelCase       :		Union[str, Any] 		=    [np.asarray(A_						,					dtype=np.floataa	) for feature in input_features]

										_UpperCAmelCase       :		Optional[int] 		=    padded_inputs.get("attention_mask"	)
										if attention_mask is not None:
														_UpperCAmelCase       :		Any 		=    [np.asarray(A_						,					dtype=np.intaa	) for array in attention_mask]

										if self.normalize_means or self.normalize_vars:
														_UpperCAmelCase       :		int 		=    (
														    np.array(A_						,					dtype=np.intaa	)
														    if self._get_padding_strategies(A_						,					max_length=A_	) is not PaddingStrategy.DO_NOT_PAD
														    and padding
														    else None
														)
														_UpperCAmelCase       :		int 		=    self.normalize(
														    padded_inputs["input_features"]						,					attention_mask=A_	)

										if return_tensors is not None:
														_UpperCAmelCase       :		str 		=    padded_inputs.convert_to_tensors(A_	)

										return padded_inputs





from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/config.json'}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"openai-gpt"
						_lowercase    						=			{
						    "max_position_embeddings": "n_positions",
						    "hidden_size": "n_embd",
						    "num_attention_heads": "n_head",
						    "num_hidden_layers": "n_layer",
						}


						def __init__(       self						,					A_=40478						,					A_=512						,					A_=768						,					A_=12						,					A_=12						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=0.1						,					A_=1e-5						,					A_=0.02						,					A_="cls_index"						,					A_=True						,					A_=None						,					A_=True						,					A_=0.1						,					**A_						,					):







										'''simple docstring'''


										_UpperCAmelCase       :		Any 		=    vocab_size
										_UpperCAmelCase       :		str 		=    n_positions
										_UpperCAmelCase       :		List[Any] 		=    n_embd
										_UpperCAmelCase       :		Dict 		=    n_layer
										_UpperCAmelCase       :		Any 		=    n_head
										_UpperCAmelCase       :		int 		=    afn
										_UpperCAmelCase       :		int 		=    resid_pdrop
										_UpperCAmelCase       :		Tuple 		=    embd_pdrop
										_UpperCAmelCase       :		Optional[Any] 		=    attn_pdrop
										_UpperCAmelCase       :		Any 		=    layer_norm_epsilon
										_UpperCAmelCase       :		List[str] 		=    initializer_range
										_UpperCAmelCase       :		Union[str, Any] 		=    summary_type
										_UpperCAmelCase       :		List[Any] 		=    summary_use_proj
										_UpperCAmelCase       :		Optional[Any] 		=    summary_activation
										_UpperCAmelCase       :		int 		=    summary_first_dropout
										_UpperCAmelCase       :		List[str] 		=    summary_proj_to_labels
										super().__init__(**A_	)


