


"""simple docstring"""


import copy
import re
class snake_case      :
		__UpperCamelCase           =			'hp'
		__UpperCamelCase           =			{}
		__UpperCamelCase           =			None







		@classmethod
		def  a_ (					cls      :       Any    ,   a__      :       str    ,   a__      :       Any		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			prefix
						_A					  =			defaults
						cls.build_naming_info()







		@staticmethod
		def  a_ (					a__      :       Optional[Any]    ,   a__      :       Dict		)       ->						Tuple:





						'''simple docstring'''







						if len(a__		) == 0:
										return ""
						_A					  =			None
						if any(char.isdigit() for char in word		):
										raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number"""		)
						if word in info["short_word"]:
										return info["short_word"][word]
						for prefix_len in range(1    ,   len(a__		) + 1		):
										_A					  =			word[:prefix_len]
										if prefix in info["reverse_short_word"]:
														continue
										else:
														_A					  =			prefix
														break

						if short_word is None:
										# Paranoid fallback
										def int_to_alphabetic(a__      :       Optional[Any]		):
														_A					  =			""
														while integer != 0:
																		_A					  =			chr(ord("A"		) + integer % 10		) + s
																		integer //= 10
														return s

										_A					  =			0
										while True:
														_A					  =			word + "#" + int_to_alphabetic(a__		)
														if sword in info["reverse_short_word"]:
																		continue
														else:
																		_A					  =			sword
																		break

						_A					  =			short_word
						_A					  =			word
						return short_word







		@staticmethod
		def  a_ (					a__      :       Dict    ,   a__      :       Optional[Any]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			param_name.split("_"		)

						_A					  =			[TrialShortNamer.shortname_for_word(a__    ,   a__		) for word in words]

						# We try to create a separatorless short name, but if there is a collision we have to fallback
						# to a separated short name
						_A					  =			["", "_"]

						for separator in separators:
										_A					  =			separator.join(a__		)
										if shortname not in info["reverse_short_param"]:
														_A					  =			shortname
														_A					  =			param_name
														return shortname

						return param_name







		@staticmethod
		def  a_ (					a__      :       Optional[Any]    ,   a__      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			TrialShortNamer.shortname_for_key(a__    ,   a__		)
						_A					  =			short_name
						_A					  =			param_name







		@classmethod
		def  a_ (					cls      :       str		)       ->						str:





						'''simple docstring'''







						if cls.NAMING_INFO is not None:
										return

						_A					  =			{
						    "short_word": {},
						    "reverse_short_word": {},
						    "short_param": {},
						    "reverse_short_param": {},
						}

						_A					  =			list(cls.DEFAULTS.keys()		)

						for k in field_keys:
										cls.add_new_param_name(a__    ,   a__		)

						_A					  =			info







		@classmethod
		def  a_ (					cls      :       Optional[int]    ,   a__      :       List[Any]		)       ->						Optional[int]:





						'''simple docstring'''







						cls.build_naming_info()
						assert cls.PREFIX is not None
						_A					  =			[copy.copy(cls.PREFIX		)]

						for k, v in params.items():
										if k not in cls.DEFAULTS:
														raise Exception(F"""You should provide a default value for the param name {k} with value {v}"""		)
										if v == cls.DEFAULTS[k]:
														# The default value is not added to the name
														continue

										_A					  =			cls.NAMING_INFO["short_param"][k]

										if isinstance(a__    ,   a__		):
														_A					  =			1 if v else 0

										_A					  =			"" if isinstance(a__    ,   (int, float)		) else "-"
										_A					  =			F"""{key}{sep}{v}"""
										name.append(a__		)

						return "_".join(a__		)







		@classmethod
		def  a_ (					cls      :       Dict    ,   a__      :       int		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			repr[len(cls.PREFIX		) + 1 :]
						if repr == "":
										_A					  =			[]
						else:
										_A					  =			repr.split("_"		)

						_A					  =			{}

						for value in values:
										if "-" in value:
														_A  ,		_A					  =			value.split("-"		)
										else:
														_A					  =			re.sub("[0-9.]"    ,   ""    ,   a__		)
														_A					  =			float(re.sub("[^0-9.]"    ,   ""    ,   a__		)		)

										_A					  =			cls.NAMING_INFO["reverse_short_param"][p_k]

										_A					  =			p_v

						for k in cls.DEFAULTS:
										if k not in parameters:
														_A					  =			cls.DEFAULTS[k]

						return parameters



"""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 snake_case      (  _UpperCamelCase):
		__UpperCamelCase           =			['input_features']







		def __init__(					self      :       int    ,   a__      :       Optional[Any]=80    ,   a__      :       Optional[int]=1_60_00    ,   a__      :       int=1_60    ,   a__      :       Union[str, Any]=30    ,   a__      :       Tuple=4_00    ,   a__      :       List[Any]=0.0    ,   a__      :       Optional[Any]=False    ,   **a__      :       List[Any]    ,   )       ->						str:





						'''simple docstring'''







						super().__init__(
						    feature_size=a__    ,   sampling_rate=a__    ,   padding_value=a__    ,   return_attention_mask=a__    ,   **a__    ,   )
						_A					  =			n_fft
						_A					  =			hop_length
						_A					  =			chunk_length
						_A					  =			chunk_length * sampling_rate
						_A					  =			self.n_samples // hop_length
						_A					  =			sampling_rate
						_A					  =			mel_filter_bank(
						    num_frequency_bins=1 + n_fft // 2    ,   num_mel_filters=a__    ,   min_frequency=0.0    ,   max_frequency=8_0_0_0.0    ,   sampling_rate=a__    ,   norm="slaney"    ,   mel_scale="slaney"    ,   )







		def  a_ (					self      :       int    ,   a__      :       np.array		)       ->						np.ndarray:





						'''simple docstring'''







						_A					  =			spectrogram(
						    a__    ,   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"    ,   )
						_A					  =			log_spec[:, :-1]
						_A					  =			np.maximum(a__    ,   log_spec.max() - 8.0		)
						_A					  =			(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_ (					a__      :       List[np.ndarray]    ,   a__      :       List[np.ndarray]    ,   a__      :       float = 0.0		)       ->						List[np.ndarray]:





						'''simple docstring'''







						if attention_mask is not None:
										_A					  =			np.array(a__    ,   np.intaa		)
										_A					  =			[]

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

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

						return normed_input_values







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





						'''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.__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					  =			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					  =			is_batched_numpy or (
						    isinstance(a__    ,   (list, tuple)		) and (isinstance(raw_speech[0]    ,   (np.ndarray, tuple, list)		))
						)

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

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

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

						# convert into correct format for padding

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

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

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

						_A					  =			[self._np_extract_fbank_features(a__		) for waveform in input_features[0]]

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

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

						if return_tensors is not None:
										_A					  =			padded_inputs.convert_to_tensors(a__		)

						return padded_inputs







		def  a_ (					self      :       Dict		)       ->						Dict[str, Any]:





						'''simple docstring'''







						_A					  =			copy.deepcopy(self.__dict__		)
						_A					  =			self.__class__.__name__
						if "mel_filters" in output:
										del output["mel_filters"]
						return output