from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A_      =      logging.get_logger(__name__)






class      __lowercase					(    _A    ):
						lowercase					 =     ['input_features', 'attention_mask']

						def __init__(	self				:      List[Any]							,							__lowerCamelCase				:      Optional[int]=80							,							__lowerCamelCase				:      List[str]=1_60_00							,							__lowerCamelCase				:      Union[str, Any]=80							,							__lowerCamelCase				:      str=0.0							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      Optional[Any]=True							,							__lowerCamelCase				:      Any=True							,							**__lowerCamelCase				:      Optional[int]							,							)							->		Union[str, Any]:


									'''simple docstring'''

									super().__init__(feature_size=__lowerCamelCase							,							sampling_rate=__lowerCamelCase							,							padding_value=__lowerCamelCase							,							**__lowerCamelCase   )
									lowercase							=		num_mel_bins
									lowercase							=		do_ceptral_normalize
									lowercase							=		normalize_means
									lowercase							=		normalize_vars
									lowercase							=		True

						def        __a		(	self				:      Any							,							__lowerCamelCase				:      np.ndarray							,							)							->		np.ndarray:


									'''simple docstring'''

									lowercase							=		waveform * (2**15)  # Kaldi compliance: 16-bit signed integers
									lowercase							=		torch.from_numpy(__lowerCamelCase   ).unsqueeze(0   )
									lowercase							=		ta_kaldi.fbank(__lowerCamelCase							,							num_mel_bins=self.num_mel_bins							,							sample_frequency=self.sampling_rate   )
									return features.numpy()

						@staticmethod
						def        __a		(	__lowerCamelCase				:      np.ndarray							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      Optional[bool] = True							,							__lowerCamelCase				:      Optional[bool] = True							,							__lowerCamelCase				:      float = 0.0							,							)							->		np.ndarray:


									'''simple docstring'''

									if normalize_means:
												lowercase							=		x[:input_length].mean(axis=0   )
												lowercase							=		np.subtract(__lowerCamelCase							,							__lowerCamelCase   )
									if normalize_vars:
												lowercase							=		x[:input_length].std(axis=0   )
												lowercase							=		np.divide(__lowerCamelCase							,							__lowerCamelCase   )

									if input_length < x.shape[0]:
												lowercase							=		padding_value

									# make sure array is in float32
									lowercase							=		x.astype(np.floataa   )

									return x

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      List[np.ndarray]							,							__lowerCamelCase				:      Optional[np.ndarray] = None   )							->		List[np.ndarray]:


									'''simple docstring'''

									lowercase							=		attention_mask.sum(-1   ) if attention_mask is not None else [x.shape[0] for x in input_features]
									return [
									    self.utterance_cmvn(__lowerCamelCase							,							__lowerCamelCase							,							self.normalize_means							,							self.normalize_vars							,							self.padding_value   )
									    for x, n in zip(__lowerCamelCase							,							__lowerCamelCase   )
									]



						def __call__(	self				:      Dict							,							__lowerCamelCase				:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]							,							__lowerCamelCase				:      Union[bool, str, PaddingStrategy] = False							,							__lowerCamelCase				:      Optional[int] = None							,							__lowerCamelCase				:      bool = False							,							__lowerCamelCase				:      Optional[int] = None							,							__lowerCamelCase				:      Optional[Union[str, TensorType]] = None							,							__lowerCamelCase				:      Optional[int] = None							,							__lowerCamelCase				:      Optional[bool] = None							,							**__lowerCamelCase				:      Optional[int]							,							)							->		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} 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.'''   )

									lowercase							=		isinstance(__lowerCamelCase							,							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}'   )
									lowercase							=		is_batched_numpy or (
									    isinstance(__lowerCamelCase							,							(list, tuple)   ) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)   ))
									)

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

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

									# extract fbank features
									lowercase							=		[self._extract_fbank_features(__lowerCamelCase   ) for waveform in raw_speech]

									# convert into correct format for padding
									lowercase							=		BatchFeature({'''input_features''': features}   )

									lowercase							=		self.pad(
									    __lowerCamelCase							,							padding=__lowerCamelCase							,							max_length=__lowerCamelCase							,							truncation=__lowerCamelCase							,							pad_to_multiple_of=__lowerCamelCase							,							return_attention_mask=__lowerCamelCase							,							**__lowerCamelCase							,							)

									# make sure list is in array format
									lowercase							=		padded_inputs.get('''input_features'''   )
									if isinstance(input_features[0]							,							__lowerCamelCase   ):
												lowercase							=		[np.asarray(__lowerCamelCase							,							dtype=np.floataa   ) for feature in input_features]

									lowercase							=		padded_inputs.get('''attention_mask'''   )
									if attention_mask is not None:
												lowercase							=		[np.asarray(__lowerCamelCase							,							dtype=np.intaa   ) for array in attention_mask]

									# Utterance-level cepstral mean and variance normalization
									if self.do_ceptral_normalize:
												lowercase							=		(
												    np.array(__lowerCamelCase							,							dtype=np.intaa   )
												    if self._get_padding_strategies(__lowerCamelCase							,							max_length=__lowerCamelCase   ) is not PaddingStrategy.DO_NOT_PAD
												    else None
												)
												lowercase							=		self.normalize(
												    padded_inputs['''input_features''']							,							attention_mask=__lowerCamelCase   )

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

									return padded_inputs






from __future__ import annotations

from collections import deque






class      __lowercase					:

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      list[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									self.adlist.append(
									    {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}   )

									for keyword in keywords:
												self.add_keyword(__lowerCamelCase   )
									self.set_fail_transitions()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      str   )							->		int | None:


									'''simple docstring'''

									for state in self.adlist[current_state]["next_states"]:
												if char == self.adlist[state]["value"]:
															return state
									return None

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      str   )							->		None:


									'''simple docstring'''

									lowercase							=		0
									for character in keyword:
												lowercase							=		self.find_next_state(__lowerCamelCase							,							__lowerCamelCase   )
												if next_state is None:
															self.adlist.append(
															    {
															        '''value''': character,
															        '''next_states''': [],
															        '''fail_state''': 0,
															        '''output''': [],
															    }   )
															self.adlist[current_state]["next_states"].append(len(self.adlist   ) - 1   )
															lowercase							=		len(self.adlist   ) - 1
												else:
															lowercase							=		next_state
									self.adlist[current_state]["output"].append(__lowerCamelCase   )

						def        __a		(	self				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		deque()
									for node in self.adlist[0]["next_states"]:
												q.append(__lowerCamelCase   )
												lowercase							=		0
									while q:
												lowercase							=		q.popleft()
												for child in self.adlist[r]["next_states"]:
															q.append(__lowerCamelCase   )
															lowercase							=		self.adlist[r]['''fail_state''']
															while (
															    self.find_next_state(__lowerCamelCase							,							self.adlist[child]['''value''']   ) is None
															    and state != 0
															):
																		lowercase							=		self.adlist[state]['''fail_state''']
															lowercase							=		self.find_next_state(
															    __lowerCamelCase							,							self.adlist[child]['''value''']   )
															if self.adlist[child]["fail_state"] is None:
																		lowercase							=		0
															lowercase							=		(
															    self.adlist[child]['''output''']
															    + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
															)



						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str   )							->		dict[str, list[int]]:


									'''simple docstring'''

									lowercase							=		{}  # returns a dict with keywords and list of its occurrences
									lowercase							=		0
									for i in range(len(__lowerCamelCase   )   ):
												while (
												    self.find_next_state(__lowerCamelCase							,							string[i]   ) is None
												    and current_state != 0
												):
															lowercase							=		self.adlist[current_state]['''fail_state''']
												lowercase							=		self.find_next_state(__lowerCamelCase							,							string[i]   )
												if next_state is None:
															lowercase							=		0
												else:
															lowercase							=		next_state
															for key in self.adlist[current_state]["output"]:
																		if key not in result:
																					lowercase							=		[]
																		result[key].append(i - len(__lowerCamelCase   ) + 1   )
									return result


if __name__ == "__main__":
						import doctest

						doctest.testmod()





