
"""simple docstring"""





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


UpperCamelCase_					:						List[Any]				= logging.get_logger(__name__)



class __lowerCAmelCase				(					_lowercase      ):




   """simple docstring"""







   snake_case	      =				["input_features", "attention_mask"]




   def __init__(				self						:					List[Any]       ,   _snake_case						:					int=80       ,   _snake_case						:					Optional[int]=16_000       ,   _snake_case						:					Optional[Any]=0.0       ,   _snake_case						:					List[Any]=10       ,   _snake_case						:					str=25       ,   _snake_case						:					Optional[Any]="hamming_window"       ,   _snake_case						:					Optional[int]=32_768.0       ,   _snake_case						:					int=0.9_7       ,   _snake_case						:					Optional[int]=1.0       ,   _snake_case						:					List[str]=True       ,   _snake_case						:					Any=True       ,   _snake_case						:					Optional[int]=False       ,   **_snake_case						:					Tuple       ,   )      -> Union[str, Any]:







        """simple docstring"""
        super().__init__(feature_size=_snake_case       ,   sampling_rate=_snake_case       ,   padding_value=_snake_case       ,   **_snake_case	)

        A_				 =						feature_size
        A_				 =						sampling_rate
        A_				 =						padding_value
        A_				 =						hop_length
        A_				 =						win_length
        A_				 =						frame_signal_scale
        A_				 =						preemphasis_coeff
        A_				 =						mel_floor
        A_				 =						normalize_means
        A_				 =						normalize_vars
        A_				 =						win_function
        A_				 =						return_attention_mask

        A_				 =						win_length * sampling_rate // 1_000
        A_				 =						hop_length * sampling_rate // 1_000

        A_				 =						optimal_fft_length(self.sample_size	)
        A_				 =						(self.n_fft // 2) + 1




   def      lowerCamelCase__     (				self						:					Tuple       ,   _snake_case						:					np.array	)      -> np.ndarray:







        """simple docstring"""
        if self.win_function == "hamming_window":
             A_				 =						window_function(window_length=self.sample_size       ,   name=self.win_function       ,   periodic=_snake_case	)
        else:
             A_				 =						window_function(window_length=self.sample_size       ,   name=self.win_function	)

        A_				 =						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       ,   )

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




   def      lowerCamelCase__     (				self						:					List[Any]       ,   _snake_case						:					List[str]       ,   _snake_case						:					Union[str, Any]       ,   _snake_case						:					Tuple	)      -> str:







        """simple docstring"""
        # make sure we normalize float32 arrays
        if self.normalize_means:
             A_				 =						x[:input_length].mean(axis=0	)
             A_				 =						np.subtract(_snake_case       ,   _snake_case	)
        if self.normalize_vars:
             A_				 =						x[:input_length].std(axis=0	)
             A_				 =						np.divide(_snake_case       ,   _snake_case	)

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

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

        return x




   def      lowerCamelCase__     (				self						:					Dict       ,   _snake_case						:					List[np.ndarray]       ,   _snake_case						:					Optional[np.ndarray] = None	)      -> List[np.ndarray]:







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






   def __call__(				self						:					str       ,   _snake_case						:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]       ,   _snake_case						:					Union[bool, str, PaddingStrategy] = False       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					bool = False       ,   _snake_case						:					Optional[int] = None       ,   _snake_case						:					Optional[bool] = None       ,   _snake_case						:					Optional[Union[str, TensorType]] = None       ,   _snake_case						:					Optional[int] = None       ,   **_snake_case						:					Tuple       ,   )      -> 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."	)

        A_				 =						isinstance(_snake_case       ,   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(_snake_case       ,   (list, tuple)	) and (isinstance(raw_speech[0]       ,   (np.ndarray, tuple, list)	))
        )

        if is_batched:
             A_				 =						[np.asarray(_snake_case       ,   dtype=np.floataa	) for speech in raw_speech]
        elif not is_batched and not isinstance(_snake_case       ,   np.ndarray	):
             A_				 =						np.asarray(_snake_case       ,   dtype=np.floataa	)
        elif isinstance(_snake_case       ,   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_				 =						[raw_speech]

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

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

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

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

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

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

        return padded_inputs






"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


UpperCamelCase_					:						Any				= logging.get_logger(__name__)

UpperCamelCase_					:						Any				= {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_					:						List[str]				= {
    '''vocab_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''yjernite/retribert-base-uncased''': (
            '''https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json'''
        ),
    },
}

UpperCamelCase_					:						Union[str, Any]				= {
    '''yjernite/retribert-base-uncased''': 512,
}


UpperCamelCase_					:						str				= {
    '''yjernite/retribert-base-uncased''': {'''do_lower_case''': True},
}



class __lowerCAmelCase				(					_lowercase      ):




			"""simple docstring"""







			snake_case	      =				VOCAB_FILES_NAMES
			snake_case	      =				PRETRAINED_VOCAB_FILES_MAP
			snake_case	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			snake_case	      =				PRETRAINED_INIT_CONFIGURATION
			snake_case	      =				RetriBertTokenizer
			snake_case	      =				["input_ids", "attention_mask"]




			def __init__(				self						:					Tuple       ,   _snake_case						:					int=None       ,   _snake_case						:					Any=None       ,   _snake_case						:					str=True       ,   _snake_case						:					List[Any]="[UNK]"       ,   _snake_case						:					Optional[Any]="[SEP]"       ,   _snake_case						:					str="[PAD]"       ,   _snake_case						:					List[str]="[CLS]"       ,   _snake_case						:					List[str]="[MASK]"       ,   _snake_case						:					Dict=True       ,   _snake_case						:					Optional[int]=None       ,   **_snake_case						:					Tuple       ,   )      -> Union[str, Any]:







								"""simple docstring"""
								super().__init__(
								    _snake_case       ,   tokenizer_file=_snake_case       ,   do_lower_case=_snake_case       ,   unk_token=_snake_case       ,   sep_token=_snake_case       ,   pad_token=_snake_case       ,   cls_token=_snake_case       ,   mask_token=_snake_case       ,   tokenize_chinese_chars=_snake_case       ,   strip_accents=_snake_case       ,   **_snake_case       ,   )

								A_				 =						json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
								if (
								    normalizer_state.get("lowercase"       ,   _snake_case	) != do_lower_case
								    or normalizer_state.get("strip_accents"       ,   _snake_case	) != strip_accents
								    or normalizer_state.get("handle_chinese_chars"       ,   _snake_case	) != tokenize_chinese_chars
								):
													A_				 =						getattr(_snake_case       ,   normalizer_state.pop("type"	)	)
													A_				 =						do_lower_case
													A_				 =						strip_accents
													A_				 =						tokenize_chinese_chars
													A_				 =						normalizer_class(**_snake_case	)

								A_				 =						do_lower_case




			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					List[Any]       ,   _snake_case						:					Union[str, Any]=None	)      -> List[Any]:







								"""simple docstring"""
								A_				 =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
													output += token_ids_a + [self.sep_token_id]

								return output




			def      lowerCamelCase__     (				self						:					Any       ,   _snake_case						:					List[int]       ,   _snake_case						:					Optional[List[int]] = None	)      -> List[int]:







								"""simple docstring"""
								A_				 =						[self.sep_token_id]
								A_				 =						[self.cls_token_id]
								if token_ids_a is None:
													return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]






			def      lowerCamelCase__     (				self						:					Union[str, Any]       ,   _snake_case						:					str       ,   _snake_case						:					Optional[str] = None	)      -> Tuple[str]:







								"""simple docstring"""
								A_				 =						self._tokenizer.model.save(_snake_case       ,   name=_snake_case	)
								return tuple(_snake_case	)




