






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


lowercase  							=					logging.get_logger(__name__)




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''input_features''', '''attention_mask''']




	def __init__(       self       ,			a=80       ,			a=1_60_00       ,			a=0.0       ,			a=10       ,			a=25       ,			a="hamming_window"       ,			a=32_768.0       ,			a=0.97       ,			a=1.0       ,			a=True       ,			a=True       ,			a=False       ,			**a       ,			)       ->    Any:
								super().__init__(feature_size=a       ,			sampling_rate=a       ,			padding_value=a       ,			**a					)

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

								snake_case_				=							win_length * sampling_rate // 10_00
								snake_case_				=							hop_length * sampling_rate // 10_00

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




	def 							_UpperCamelCase			(       self       ,			a					)       ->    np.ndarray:
								if self.win_function == "hamming_window":
															snake_case_				=							window_function(window_length=self.sample_size       ,			name=self.win_function       ,			periodic=a					)
								else:
															snake_case_				=							window_function(window_length=self.sample_size       ,			name=self.win_function					)

								snake_case_				=							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       ,			)

								snake_case_				=							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					)       ->    List[str]:
								# make sure we normalize float32 arrays
								if self.normalize_means:
															snake_case_				=							x[:input_length].mean(axis=0					)
															snake_case_				=							np.subtract(a       ,			a					)
								if self.normalize_vars:
															snake_case_				=							x[:input_length].std(axis=0					)
															snake_case_				=							np.divide(a       ,			a					)

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

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

								return x




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[np.ndarray]:
								snake_case_				=							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       ,			)       ->    BatchFeature:

								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.'					)

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

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

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

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

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

								snake_case_				=							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
								snake_case_				=							padded_inputs.get('input_features'					)
								if isinstance(input_features[0]       ,			a					):
															snake_case_				=							[np.asarray(a       ,			dtype=np.floataa					) for feature in input_features]

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

								if self.normalize_means or self.normalize_vars:
															snake_case_				=							(
															    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
															)
															snake_case_				=							self.normalize(
															    padded_inputs['input_features']       ,			attention_mask=a					)

								if return_tensors is not None:
															snake_case_				=							padded_inputs.convert_to_tensors(a					)

								return padded_inputs













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

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

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[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 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





