





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


_a						=   logging.get_logger(__name__)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		["""input_features""", """attention_mask"""]





			def __init__(					self ,						__lowerCAmelCase=8_0 ,						__lowerCAmelCase=1_6_0_0_0 ,						__lowerCAmelCase=0.0 ,						__lowerCAmelCase=1_0 ,						__lowerCAmelCase=2_5 ,						__lowerCAmelCase="hamming_window" ,						__lowerCAmelCase=3_2768.0 ,						__lowerCAmelCase=0.97 ,						__lowerCAmelCase=1.0 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=True ,						__lowerCAmelCase=False ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







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

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

				lowerCamelCase__              =	win_length * sampling_rate // 1_0_0_0
				lowerCamelCase__              =	hop_length * sampling_rate // 1_0_0_0

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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				if self.win_function == "hamming_window":
					lowerCamelCase__              =	window_function(window_length=self.sample_size ,						name=self.win_function ,						periodic=__lowerCAmelCase							)
				else:
					lowerCamelCase__              =	window_function(window_length=self.sample_size ,						name=self.win_function							)

				lowerCamelCase__              =	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 ,						)

				lowerCamelCase__              =	spectrogram(
				    one_waveform * self.frame_signal_scale ,						window=__lowerCAmelCase ,						frame_length=self.sample_size ,						hop_length=self.sample_stride ,						fft_length=self.n_fft ,						center=__lowerCAmelCase ,						preemphasis=self.preemphasis_coeff ,						mel_filters=__lowerCAmelCase ,						mel_floor=self.mel_floor ,						log_mel='''log''' ,						)
				return msfc_features.T





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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

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

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

				return x





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase = None							):



				'''simple docstring'''







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





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



				'''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.'''							)

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

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

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

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

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

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

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

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

				if return_tensors is not None:
					lowerCamelCase__              =	padded_inputs.convert_to_tensors(__lowerCAmelCase							)

				return padded_inputs










import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_a						=   logging.get_logger(__name__)






@dataclass
class       __A					:

			'''simple docstring'''







			lowerCAmelCase_							  =		field(metadata={"""help""": """The name of the task to train on: """ + """, """.join(glue_processors.keys()				)}				)
			lowerCAmelCase_							  =		field(
			    metadata={"""help""": """The input data dir. Should contain the .tsv files (or other data files) for the task."""}				)
			lowerCAmelCase_							  =		field(
			    default=128    ,       metadata={
			        """help""": (
			            """The maximum total input sequence length after tokenization. Sequences longer """
			            """than this will be truncated, sequences shorter will be padded."""
			        )
			    }    ,       )
			lowerCAmelCase_							  =		field(
			    default=lowerCAmelCase    ,       metadata={"""help""": """Overwrite the cached training and evaluation sets"""}				)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.task_name.lower()






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		"""train"""
			lowerCAmelCase_							  =		"""dev"""
			lowerCAmelCase_							  =		"""test"""






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42
			lowerCAmelCase_							  =		42





			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = None ,						__lowerCAmelCase = Split.train ,						__lowerCAmelCase = None ,						):



				'''simple docstring'''







				warnings.warn(
				    '''This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '''
				    '''library. You can have a look at this example script for pointers: '''
				    '''https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py''' ,						__lowerCAmelCase ,						)
				lowerCamelCase__              =	args
				lowerCamelCase__              =	glue_processors[args.task_name]()
				lowerCamelCase__              =	glue_output_modes[args.task_name]
				if isinstance(__lowerCAmelCase ,						__lowerCAmelCase							):
					try:
						lowerCamelCase__              =	Split[mode]
					except KeyError:
						raise KeyError('''mode is not a valid split name'''							)
        # Load data features from cache or dataset file
				lowerCamelCase__              =	os.path.join(
				    cache_dir if cache_dir is not None else args.data_dir ,						F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' ,						)
				lowerCamelCase__              =	self.processor.get_labels()
				if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
				    "RobertaTokenizer",
				    "RobertaTokenizerFast",
				    "XLMRobertaTokenizer",
				    "BartTokenizer",
				    "BartTokenizerFast",
				):
					# HACK(label indices are swapped in RoBERTa pretrained model)
					lowerCamelCase__       ,			lowerCamelCase__              =	label_list[2], label_list[1]
				lowerCamelCase__              =	label_list

				# Make sure only the first process in distributed training processes the dataset,
				# and the others will use the cache.
				lowerCamelCase__              =	cached_features_file + '''.lock'''
				with FileLock(__lowerCAmelCase							):
					if os.path.exists(__lowerCAmelCase							) and not args.overwrite_cache:
						lowerCamelCase__              =	time.time()
						lowerCamelCase__              =	torch.load(__lowerCAmelCase							)
						logger.info(
						    F'Loading features from cached file {cached_features_file} [took %.3f s]' ,						time.time() - start							)
					else:
						logger.info(F'Creating features from dataset file at {args.data_dir}'							)

						if mode == Split.dev:
							lowerCamelCase__              =	self.processor.get_dev_examples(args.data_dir							)
						elif mode == Split.test:
							lowerCamelCase__              =	self.processor.get_test_examples(args.data_dir							)
						else:
							lowerCamelCase__              =	self.processor.get_train_examples(args.data_dir							)
						if limit_length is not None:
							lowerCamelCase__              =	examples[:limit_length]
						lowerCamelCase__              =	glue_convert_examples_to_features(
						    __lowerCAmelCase ,						__lowerCAmelCase ,						max_length=args.max_seq_length ,						label_list=__lowerCAmelCase ,						output_mode=self.output_mode ,						)
						lowerCamelCase__              =	time.time()
						torch.save(self.features ,						__lowerCAmelCase							)
						# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
						logger.info(
						    F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]'							)





			def __len__(					self							):



				'''simple docstring'''







				return len(self.features							)





			def __getitem__(					self ,						__lowerCAmelCase							):



				'''simple docstring'''







				return self.features[i]





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.label_list



