




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 TensorType, logging


lowercase__      :			Tuple           =					logging.get_logger(__name__)




class 			a__       (					lowerCAmelCase__      ):
     a		:			Optional[int]							=	["input_values", "attention_mask"]







     def __init__(	self  ,		A=1  ,		A=16000  ,		A=128  ,		A=1024  ,		A=0.0  ,		A=True  ,		A=-4.2_6_7_7_3_9_3  ,		A=4.5_6_8_9_9_7_4  ,		A=False  ,		**A  ,		)  ->    Optional[int]:



          '''simple docstring'''



          super().__init__(feature_size=_lowerCamelCase  ,		sampling_rate=_lowerCamelCase  ,		padding_value=_lowerCamelCase  ,		**_lowerCamelCase )
          a      =      num_mel_bins
          a      =      max_length
          a      =      do_normalize
          a      =      mean
          a      =      std
          a      =      return_attention_mask







     def 			lowerCAmelCase_				(	self  ,		A  ,		A  ,		)  ->    np.ndarray:



          '''simple docstring'''



          a      =      torch.from_numpy(_lowerCamelCase ).unsqueeze(0 )
          a      =      ta_kaldi.fbank(
              _lowerCamelCase  ,		htk_compat=_lowerCamelCase  ,		sample_frequency=self.sampling_rate  ,		use_energy=_lowerCamelCase  ,		window_type="hanning"  ,		num_mel_bins=self.num_mel_bins  ,		dither=0.0  ,		frame_shift=10  ,		)

          a      =      fbank.shape[0]
          a      =      max_length - n_frames

          # pad or truncate, depending on difference
          if difference > 0:
               a      =      torch.nn.ZeroPadad((0, 0, 0, difference) )
               a      =      pad_module(_lowerCamelCase )
          elif difference < 0:
               a      =      fbank[0:max_length, :]

          a      =      fbank.numpy()

          return fbank







     def 			lowerCAmelCase_				(	self  ,		A )  ->    np.ndarray:



          '''simple docstring'''



          return (input_values - (self.mean)) / (self.std * 2)







     def __call__(	self  ,		A  ,		A = None  ,		A = None  ,		**A  ,		)  ->    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(_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}''' )
          a      =      is_batched_numpy or (
              isinstance(_lowerCamelCase  ,		(list, tuple) ) and (isinstance(raw_speech[0]  ,		(np.ndarray, tuple, list) ))
          )

          if is_batched:
               a      =      [np.asarray(_lowerCamelCase  ,		dtype=np.floataa ) for speech in raw_speech]
          elif not is_batched and not isinstance(_lowerCamelCase  ,		np.ndarray ):
               a      =      np.asarray(_lowerCamelCase  ,		dtype=np.floataa )
          elif isinstance(_lowerCamelCase  ,		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 and pad/truncate to max_length
          a      =      [self._extract_fbank_features(_lowerCamelCase  ,		max_length=self.max_length ) for waveform in raw_speech]

          # convert into BatchFeature
          a      =      BatchFeature({"input_values": features} )

          # make sure list is in array format
          a      =      padded_inputs.get("input_values" )
          if isinstance(input_values[0]  ,		_lowerCamelCase ):
               a      =      [np.asarray(_lowerCamelCase  ,		dtype=np.floataa ) for feature in input_values]

          # normalization
          if self.do_normalize:
               a      =      [self.normalize(_lowerCamelCase ) for feature in input_values]

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

          return padded_inputs








'''simple docstring'''



import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_SCREAMING_SNAKE_CASE    =			'''\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
'''

# TODO: Add description of the dataset here
# You can copy an official description
_SCREAMING_SNAKE_CASE    =			'''\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
'''

# TODO: Add a link to an official homepage for the dataset here
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add the licence for the dataset here if you can find it
_SCREAMING_SNAKE_CASE    =			''''''

# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_SCREAMING_SNAKE_CASE    =			{
    '''first_domain''': '''https://huggingface.co/great-new-dataset-first_domain.zip''',
    '''second_domain''': '''https://huggingface.co/great-new-dataset-second_domain.zip''',
}






class 							__lowercase				(					datasets.GeneratorBasedBuilder  ):







	'''simple docstring'''

	a			: Tuple      =				datasets.Version("1.1.0"  )

	# This is an example of a dataset with multiple configurations.
	# If you don't want/need to define several sub-sets in your dataset,
	# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

	# If you need to make complex sub-parts in the datasets with configurable options
	# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
	# BUILDER_CONFIG_CLASS = MyBuilderConfig

	# You will be able to load one or the other configurations in the following list with
	# data = datasets.load_dataset('my_dataset', 'first_domain')
	# data = datasets.load_dataset('my_dataset', 'second_domain')
	a			: List[Any]      =				[
	    datasets.BuilderConfig(name="first_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a first domain"  ),
	    datasets.BuilderConfig(name="second_domain"       ,					version=lowerCAmelCase__       ,					description="This part of my dataset covers a second domain"  ),
	]

	a			: Dict      =				"first_domain"  # It's not mandatory to have a default configuration. Just use one if it make sense.


	def   _UpperCAmelCase   (self       )							->							List[str]:







				'''simple docstring'''





				if self.config.name == "first_domain":  # This is the name of the configuration selected in BUILDER_CONFIGS above
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option1''': datasets.Value('''string'''       ),
							        '''answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				else:  # This is an example to show how to have different features for "first_domain" and "second_domain"
							__lowercase   =							datasets.Features(
							    {
							        '''sentence''': datasets.Value('''string'''       ),
							        '''option2''': datasets.Value('''string'''       ),
							        '''second_domain_answer''': datasets.Value('''string'''       )
							        # These are the features of your dataset like images, labels ...
							    }       )
				return datasets.DatasetInfo(
				    # This is the description that will appear on the datasets page.
				    description=_DESCRIPTION   ,features=_lowerCamelCase   ,homepage=_HOMEPAGE   ,license=_LICENSE   ,citation=_CITATION   ,)


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				__lowercase   =							_URLS[self.config.name]
				__lowercase   =							dl_manager.download_and_extract(_lowerCamelCase       )
				return [
				    datasets.SplitGenerator(
				        name=datasets.Split.TRAIN   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''train.jsonl'''       ),
				            '''split''': '''train''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.VALIDATION   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''dev.jsonl'''       ),
				            '''split''': '''dev''',
				        }   ,),
				    datasets.SplitGenerator(
				        name=datasets.Split.TEST   ,gen_kwargs={
				            '''filepath''': os.path.join(_lowerCamelCase   ,'''test.jsonl'''       ),
				            '''split''': '''test'''
				        }   ,),
				]


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase       )							->							str:







				'''simple docstring'''





				with open(_lowerCamelCase   ,encoding='''utf-8'''       ) as f:
							for key, row in enumerate(_lowerCamelCase       ):
										__lowercase   =							json.loads(_lowerCamelCase       )
										if self.config.name == "first_domain":
													# Yields examples as (key, example) tuples
													yield key, {
													    "sentence": data["sentence"],
													    "option1": data["option1"],
													    "answer": "" if split == "test" else data["answer"],
													}
										else:
													yield key, {
													    "sentence": data["sentence"],
													    "option2": data["option2"],
													    "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
													}





