





'''simple docstring'''




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

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

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


a__					:			Union[str, Any]          =     logging.get_logger(__name__)

a__					:			Dict          =     list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
a__					:			Dict          =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)






@dataclass
class 				__snake_case     :
	__lowerCAmelCase       						=  field(
	    default=_snake_case       ,       metadata={'''help''': '''Model type selected in the list: ''' + ''', '''.join(_snake_case							)}							)
	__lowerCAmelCase       						=  field(
	    default=_snake_case       ,       metadata={'''help''': '''The input data dir. Should contain the .json files for the SQuAD 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=128       ,       metadata={'''help''': '''When splitting up a long document into chunks, how much stride to take between chunks.'''}       ,       )
	__lowerCAmelCase       						=  field(
	    default=64       ,       metadata={
	        '''help''': (
	            '''The maximum number of tokens for the question. Questions longer than this will '''
	            '''be truncated to this length.'''
	        )
	    }       ,       )
	__lowerCAmelCase       						=  field(
	    default=30       ,       metadata={
	        '''help''': (
	            '''The maximum length of an answer that can be generated. This is needed because the start '''
	            '''and end predictions are not conditioned on one another.'''
	        )
	    }       ,       )
	__lowerCAmelCase       						=  field(
	    default=_snake_case       ,       metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}							)
	__lowerCAmelCase       						=  field(
	    default=_snake_case       ,       metadata={'''help''': '''If true, the SQuAD examples contain some that do not have an answer.'''}							)
	__lowerCAmelCase       						=  field(
	    default=0.0       ,       metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}							)
	__lowerCAmelCase       						=  field(
	    default=20       ,       metadata={'''help''': '''If null_score - best_non_null is greater than the threshold predict null.'''}							)
	__lowerCAmelCase       						=  field(
	    default=0       ,       metadata={
	        '''help''': (
	            '''language id of input for language-specific xlm models (see'''
	            ''' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'''
	        )
	    }       ,       )
	__lowerCAmelCase       						=  field(default=1       ,       metadata={'''help''': '''multiple threads for converting example to features'''}							)






class 				__snake_case     (					_snake_case							):
	__lowerCAmelCase       						=  'train'
	__lowerCAmelCase       						=  'dev'






class 				__snake_case     (					_snake_case							):
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42
	__lowerCAmelCase       						=  42





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = Split.train      ,       UpperCamelCase_ = False      ,       UpperCamelCase_ = None      ,       UpperCamelCase_ = "pt"      ,       )      ->		List[Any]:
		snake_case__	       =   args
		snake_case__	       =   is_language_sensitive
		snake_case__	       =   SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
		if isinstance(UpperCamelCase_      ,       UpperCamelCase_		):
			try:
				snake_case__	       =   Split[mode]
			except KeyError:
				raise KeyError('mode is not a valid split name'		)
		snake_case__	       =   mode
		# Load data features from cache or dataset file
		snake_case__	       =   'v2' if args.version_2_with_negative else 'v1'
		snake_case__	       =   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}_{version_tag}'''      ,       )

		# Make sure only the first process in distributed training processes the dataset,
		# and the others will use the cache.
		snake_case__	       =   cached_features_file + '.lock'
		with FileLock(UpperCamelCase_		):
			if os.path.exists(UpperCamelCase_		) and not args.overwrite_cache:
				snake_case__	       =   time.time()
				snake_case__	       =   torch.load(UpperCamelCase_		)

				# Legacy cache files have only features, while new cache files
				# will have dataset and examples also.
				snake_case__	       =   self.old_features['features']
				snake_case__	       =   self.old_features.get('dataset'      ,       UpperCamelCase_		)
				snake_case__	       =   self.old_features.get('examples'      ,       UpperCamelCase_		)
				logger.info(
				    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''      ,       time.time() - start		)

				if self.dataset is None or self.examples is None:
					logger.warning(
					    F'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
					    ' future run'		)
			else:
				if mode == Split.dev:
					snake_case__	       =   self.processor.get_dev_examples(args.data_dir		)
				else:
					snake_case__	       =   self.processor.get_train_examples(args.data_dir		)

				snake_case__       ,  snake_case__	       =   squad_convert_examples_to_features(
				    examples=self.examples      ,       tokenizer=UpperCamelCase_      ,       max_seq_length=args.max_seq_length      ,       doc_stride=args.doc_stride      ,       max_query_length=args.max_query_length      ,       is_training=mode == Split.train      ,       threads=args.threads      ,       return_dataset=UpperCamelCase_      ,       )

				snake_case__	       =   time.time()
				torch.save(
				    {'features': self.features, 'dataset': self.dataset, 'examples': self.examples}      ,       UpperCamelCase_      ,       )
				# ^ 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		)      ->		str:
		return len(self.features		)





	def __getitem__(		self      ,       UpperCamelCase_		)      ->		Tuple:
		snake_case__	       =   self.features[i]

		snake_case__	       =   torch.tensor(feature.input_ids      ,       dtype=torch.long		)
		snake_case__	       =   torch.tensor(feature.attention_mask      ,       dtype=torch.long		)
		snake_case__	       =   torch.tensor(feature.token_type_ids      ,       dtype=torch.long		)
		snake_case__	       =   torch.tensor(feature.cls_index      ,       dtype=torch.long		)
		snake_case__	       =   torch.tensor(feature.p_mask      ,       dtype=torch.float		)
		snake_case__	       =   torch.tensor(feature.is_impossible      ,       dtype=torch.float		)

		snake_case__	       =   {
		    'input_ids': input_ids,
		    'attention_mask': attention_mask,
		    'token_type_ids': token_type_ids,
		}

		if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
			del inputs["token_type_ids"]

		if self.args.model_type in ["xlnet", "xlm"]:
			inputs.update({'cls_index': cls_index, 'p_mask': p_mask}		)
			if self.args.version_2_with_negative:
				inputs.update({'is_impossible': is_impossible}		)
			if self.is_language_sensitive:
				inputs.update({'langs': (torch.ones(input_ids.shape      ,       dtype=torch.intaa		) * self.args.lang_id)}		)

		if self.mode == Split.train:
			snake_case__	       =   torch.tensor(feature.start_position      ,       dtype=torch.long		)
			snake_case__	       =   torch.tensor(feature.end_position      ,       dtype=torch.long		)
			inputs.update({'start_positions': start_positions, 'end_positions': end_positions}		)

		return inputs










'''simple docstring'''

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


a   	=    logging.get_logger(__name__)







class __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						['input_features', 'is_longer']





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]=64					,lowerCamelCase					:      Any=4_8000					,lowerCamelCase					:      int=480					,lowerCamelCase					:      Any=10					,lowerCamelCase					:      Dict=1024					,lowerCamelCase					:      Union[str, Any]=0.0					,lowerCamelCase					:      int=False					,lowerCamelCase					:      float = 0					,lowerCamelCase					:      float = 1_4000					,lowerCamelCase					:      int = None					,lowerCamelCase					:      str = "fusion"					,lowerCamelCase					:      str = "repeatpad"					,**lowerCamelCase					:      Dict					,):

      '''simple docstring'''

      super().__init__(
          feature_size=lowerCamelCase					,sampling_rate=lowerCamelCase					,padding_value=lowerCamelCase					,return_attention_mask=lowerCamelCase					,**lowerCamelCase					,)
      __SCREAMING_SNAKE_CASE   				=							top_db
      __SCREAMING_SNAKE_CASE   				=							truncation
      __SCREAMING_SNAKE_CASE   				=							padding
      __SCREAMING_SNAKE_CASE   				=							fft_window_size
      __SCREAMING_SNAKE_CASE   				=							(fft_window_size >> 1) + 1
      __SCREAMING_SNAKE_CASE   				=							hop_length
      __SCREAMING_SNAKE_CASE   				=							max_length_s
      __SCREAMING_SNAKE_CASE   				=							max_length_s * sampling_rate
      __SCREAMING_SNAKE_CASE   				=							sampling_rate
      __SCREAMING_SNAKE_CASE   				=							frequency_min
      __SCREAMING_SNAKE_CASE   				=							frequency_max
      __SCREAMING_SNAKE_CASE   				=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase					,min_frequency=lowerCamelCase					,max_frequency=lowerCamelCase					,sampling_rate=lowerCamelCase					,norm=lowerCamelCase					,mel_scale="""htk"""					,)
      __SCREAMING_SNAKE_CASE   				=							mel_filter_bank(
          num_frequency_bins=self.nb_frequency_bins					,num_mel_filters=lowerCamelCase					,min_frequency=lowerCamelCase					,max_frequency=lowerCamelCase					,sampling_rate=lowerCamelCase					,norm="""slaney"""					,mel_scale="""slaney"""					,)





   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							copy.deepcopy(self.__dict__     )
      __SCREAMING_SNAKE_CASE   				=							self.__class__.__name__
      if "mel_filters" in output:
         del output["mel_filters"]
      if "mel_filters_slaney" in output:
         del output["mel_filters_slaney"]
      return output





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      np.array					,lowerCamelCase					:      Optional[np.array] = None     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							spectrogram(
          lowerCamelCase					,window_function(self.fft_window_size					,"""hann"""     )					,frame_length=self.fft_window_size					,hop_length=self.hop_length					,power=2.0					,mel_filters=lowerCamelCase					,log_mel="""dB"""					,)
      return log_mel_spectrogram.T





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							np.array_split(list(range(0					,total_frames - chunk_frames + 1     )     )					,3     )
      if len(ranges[1]     ) == 0:
         # if the audio is too short, we just use the first chunk
         __SCREAMING_SNAKE_CASE   				=							[0]
      if len(ranges[2]     ) == 0:
         # if the audio is too short, we just use the first chunk
         __SCREAMING_SNAKE_CASE   				=							[0]
      # randomly choose index for each part
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[0]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[1]     )
      __SCREAMING_SNAKE_CASE   				=							np.random.choice(ranges[2]     )

      __SCREAMING_SNAKE_CASE   				=							mel[idx_front : idx_front + chunk_frames, :]
      __SCREAMING_SNAKE_CASE   				=							mel[idx_middle : idx_middle + chunk_frames, :]
      __SCREAMING_SNAKE_CASE   				=							mel[idx_back : idx_back + chunk_frames, :]

      __SCREAMING_SNAKE_CASE   				=							torch.tensor(mel[None, None, :]     )
      __SCREAMING_SNAKE_CASE   				=							torch.nn.functional.interpolate(
          lowerCamelCase					,size=[chunk_frames, 64]					,mode="""bilinear"""					,align_corners=lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							mel_shrink[0][0].numpy()
      __SCREAMING_SNAKE_CASE   				=							np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]					,axis=0     )
      return mel_fusion





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      np.array					,lowerCamelCase					:      str					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      if waveform.shape[0] > max_length:
         if truncation == "rand_trunc":
            __SCREAMING_SNAKE_CASE   				=							True
            # random crop to max_length (for compatibility) -> this should be handled by self.pad
            __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     ) - max_length
            __SCREAMING_SNAKE_CASE   				=							np.random.randint(0					,overflow + 1     )
            __SCREAMING_SNAKE_CASE   				=							waveform[idx : idx + max_length]
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters_slaney     )[None, :]
         elif truncation == "fusion":
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters     )
            __SCREAMING_SNAKE_CASE   				=							max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
            __SCREAMING_SNAKE_CASE   				=							mel.shape[0]
            if chunk_frames == total_frames:
               # there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
               # In this case, we just use the whole audio.
               __SCREAMING_SNAKE_CASE   				=							np.stack([mel, mel, mel, mel]					,axis=0     )
               __SCREAMING_SNAKE_CASE   				=							False
            else:
               __SCREAMING_SNAKE_CASE   				=							self._random_mel_fusion(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
               __SCREAMING_SNAKE_CASE   				=							True
         else:
            raise NotImplementedError(f"""data_truncating {truncation} not implemented"""     )

      else:
         __SCREAMING_SNAKE_CASE   				=							False
         # only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
         if waveform.shape[0] < max_length:
            if padding == "repeat":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,n_repeat + 1     )     )[:max_length]
            if padding == "repeatpad":
               __SCREAMING_SNAKE_CASE   				=							int(max_length / len(lowerCamelCase     )     )
               __SCREAMING_SNAKE_CASE   				=							np.stack(np.tile(lowerCamelCase					,lowerCamelCase     )     )
            __SCREAMING_SNAKE_CASE   				=							np.pad(lowerCamelCase					,(0, max_length - waveform.shape[0])					,mode="""constant"""					,constant_values=0     )

         if truncation == "fusion":
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters     )
            __SCREAMING_SNAKE_CASE   				=							np.stack([input_mel, input_mel, input_mel, input_mel]					,axis=0     )
         else:
            __SCREAMING_SNAKE_CASE   				=							self._np_extract_fbank_features(lowerCamelCase					,self.mel_filters_slaney     )[None, :]

      return input_mel, longer





   def __call__(       self					:      Optional[Any]					,lowerCamelCase					:      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowerCamelCase					:      str = None					,lowerCamelCase					:      Optional[str] = None					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Optional[int] = None					,lowerCamelCase					:      Optional[Union[str, TensorType]] = None					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							truncation if truncation is not None else self.truncation
      __SCREAMING_SNAKE_CASE   				=							padding if padding else self.padding

      if sampling_rate is not None:
         if sampling_rate != self.sampling_rate:
            raise ValueError(
                f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
                f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
                f""" was sampled with {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."""     )

      __SCREAMING_SNAKE_CASE   				=							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}"""     )
      __SCREAMING_SNAKE_CASE   				=							is_batched_numpy or (
          isinstance(lowerCamelCase					,(list, tuple)     ) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)     ))
      )

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

      # always return batch
      if not is_batched:
         __SCREAMING_SNAKE_CASE   				=							[np.asarray(lowerCamelCase     )]

      # convert to mel spectrogram, truncate and pad if needed.
      __SCREAMING_SNAKE_CASE   				=							[
          self._get_input_mel(lowerCamelCase					,max_length if max_length else self.nb_max_samples					,lowerCamelCase					,lowerCamelCase     )
          for waveform in raw_speech
      ]

      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							[]
      for mel, longer in padded_inputs:
         input_mel.append(lowerCamelCase     )
         is_longer.append(lowerCamelCase     )

      if truncation == "fusion" and sum(lowerCamelCase     ) == 0:
         # if no audio is longer than 10s, then randomly select one audio to be longer
         __SCREAMING_SNAKE_CASE   				=							np.random.randint(0					,len(lowerCamelCase     )     )
         __SCREAMING_SNAKE_CASE   				=							True

      if isinstance(input_mel[0]					,lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							[np.asarray(lowerCamelCase					,dtype=np.floataa     ) for feature in input_mel]

      # is_longer is a list of bool
      __SCREAMING_SNAKE_CASE   				=							[[longer] for longer in is_longer]

      __SCREAMING_SNAKE_CASE   				=							{"""input_features""": input_mel, """is_longer""": is_longer}
      __SCREAMING_SNAKE_CASE   				=							BatchFeature(lowerCamelCase     )

      if return_tensors is not None:
         __SCREAMING_SNAKE_CASE   				=							input_features.convert_to_tensors(lowerCamelCase     )

      return input_features





