





'''simple docstring'''




import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


a__					:			Optional[Any]          =     logging.getLogger(__name__)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Dict:







	if os.path.exists(__UpperCAmelCase							):
		if os.path.exists(os.path.join(__UpperCAmelCase					,				'config.json'							)							) and os.path.isfile(
		    os.path.join(__UpperCAmelCase					,				'config.json'							)							):
			os.remove(os.path.join(__UpperCAmelCase					,				'config.json'							)							)
		if os.path.exists(os.path.join(__UpperCAmelCase					,				'pytorch_model.bin'							)							) and os.path.isfile(
		    os.path.join(__UpperCAmelCase					,				'pytorch_model.bin'							)							):
			os.remove(os.path.join(__UpperCAmelCase					,				'pytorch_model.bin'							)							)
	else:
		os.makedirs(__UpperCAmelCase							)
	model.save_pretrained(__UpperCAmelCase							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=False							)   ->str:
	snake_case__	       =   2
	if unlogit:
		snake_case__	       =   torch.pow(__UpperCAmelCase					,				__UpperCAmelCase							)
	snake_case__	       =   p * torch.log(__UpperCAmelCase							)
	snake_case__	       =   0
	return -plogp.sum(dim=-1							)




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->Optional[int]:
	logger.info('lv, h >\t' + '\t'.join(f'''{x + 1}''' for x in range(len(__UpperCAmelCase							)							)							)							)
	for row in range(len(__UpperCAmelCase							)							):
		if tensor.dtype != torch.long:
			logger.info(f'''layer {row + 1}:\t''' + '\t'.join(f'''{x:.5f}''' for x in tensor[row].cpu().data							)							)
		else:
			logger.info(f'''layer {row + 1}:\t''' + '\t'.join(f'''{x:d}''' for x in tensor[row].cpu().data							)							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=True					,				UpperCAmelCase_=True					,				UpperCAmelCase_=None					,				UpperCAmelCase_=False							)   ->Dict:
	snake_case__       ,  snake_case__	       =   model.config.num_hidden_layers, model.config.num_attention_heads
	snake_case__	       =   torch.zeros(__UpperCAmelCase					,				__UpperCAmelCase							).to(args.device							)
	snake_case__	       =   torch.zeros(__UpperCAmelCase					,				__UpperCAmelCase							).to(args.device							)

	if head_mask is None:
		snake_case__	       =   torch.ones(__UpperCAmelCase					,				__UpperCAmelCase							).to(args.device							)

	head_mask.requires_grad_(requires_grad=__UpperCAmelCase							)
	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
	if actually_pruned:
		snake_case__	       =   None

	snake_case__	       =   0.0
	snake_case__	       =   0.0
	for step, inputs in enumerate(tqdm(__UpperCAmelCase					,				desc='Iteration'					,				disable=args.local_rank not in [-1, 0]							)							):
		snake_case__	       =   tuple(t.to(args.device							) for t in inputs							)
		((snake_case__  )       ,    )	       =   inputs

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		snake_case__	       =   model(__UpperCAmelCase					,				labels=__UpperCAmelCase					,				head_mask=__UpperCAmelCase							)
		#  (loss), lm_logits, presents, (all hidden_states), (attentions)
		snake_case__       ,  snake_case__       ,  snake_case__	       =   (
		    outputs[0],
		    outputs[1],
		    outputs[-1],
		)  # Loss and logits are the first, attention the last
		loss.backward()  # Backpropagate to populate the gradients in the head mask
		total_loss += loss.detach().cpu().numpy()
		if compute_entropy:
			for layer, attn in enumerate(__UpperCAmelCase							):
				snake_case__	       =   entropy(attn.detach()					,				__UpperCAmelCase							)
				attn_entropy[layer] += masked_entropy.sum(-1							).sum(0							).sum(0							).detach()

		if compute_importance:
			head_importance += head_mask.grad.abs().detach()
		tot_tokens += torch.ones_like(__UpperCAmelCase							).float().detach().sum().data

	# Normalize
	attn_entropy /= tot_tokens
	head_importance /= tot_tokens
	# Layerwise importance normalization
	if not args.dont_normalize_importance_by_layer:
		snake_case__	       =   2
		snake_case__	       =   torch.pow(torch.pow(__UpperCAmelCase					,				__UpperCAmelCase							).sum(-1							)					,				1 / exponent							)
		head_importance /= norm_by_layer.unsqueeze(-1							) + 1E-20

	if not args.dont_normalize_global_importance:
		snake_case__	       =   (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

	# Print matrices
	if compute_entropy:
		logger.info('Attention entropies'							)
		print_ad_tensor(__UpperCAmelCase							)
	if compute_importance:
		logger.info('Head importance scores'							)
		print_ad_tensor(__UpperCAmelCase							)
	logger.info('Head ranked by importance scores'							)
	snake_case__	       =   torch.zeros(head_importance.numel()					,				dtype=torch.long					,				device=args.device							)
	snake_case__	       =   torch.arange(
	    head_importance.numel()					,				device=args.device							)
	snake_case__	       =   head_ranks.view_as(__UpperCAmelCase							)
	print_ad_tensor(__UpperCAmelCase							)
	return attn_entropy, head_importance, total_loss




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Tuple:
	snake_case__       ,  snake_case__       ,  snake_case__	       =   compute_heads_importance(__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase					,				compute_entropy=__UpperCAmelCase							)
	snake_case__	       =   1 / loss  # instead of downsteam score use the LM loss
	logger.info('Pruning: original score: %f, threshold: %f'					,				__UpperCAmelCase					,				original_score * args.masking_threshold							)

	snake_case__	       =   torch.ones_like(__UpperCAmelCase							)
	snake_case__	       =   max(1					,				int(new_head_mask.numel() * args.masking_amount							)							)

	snake_case__	       =   original_score
	while current_score >= original_score * args.masking_threshold:
		snake_case__	       =   new_head_mask.clone().detach()  # save current head mask
		# heads from least important to most - keep only not-masked heads
		snake_case__	       =   float('Inf'							)
		snake_case__	       =   head_importance.view(-1							).sort()[1]

		if len(__UpperCAmelCase							) <= num_to_mask:
			print('BREAK BY num_to_mask'							)
			break

		# mask heads
		snake_case__	       =   current_heads_to_mask[:num_to_mask]
		logger.info('Heads to mask: %s'					,				str(current_heads_to_mask.tolist()							)							)
		snake_case__	       =   new_head_mask.view(-1							)
		snake_case__	       =   0.0
		snake_case__	       =   new_head_mask.view_as(__UpperCAmelCase							)
		snake_case__	       =   new_head_mask.clone().detach()
		print_ad_tensor(__UpperCAmelCase							)

		# Compute metric and head importance again
		snake_case__       ,  snake_case__       ,  snake_case__	       =   compute_heads_importance(
		    __UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase					,				compute_entropy=__UpperCAmelCase					,				head_mask=__UpperCAmelCase							)
		snake_case__	       =   1 / loss
		logger.info(
		    'Masking: current score: %f, remaining heads %d (%.1f percents)'					,				__UpperCAmelCase					,				new_head_mask.sum()					,				new_head_mask.sum() / new_head_mask.numel() * 1_00					,				)

	logger.info('Final head mask'							)
	print_ad_tensor(__UpperCAmelCase							)
	np.save(os.path.join(args.output_dir					,				'head_mask.npy'							)					,				head_mask.detach().cpu().numpy()							)

	return head_mask




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)   ->Any:
	snake_case__	       =   datetime.now()
	snake_case__       ,  snake_case__       ,  snake_case__	       =   compute_heads_importance(
	    __UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase					,				compute_entropy=__UpperCAmelCase					,				compute_importance=__UpperCAmelCase					,				head_mask=__UpperCAmelCase							)
	snake_case__	       =   1 / loss
	snake_case__	       =   datetime.now() - before_time

	snake_case__	       =   sum(p.numel() for p in model.parameters()							)
	snake_case__	       =   {
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__UpperCAmelCase							)							)
	}

	for k, v in heads_to_prune.items():
		if isinstance(__UpperCAmelCase					,				__UpperCAmelCase							):
			snake_case__	       =   [
			    v,
			]

	assert sum(len(__UpperCAmelCase							) for h in heads_to_prune.values()							) == (1 - head_mask.long()).sum().item()
	model.prune_heads(__UpperCAmelCase							)
	snake_case__	       =   sum(p.numel() for p in model.parameters()							)

	snake_case__	       =   datetime.now()
	snake_case__       ,  snake_case__       ,  snake_case__	       =   compute_heads_importance(
	    __UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase					,				compute_entropy=__UpperCAmelCase					,				compute_importance=__UpperCAmelCase					,				head_mask=__UpperCAmelCase					,				actually_pruned=__UpperCAmelCase					,				)

	snake_case__	       =   1 / loss
	snake_case__	       =   datetime.now() - before_time

	logger.info(
	    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'					,				__UpperCAmelCase					,				__UpperCAmelCase					,				pruned_num_params / original_num_params * 1_00					,				)
	logger.info('Pruning: score with masking: %f score with pruning: %f'					,				__UpperCAmelCase					,				__UpperCAmelCase							)
	logger.info('Pruning: speed ratio (original timing / new timing): %f percents'					,				original_time / new_time * 1_00							)
	save_model(__UpperCAmelCase					,				args.output_dir							)




def 						__lowerCamelCase			(	)   ->int:
	snake_case__	       =   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--data_dir'					,				default=__UpperCAmelCase					,				type=__UpperCAmelCase					,				required=__UpperCAmelCase					,				help='The input data dir. Should contain the .tsv files (or other data files) for the task.'					,				)
	parser.add_argument(
	    '--model_name_or_path'					,				default=__UpperCAmelCase					,				type=__UpperCAmelCase					,				required=__UpperCAmelCase					,				help='Path to pretrained model or model identifier from huggingface.co/models'					,				)
	parser.add_argument(
	    '--output_dir'					,				default=__UpperCAmelCase					,				type=__UpperCAmelCase					,				required=__UpperCAmelCase					,				help='The output directory where the model predictions and checkpoints will be written.'					,				)

	# Other parameters
	parser.add_argument(
	    '--config_name'					,				default=''					,				type=__UpperCAmelCase					,				help='Pretrained config name or path if not the same as model_name_or_path'					,				)
	parser.add_argument(
	    '--tokenizer_name'					,				default=''					,				type=__UpperCAmelCase					,				help='Pretrained tokenizer name or path if not the same as model_name_or_path'					,				)
	parser.add_argument(
	    '--cache_dir'					,				default=__UpperCAmelCase					,				type=__UpperCAmelCase					,				help='Where do you want to store the pre-trained models downloaded from s3'					,				)
	parser.add_argument(
	    '--data_subset'					,				type=__UpperCAmelCase					,				default=-1					,				help='If > 0: limit the data to a subset of data_subset instances.'							)
	parser.add_argument(
	    '--overwrite_output_dir'					,				action='store_true'					,				help='Whether to overwrite data in output directory'							)
	parser.add_argument(
	    '--overwrite_cache'					,				action='store_true'					,				help='Overwrite the cached training and evaluation sets'							)

	parser.add_argument(
	    '--dont_normalize_importance_by_layer'					,				action='store_true'					,				help='Don\'t normalize importance score by layers'							)
	parser.add_argument(
	    '--dont_normalize_global_importance'					,				action='store_true'					,				help='Don\'t normalize all importance scores between 0 and 1'					,				)

	parser.add_argument(
	    '--try_masking'					,				action='store_true'					,				help='Whether to try to mask head until a threshold of accuracy.'							)
	parser.add_argument(
	    '--masking_threshold'					,				default=0.9					,				type=__UpperCAmelCase					,				help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'					,				)
	parser.add_argument(
	    '--masking_amount'					,				default=0.1					,				type=__UpperCAmelCase					,				help='Amount to heads to masking at each masking step.'							)
	parser.add_argument('--metric_name'					,				default='acc'					,				type=__UpperCAmelCase					,				help='Metric to use for head masking.'							)

	parser.add_argument(
	    '--max_seq_length'					,				default=1_28					,				type=__UpperCAmelCase					,				help=(
	        'The maximum total input sequence length after WordPiece tokenization. \n'
	        'Sequences longer than this will be truncated, sequences shorter padded.'
	    )					,				)
	parser.add_argument('--batch_size'					,				default=1					,				type=__UpperCAmelCase					,				help='Batch size.'							)

	parser.add_argument('--seed'					,				type=__UpperCAmelCase					,				default=42							)
	parser.add_argument('--local_rank'					,				type=__UpperCAmelCase					,				default=-1					,				help='local_rank for distributed training on gpus'							)
	parser.add_argument('--no_cuda'					,				action='store_true'					,				help='Whether not to use CUDA when available'							)
	parser.add_argument('--server_ip'					,				type=__UpperCAmelCase					,				default=''					,				help='Can be used for distant debugging.'							)
	parser.add_argument('--server_port'					,				type=__UpperCAmelCase					,				default=''					,				help='Can be used for distant debugging.'							)
	snake_case__	       =   parser.parse_args()

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print('Waiting for debugger attach'							)
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)					,				redirect_output=__UpperCAmelCase							)
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		snake_case__	       =   torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
		snake_case__	       =   0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank							)
		snake_case__	       =   torch.device('cuda'					,				args.local_rank							)
		snake_case__	       =   1
		torch.distributed.init_process_group(backend='nccl'							)  # Initializes the distributed backend

	# Setup logging
	logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN							)
	logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device					,				args.n_gpu					,				bool(args.local_rank != -1							)							)							)

	snake_case__	       =   GPTaLMHeadModel.from_pretrained(args.model_name_or_path							)

	# Distributed and parallel training
	model.to(args.device							)
	if args.local_rank != -1:
		snake_case__	       =   nn.parallel.DistributedDataParallel(
		    __UpperCAmelCase					,				device_ids=[args.local_rank]					,				output_device=args.local_rank					,				find_unused_parameters=__UpperCAmelCase							)
	elif args.n_gpu > 1:
		snake_case__	       =   nn.DataParallel(__UpperCAmelCase							)

	# Print/save training arguments
	os.makedirs(args.output_dir					,				exist_ok=__UpperCAmelCase							)
	torch.save(__UpperCAmelCase					,				os.path.join(args.output_dir					,				'run_args.bin'							)							)
	logger.info('Training/evaluation parameters %s'					,				__UpperCAmelCase							)

	# Prepare dataset
	snake_case__	       =   np.concatenate(
	    [
	        np.loadtxt(args.data_dir					,				dtype=np.intaa							),
	    ]							)
	snake_case__	       =   (torch.from_numpy(__UpperCAmelCase							),)
	snake_case__	       =   TensorDataset(*__UpperCAmelCase							)
	snake_case__	       =   RandomSampler(__UpperCAmelCase							)
	snake_case__	       =   DataLoader(__UpperCAmelCase					,				sampler=__UpperCAmelCase					,				batch_size=args.batch_size							)

	# Compute head entropy and importance score
	compute_heads_importance(__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase							)

	# Try head masking (set heads to zero until the score goes under a threshole)
	# and head pruning (remove masked heads and see the effect on the network)
	if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
		snake_case__	       =   mask_heads(__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase							)
		prune_heads(__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase							)


if __name__ == "__main__":
			main()










'''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





