
'''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


lowercase       :				Dict =   logging.getLogger(__name__)







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A      )			->      int:


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







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A=False      )			->      int:
	_snake_case  		=						2
	if unlogit:
		_snake_case  		=						torch.pow(__A     ,							__A      )
	_snake_case  		=						p * torch.log(__A      )
	_snake_case  		=						0
	return -plogp.sum(dim=-1      )







def 							SCREAMING_SNAKE_CASE__      (  __A      )			->      Optional[int]:
	logger.info('lv, h >\t' + '\t'.join(F'{x + 1}' for x in range(len(__A      )      )      )      )
	for row in range(len(__A      )      ):
		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 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A     ,							__A     ,							__A=True     ,							__A=True     ,							__A=None     ,							__A=False      )			->      Tuple:
	_snake_case		,					_snake_case  		=						model.config.num_hidden_layers, model.config.num_attention_heads
	_snake_case  		=						torch.zeros(__A     ,							__A      ).to(args.device      )
	_snake_case  		=						torch.zeros(__A     ,							__A      ).to(args.device      )

	if head_mask is None:
		_snake_case  		=						torch.ones(__A     ,							__A      ).to(args.device      )

	head_mask.requires_grad_(requires_grad=__A      )
	# 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(__A     ,							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(__A     ,							labels=__A     ,							head_mask=__A      )
		#  (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(__A      ):
				_snake_case  		=						entropy(attn.detach()     ,							__A      )
				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(__A      ).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(__A     ,							__A      ).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(__A      )
	if compute_importance:
		logger.info('Head importance scores'      )
		print_ad_tensor(__A      )
	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(__A      )
	print_ad_tensor(__A      )
	return attn_entropy, head_importance, total_loss







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A     ,							__A      )			->      Optional[Any]:
	_snake_case		,					_snake_case		,					_snake_case  		=						compute_heads_importance(__A     ,							__A     ,							__A     ,							compute_entropy=__A      )
	_snake_case  		=						1 / loss  # instead of downsteam score use the LM loss
	logger.info('Pruning: original score: %f, threshold: %f'     ,							__A     ,							original_score * args.masking_threshold      )

	_snake_case  		=						torch.ones_like(__A      )
	_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(__A      ) <= 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(__A      )
		_snake_case  		=						new_head_mask.clone().detach()
		print_ad_tensor(__A      )

		# Compute metric and head importance again
		_snake_case		,					_snake_case		,					_snake_case  		=						compute_heads_importance(
		    __A     ,							__A     ,							__A     ,							compute_entropy=__A     ,							head_mask=__A      )
		_snake_case  		=						1 / loss
		logger.info(
		    'Masking: current score: %f, remaining heads %d (%.1f percents)'     ,							__A     ,							new_head_mask.sum()     ,							new_head_mask.sum() / new_head_mask.numel() * 100     ,							)

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

	return head_mask







def 							SCREAMING_SNAKE_CASE__      (  __A     ,							__A     ,							__A     ,							__A      )			->      Optional[Any]:
	_snake_case  		=						datetime.now()
	_snake_case		,					_snake_case		,					_snake_case  		=						compute_heads_importance(
	    __A     ,							__A     ,							__A     ,							compute_entropy=__A     ,							compute_importance=__A     ,							head_mask=__A      )
	_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(__A      )      )
	}

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

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

	_snake_case  		=						datetime.now()
	_snake_case		,					_snake_case		,					_snake_case  		=						compute_heads_importance(
	    __A     ,							__A     ,							__A     ,							compute_entropy=__A     ,							compute_importance=__A     ,							head_mask=__A     ,							actually_pruned=__A     ,							)

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

	logger.info(
	    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'     ,							__A     ,							__A     ,							pruned_num_params / original_num_params * 100     ,							)
	logger.info('Pruning: score with masking: %f score with pruning: %f'     ,							__A     ,							__A      )
	logger.info('Pruning: speed ratio (original timing / new timing): %f percents'     ,							original_time / new_time * 100      )
	save_model(__A     ,							args.output_dir      )







def 							SCREAMING_SNAKE_CASE__      (  )			->      int:
	_snake_case  		=						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--data_dir'     ,							default=__A     ,							type=__A     ,							required=__A     ,							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=__A     ,							type=__A     ,							required=__A     ,							help='Path to pretrained model or model identifier from huggingface.co/models'     ,							)
	parser.add_argument(
	    '--output_dir'     ,							default=__A     ,							type=__A     ,							required=__A     ,							help='The output directory where the model predictions and checkpoints will be written.'     ,							)

	# Other parameters
	parser.add_argument(
	    '--config_name'     ,							default=''     ,							type=__A     ,							help='Pretrained config name or path if not the same as model_name_or_path'     ,							)
	parser.add_argument(
	    '--tokenizer_name'     ,							default=''     ,							type=__A     ,							help='Pretrained tokenizer name or path if not the same as model_name_or_path'     ,							)
	parser.add_argument(
	    '--cache_dir'     ,							default=__A     ,							type=__A     ,							help='Where do you want to store the pre-trained models downloaded from s3'     ,							)
	parser.add_argument(
	    '--data_subset'     ,							type=__A     ,							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=__A     ,							help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'     ,							)
	parser.add_argument(
	    '--masking_amount'     ,							default=0.1     ,							type=__A     ,							help='Amount to heads to masking at each masking step.'      )
	parser.add_argument('--metric_name'     ,							default='acc'     ,							type=__A     ,							help='Metric to use for head masking.'      )

	parser.add_argument(
	    '--max_seq_length'     ,							default=128     ,							type=__A     ,							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=__A     ,							help='Batch size.'      )

	parser.add_argument('--seed'     ,							type=__A     ,							default=42      )
	parser.add_argument('--local_rank'     ,							type=__A     ,							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=__A     ,							default=''     ,							help='Can be used for distant debugging.'      )
	parser.add_argument('--server_port'     ,							type=__A     ,							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=__A      )
		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(
		    __A     ,							device_ids=[args.local_rank]     ,							output_device=args.local_rank     ,							find_unused_parameters=__A      )
	elif args.n_gpu > 1:
		_snake_case  		=						nn.DataParallel(__A      )

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

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

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

	# 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(__A     ,							__A     ,							__A      )
		prune_heads(__A     ,							__A     ,							__A     ,							__A      )


if __name__ == "__main__":
					main()



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


_snake_case      :							Any     	=    logging.get_logger(__name__)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					['''input_features''', '''attention_mask''']





						def __init__(			self							:List[Any]		,    lowerCAmelCase__							:List[Any]=80		,    lowerCAmelCase__							:int=16000		,    lowerCAmelCase__							:str=0.0		,    lowerCAmelCase__							:int=10		,    lowerCAmelCase__							:List[Any]=25		,    lowerCAmelCase__							:List[str]="hamming_window"		,    lowerCAmelCase__							:int=3_27_68.0		,    lowerCAmelCase__							:str=0.97		,    lowerCAmelCase__							:List[str]=1.0		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:Tuple=True		,    lowerCAmelCase__							:str=False		,    **lowerCAmelCase__							:Optional[Any]		,    )  ->Union[str, Any]:
										super().__init__(feature_size=lowerCAmelCase__		,    sampling_rate=lowerCAmelCase__		,    padding_value=lowerCAmelCase__		,    **lowerCAmelCase__     )

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

										lowercase         =      win_length * sampling_rate // 1000
										lowercase         =      hop_length * sampling_rate // 1000

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





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]		,    lowerCAmelCase__							:np.array     )  ->np.ndarray:
										if self.win_function == "hamming_window":
														lowercase         =      window_function(window_length=self.sample_size		,    name=self.win_function		,    periodic=lowerCAmelCase__     )
										else:
														lowercase         =      window_function(window_length=self.sample_size		,    name=self.win_function     )

										lowercase         =      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		,    )

										lowercase         =      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        SCREAMING_SNAKE_CASE(			self							:Tuple		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Dict     )  ->int:
										# make sure we normalize float32 arrays
										if self.normalize_means:
														lowercase         =      x[:input_length].mean(axis=0     )
														lowercase         =      np.subtract(lowerCAmelCase__		,    lowerCAmelCase__     )
										if self.normalize_vars:
														lowercase         =      x[:input_length].std(axis=0     )
														lowercase         =      np.divide(lowerCAmelCase__		,    lowerCAmelCase__     )

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

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

										return x





						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[np.ndarray]		,    lowerCAmelCase__							:Optional[np.ndarray] = None     )  ->List[np.ndarray]:
										lowercase         =      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							:Any		,    lowerCAmelCase__							:Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]		,    lowerCAmelCase__							:Union[bool, str, PaddingStrategy] = False		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:bool = False		,    lowerCAmelCase__							:Optional[int] = None		,    lowerCAmelCase__							:Optional[bool] = None		,    lowerCAmelCase__							:Optional[Union[str, TensorType]] = None		,    lowerCAmelCase__							:Optional[int] = None		,    **lowerCAmelCase__							:Union[str, Any]		,    )  ->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."     )

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

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

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

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

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

										lowercase         =      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
										lowercase         =      padded_inputs.get("input_features"     )
										if isinstance(input_features[0]		,    lowerCAmelCase__     ):
														lowercase         =      [np.asarray(lowerCAmelCase__		,    dtype=np.floataa     ) for feature in input_features]

										lowercase         =      padded_inputs.get("attention_mask"     )
										if attention_mask is not None:
														lowercase         =      [np.asarray(lowerCAmelCase__		,    dtype=np.intaa     ) for array in attention_mask]

										if self.normalize_means or self.normalize_vars:
														lowercase         =      (
														    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
														)
														lowercase         =      self.normalize(
														    padded_inputs["input_features"]		,    attention_mask=lowerCAmelCase__     )

										if return_tensors is not None:
														lowercase         =      padded_inputs.convert_to_tensors(lowerCAmelCase__     )

										return padded_inputs
