






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


__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			logging.getLogger(__name__)
def  snake_case						(__lowercase ,      __lowercase						)	->	Tuple:

						'''simple docstring'''



						if os.path.exists(_snake_case						):
												if os.path.exists(os.path.join(_snake_case ,      "config.json"						)						) and os.path.isfile(
												    os.path.join(_snake_case ,      "config.json"						)						):
																		os.remove(os.path.join(_snake_case ,      "config.json"						)						)
												if os.path.exists(os.path.join(_snake_case ,      "pytorch_model.bin"						)						) and os.path.isfile(
												    os.path.join(_snake_case ,      "pytorch_model.bin"						)						):
																		os.remove(os.path.join(_snake_case ,      "pytorch_model.bin"						)						)
						else:
												os.makedirs(_snake_case						)
						model.save_pretrained(_snake_case						)
def  snake_case						(__lowercase ,      __lowercase=False						)	->	Any:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   2
						if unlogit:
												_snake_case						:					List[Any]    =   torch.pow(_snake_case ,      _snake_case						)
						_snake_case						:					List[Any]    =   p * torch.log(_snake_case						)
						_snake_case						:					str    =   0
						return -plogp.sum(dim=-1						)
def  snake_case						(__lowercase						)	->	str:

						'''simple docstring'''



						logger.info("lv, h >\t" + "\t".join(F"""{x + 1}""" for x in range(len(_snake_case						)						)						)						)
						for row in range(len(_snake_case						)						):
												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  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase=True ,      __lowercase=True ,      __lowercase=None ,      __lowercase=False						)	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Optional[int]    =   model.config.num_hidden_layers, model.config.num_attention_heads
						_snake_case						:					Optional[Any]    =   torch.zeros(_snake_case ,      _snake_case						).to(args.device						)
						_snake_case						:					Optional[int]    =   torch.zeros(_snake_case ,      _snake_case						).to(args.device						)

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

						head_mask.requires_grad_(requires_grad=_snake_case						)
						# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
						if actually_pruned:
												_snake_case						:					Optional[Any]    =   None

						_snake_case						:					str    =   0.0
						_snake_case						:					Optional[int]    =   0.0
						for step, inputs in enumerate(tqdm(_snake_case ,      desc="Iteration" ,      disable=args.local_rank not in [-1, 0]						)						):
												_snake_case						:					Optional[int]    =   tuple(t.to(args.device						) for t in inputs						)
												(_snake_case   )						:					List[Any]    =   inputs

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												_snake_case						:					str    =   model(_snake_case ,      labels=_snake_case ,      head_mask=_snake_case						)
												#  (loss), lm_logits, presents, (all hidden_states), (attentions)
												_snake_case						:					Dict    =   (
												    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(_snake_case						):
																								_snake_case						:					str    =   entropy(attn.detach() ,      _snake_case						)
																								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(_snake_case						).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						:					List[Any]    =   2
												_snake_case						:					Dict    =   torch.pow(torch.pow(_snake_case ,      _snake_case						).sum(-1						) ,      1 / exponent						)
												head_importance /= norm_by_layer.unsqueeze(-1						) + 1e-20

						if not args.dont_normalize_global_importance:
												_snake_case						:					Optional[Any]    =   (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

						# Print matrices
						if compute_entropy:
												logger.info("Attention entropies"						)
												print_ad_tensor(_snake_case						)
						if compute_importance:
												logger.info("Head importance scores"						)
												print_ad_tensor(_snake_case						)
						logger.info("Head ranked by importance scores"						)
						_snake_case						:					List[Any]    =   torch.zeros(head_importance.numel() ,      dtype=torch.long ,      device=args.device						)
						_snake_case						:					int    =   torch.arange(
						    head_importance.numel() ,      device=args.device						)
						_snake_case						:					int    =   head_ranks.view_as(_snake_case						)
						print_ad_tensor(_snake_case						)
						return attn_entropy, head_importance, total_loss
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase						)	->	Union[str, Any]:

						'''simple docstring'''



						_snake_case						:					int    =   compute_heads_importance(_snake_case ,      _snake_case ,      _snake_case ,      compute_entropy=_snake_case						)
						_snake_case						:					List[str]    =   1 / loss  # instead of downsteam score use the LM loss
						logger.info("Pruning: original score: %f, threshold: %f" ,      _snake_case ,      original_score * args.masking_threshold						)

						_snake_case						:					Tuple    =   torch.ones_like(_snake_case						)
						_snake_case						:					Union[str, Any]    =   max(1 ,      int(new_head_mask.numel() * args.masking_amount						)						)

						_snake_case						:					Any    =   original_score
						while current_score >= original_score * args.masking_threshold:
												_snake_case						:					List[Any]    =   new_head_mask.clone().detach()  # save current head mask
												# heads from least important to most - keep only not-masked heads
												_snake_case						:					Dict    =   float("Inf"						)
												_snake_case						:					Union[str, Any]    =   head_importance.view(-1						).sort()[1]

												if len(_snake_case						) <= num_to_mask:
																		print("BREAK BY num_to_mask"						)
																		break

												# mask heads
												_snake_case						:					Optional[int]    =   current_heads_to_mask[:num_to_mask]
												logger.info("Heads to mask: %s" ,      str(current_heads_to_mask.tolist()						)						)
												_snake_case						:					List[str]    =   new_head_mask.view(-1						)
												_snake_case						:					Any    =   0.0
												_snake_case						:					List[Any]    =   new_head_mask.view_as(_snake_case						)
												_snake_case						:					List[Any]    =   new_head_mask.clone().detach()
												print_ad_tensor(_snake_case						)

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

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

						return head_mask
def  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase						)	->	Any:

						'''simple docstring'''



						_snake_case						:					Any    =   datetime.now()
						_snake_case						:					str    =   compute_heads_importance(
						    _snake_case ,      _snake_case ,      _snake_case ,      compute_entropy=_snake_case ,      compute_importance=_snake_case ,      head_mask=_snake_case						)
						_snake_case						:					Optional[int]    =   1 / loss
						_snake_case						:					int    =   datetime.now() - before_time

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

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

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

						_snake_case						:					Any    =   datetime.now()
						_snake_case						:					List[Any]    =   compute_heads_importance(
						    _snake_case ,      _snake_case ,      _snake_case ,      compute_entropy=_snake_case ,      compute_importance=_snake_case ,      head_mask=_snake_case ,      actually_pruned=_snake_case ,      )

						_snake_case						:					Tuple    =   1 / loss
						_snake_case						:					Union[str, Any]    =   datetime.now() - before_time

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

						'''simple docstring'''



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

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

						parser.add_argument(
						    "--max_seq_length" ,      default=128 ,      type=_snake_case ,      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=_snake_case ,      help="Batch size."						)

						parser.add_argument("--seed" ,      type=_snake_case ,      default=42						)
						parser.add_argument("--local_rank" ,      type=_snake_case ,      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=_snake_case ,      default="" ,      help="Can be used for distant debugging."						)
						parser.add_argument("--server_port" ,      type=_snake_case ,      default="" ,      help="Can be used for distant debugging."						)
						_snake_case						:					Tuple    =   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=_snake_case						)
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												_snake_case						:					Dict    =   torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"						)
												_snake_case						:					List[str]    =   0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank						)
												_snake_case						:					Optional[Any]    =   torch.device("cuda" ,      args.local_rank						)
												_snake_case						:					str    =   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						:					Optional[int]    =   GPTaLMHeadModel.from_pretrained(args.model_name_or_path						)

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

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

						# Prepare dataset
						_snake_case						:					int    =   np.concatenate(
						    [
						        np.loadtxt(args.data_dir ,      dtype=np.intaa						),
						    ]						)
						_snake_case						:					List[Any]    =   (torch.from_numpy(_snake_case						),)
						_snake_case						:					Optional[int]    =   TensorDataset(*_snake_case						)
						_snake_case						:					Any    =   RandomSampler(_snake_case						)
						_snake_case						:					str    =   DataLoader(_snake_case ,      sampler=_snake_case ,      batch_size=args.batch_size						)

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

						# 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						:					str    =   mask_heads(_snake_case ,      _snake_case ,      _snake_case						)
												prune_heads(_snake_case ,      _snake_case ,      _snake_case ,      _snake_case						)


if __name__ == "__main__":
				main()
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class        lowercase_   (					unittest.TestCase		):



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   "laion/clap-htsat-unfused"
													_snake_case						:					Dict    =   tempfile.mkdtemp()



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return RobertaTokenizer.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													return ClapFeatureExtractor.from_pretrained(self.checkpoint				,    **lowercase_    )



							def 	UpperCamelCase	(						self    ):
													shutil.rmtree(self.tmpdirname    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[int]    =   self.get_tokenizer()
													_snake_case						:					List[Any]    =   self.get_feature_extractor()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													processor.save_pretrained(self.tmpdirname    )
													_snake_case						:					Tuple    =   ClapProcessor.from_pretrained(self.tmpdirname    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   ClapProcessor(tokenizer=self.get_tokenizer()				,    feature_extractor=self.get_feature_extractor()    )
													processor.save_pretrained(self.tmpdirname    )

													_snake_case						:					Any    =   self.get_tokenizer(bos_token="(BOS)"				,    eos_token="(EOS)"    )
													_snake_case						:					List[Any]    =   self.get_feature_extractor(do_normalize=lowercase_				,    padding_value=1.0    )

													_snake_case						:					List[Any]    =   ClapProcessor.from_pretrained(
													    self.tmpdirname				,    bos_token="(BOS)"				,    eos_token="(EOS)"				,    do_normalize=lowercase_				,    padding_value=1.0    )

													self.assertEqual(processor.tokenizer.get_vocab()				,    tokenizer_add_kwargs.get_vocab()    )
													self.assertIsInstance(processor.tokenizer				,    lowercase_    )

													self.assertEqual(processor.feature_extractor.to_json_string()				,    feature_extractor_add_kwargs.to_json_string()    )
													self.assertIsInstance(processor.feature_extractor				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.get_feature_extractor()
													_snake_case						:					Union[str, Any]    =   self.get_tokenizer()

													_snake_case						:					Optional[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					List[str]    =   floats_list((3, 1_000)    )

													_snake_case						:					Union[str, Any]    =   feature_extractor(lowercase_				,    return_tensors="np"    )
													_snake_case						:					Any    =   processor(audios=lowercase_				,    return_tensors="np"    )

													for key in input_feat_extract.keys():
																			self.assertAlmostEqual(input_feat_extract[key].sum()				,    input_processor[key].sum()				,    delta=1e-2    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					str    =   self.get_feature_extractor()
													_snake_case						:					Optional[Any]    =   self.get_tokenizer()

													_snake_case						:					Dict    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Any    =   "This is a test string"

													_snake_case						:					Optional[Any]    =   processor(text=lowercase_    )

													_snake_case						:					Optional[Any]    =   tokenizer(lowercase_    )

													for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key]				,    encoded_processor[key]    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   self.get_feature_extractor()
													_snake_case						:					Dict    =   self.get_tokenizer()

													_snake_case						:					List[Any]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													_snake_case						:					Dict    =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

													_snake_case						:					List[Any]    =   processor.batch_decode(lowercase_    )
													_snake_case						:					Optional[int]    =   tokenizer.batch_decode(lowercase_    )

													self.assertListEqual(lowercase_				,    lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					List[str]    =   self.get_feature_extractor()
													_snake_case						:					str    =   self.get_tokenizer()

													_snake_case						:					Optional[int]    =   ClapProcessor(tokenizer=lowercase_				,    feature_extractor=lowercase_    )

													self.assertListEqual(
													    processor.model_input_names[2:]				,    feature_extractor.model_input_names				,    msg="`processor` and `feature_extractor` model input names do not match"				,    )