






"""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__          = logging.getLogger(__name__)

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase       )						->    Any:

		"""simple docstring"""







		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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase=False       )						->    int:

		"""simple docstring"""







		lowerCAmelCase_   :							List[Any]									=			2
		if unlogit:
				lowerCAmelCase_   :							str									=			torch.pow(a_      ,       a_       )
		lowerCAmelCase_   :							Dict									=			p * torch.log(a_       )
		lowerCAmelCase_   :							Optional[int]									=			0
		return -plogp.sum(dim=-1       )

def 		__lowerCamelCase	(  __UpperCamelCase       )						->    Any:

		"""simple docstring"""







		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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase=True      ,       __UpperCamelCase=True      ,       __UpperCamelCase=None      ,       __UpperCamelCase=False       )						->    Optional[Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							str									=			model.config.num_hidden_layers, model.config.num_attention_heads
		lowerCAmelCase_   :							Any									=			torch.zeros(a_      ,       a_       ).to(args.device       )
		lowerCAmelCase_   :							Dict									=			torch.zeros(a_      ,       a_       ).to(args.device       )

		if head_mask is None:
				lowerCAmelCase_   :							Dict									=			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:
				lowerCAmelCase_   :							Dict									=			None

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

				# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
				lowerCAmelCase_   :							Dict									=			model(a_      ,       labels=a_      ,       head_mask=a_       )
				#  (loss), lm_logits, presents, (all hidden_states), (attentions)
				lowerCAmelCase_   :							Optional[Any]									=			(
				    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_       ):
								lowerCAmelCase_   :							Optional[int]									=			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:
				lowerCAmelCase_   :							Optional[int]									=			2
				lowerCAmelCase_   :							str									=			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:
				lowerCAmelCase_   :							List[Any]									=			(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"       )
		lowerCAmelCase_   :							Tuple									=			torch.zeros(head_importance.numel()      ,       dtype=torch.long      ,       device=args.device       )
		lowerCAmelCase_   :							int									=			torch.arange(
		    head_importance.numel()      ,       device=args.device       )
		lowerCAmelCase_   :							Dict									=			head_ranks.view_as(a_       )
		print_ad_tensor(a_       )
		return attn_entropy, head_importance, total_loss

def 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    Dict:

		"""simple docstring"""







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

		lowerCAmelCase_   :							Tuple									=			torch.ones_like(a_       )
		lowerCAmelCase_   :							List[Any]									=			max(1      ,       int(new_head_mask.numel() * args.masking_amount       )       )

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

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

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

				# Compute metric and head importance again
				lowerCAmelCase_   :							str									=			compute_heads_importance(
				    a_      ,       a_      ,       a_      ,       compute_entropy=a_      ,       head_mask=a_       )
				lowerCAmelCase_   :							Any									=			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 		__lowerCamelCase	(  __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase      ,       __UpperCamelCase       )						->    Dict:

		"""simple docstring"""







		lowerCAmelCase_   :							str									=			datetime.now()
		lowerCAmelCase_   :							Union[str, Any]									=			compute_heads_importance(
		    a_      ,       a_      ,       a_      ,       compute_entropy=a_      ,       compute_importance=a_      ,       head_mask=a_       )
		lowerCAmelCase_   :							Union[str, Any]									=			1 / loss
		lowerCAmelCase_   :							List[Any]									=			datetime.now() - before_time

		lowerCAmelCase_   :							Optional[int]									=			sum(p.numel() for p in model.parameters()       )
		lowerCAmelCase_   :							List[str]									=			{
		    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_       ):
						lowerCAmelCase_   :							Union[str, Any]									=			[
						    v,
						]

		assert sum(len(a_       ) for h in heads_to_prune.values()       ) == (1 - head_mask.long()).sum().item()
		model.prune_heads(a_       )
		lowerCAmelCase_   :							Optional[Any]									=			sum(p.numel() for p in model.parameters()       )

		lowerCAmelCase_   :							Optional[int]									=			datetime.now()
		lowerCAmelCase_   :							List[str]									=			compute_heads_importance(
		    a_      ,       a_      ,       a_      ,       compute_entropy=a_      ,       compute_importance=a_      ,       head_mask=a_      ,       actually_pruned=a_      ,       )

		lowerCAmelCase_   :							str									=			1 / loss
		lowerCAmelCase_   :							Dict									=			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 		__lowerCamelCase	(  )						->    Optional[Any]:

		"""simple docstring"""







		lowerCAmelCase_   :							List[str]									=			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."       )
		lowerCAmelCase_   :							Union[str, Any]									=			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:
				lowerCAmelCase_   :							Dict									=			torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"       )
				lowerCAmelCase_   :							Tuple									=			0 if args.no_cuda else torch.cuda.device_count()
		else:
				torch.cuda.set_device(args.local_rank       )
				lowerCAmelCase_   :							Dict									=			torch.device("cuda"      ,       args.local_rank       )
				lowerCAmelCase_   :							Tuple									=			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       )       )       )

		lowerCAmelCase_   :							List[Any]									=			GPTaLMHeadModel.from_pretrained(args.model_name_or_path       )

		# Distributed and parallel training
		model.to(args.device       )
		if args.local_rank != -1:
				lowerCAmelCase_   :							Tuple									=			nn.parallel.DistributedDataParallel(
				    a_      ,       device_ids=[args.local_rank]      ,       output_device=args.local_rank      ,       find_unused_parameters=a_       )
		elif args.n_gpu > 1:
				lowerCAmelCase_   :							str									=			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
		lowerCAmelCase_   :							List[str]									=			np.concatenate(
		    [
		        np.loadtxt(args.data_dir      ,       dtype=np.intaa       ),
		    ]       )
		lowerCAmelCase_   :							Union[str, Any]									=			(torch.from_numpy(a_       ),)
		lowerCAmelCase_   :							Optional[Any]									=			TensorDataset(*a_       )
		lowerCAmelCase_   :							Union[str, Any]									=			RandomSampler(a_       )
		lowerCAmelCase_   :							int									=			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:
				lowerCAmelCase_   :							Tuple									=			mask_heads(a_      ,       a_      ,       a_       )
				prune_heads(a_      ,       a_      ,       a_      ,       a_       )


if __name__ == "__main__":
					main()







"""simple docstring"""
from collections.abc import Callable

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()