






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__				     =			logging.getLogger(__name__)

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       int				):
			# save results
			if os.path.exists(lowerCamelCase_				):
						if os.path.exists(os.path.join(lowerCamelCase_   ,  'config.json'				)				) and os.path.isfile(
						    os.path.join(lowerCamelCase_   ,  'config.json'				)				):
									os.remove(os.path.join(lowerCamelCase_   ,  'config.json'				)				)
						if os.path.exists(os.path.join(lowerCamelCase_   ,  'pytorch_model.bin'				)				) and os.path.isfile(
						    os.path.join(lowerCamelCase_   ,  'pytorch_model.bin'				)				):
									os.remove(os.path.join(lowerCamelCase_   ,  'pytorch_model.bin'				)				)
			else:
						os.makedirs(lowerCamelCase_				)
			model.save_pretrained(lowerCamelCase_				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       int   ,  lowerCamelCase_       :       Any=False				):
			__a						:  Dict									=     2
			if unlogit:
						__a						:  Optional[Any]									=     torch.pow(lowerCamelCase_   ,  lowerCamelCase_				)
			__a						:  Any									=     p * torch.log(lowerCamelCase_				)
			__a						:  Union[str, Any]									=     0
			return -plogp.sum(dim=-1				)

def        UpperCAmelCase__		(						lowerCamelCase_       :       Any				):
			logger.info('lv, h >\t' + '\t'.join(f'''{x + 1}''' for x in range(len(lowerCamelCase_				)				)				)				)
			for row in range(len(lowerCamelCase_				)				):
						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        UpperCAmelCase__		(						lowerCamelCase_       :       Optional[int]   ,  lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       int   ,  lowerCamelCase_       :       int=True   ,  lowerCamelCase_       :       Optional[Any]=True   ,  lowerCamelCase_       :       List[Any]=None   ,  lowerCamelCase_       :       List[Any]=False				):
			__a						,					__a						:  Optional[int]									=     model.config.num_hidden_layers, model.config.num_attention_heads
			__a						:  str									=     torch.zeros(lowerCamelCase_   ,  lowerCamelCase_				).to(args.device				)
			__a						:  int									=     torch.zeros(lowerCamelCase_   ,  lowerCamelCase_				).to(args.device				)

			if head_mask is None:
						__a						:  Union[str, Any]									=     torch.ones(lowerCamelCase_   ,  lowerCamelCase_				).to(args.device				)

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

			__a						:  Optional[int]									=     0.0
			__a						:  Optional[Any]									=     0.0
			for step, inputs in enumerate(tqdm(lowerCamelCase_   ,  desc='Iteration'   ,  disable=args.local_rank not in [-1, 0]				)				):
						__a						:  Dict									=     tuple(t.to(args.device				) for t in inputs				)
						((__a)						,					)						:  Dict									=     inputs

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						__a						:  List[Any]									=     model(lowerCamelCase_   ,  labels=lowerCamelCase_   ,  head_mask=lowerCamelCase_				)
						#  (loss), lm_logits, presents, (all hidden_states), (attentions)
						__a						,					__a						,					__a						:  int									=     (
						    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(lowerCamelCase_				):
												__a						:  List[str]									=     entropy(attn.detach()   ,  lowerCamelCase_				)
												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(lowerCamelCase_				).float().detach().sum().data

			# Normalize
			attn_entropy /= tot_tokens
			head_importance /= tot_tokens
			# Layerwise importance normalization
			if not args.dont_normalize_importance_by_layer:
						__a						:  Optional[Any]									=     2
						__a						:  Union[str, Any]									=     torch.pow(torch.pow(lowerCamelCase_   ,  lowerCamelCase_				).sum(-1				)   ,  1 / exponent				)
						head_importance /= norm_by_layer.unsqueeze(-1				) + 1e-20

			if not args.dont_normalize_global_importance:
						__a						:  List[str]									=     (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

			# Print matrices
			if compute_entropy:
						logger.info('Attention entropies'				)
						print_ad_tensor(lowerCamelCase_				)
			if compute_importance:
						logger.info('Head importance scores'				)
						print_ad_tensor(lowerCamelCase_				)
			logger.info('Head ranked by importance scores'				)
			__a						:  Optional[Any]									=     torch.zeros(head_importance.numel()   ,  dtype=torch.long   ,  device=args.device				)
			__a						:  str									=     torch.arange(
			    head_importance.numel()   ,  device=args.device				)
			__a						:  Tuple									=     head_ranks.view_as(lowerCamelCase_				)
			print_ad_tensor(lowerCamelCase_				)
			return attn_entropy, head_importance, total_loss

def        UpperCAmelCase__		(						lowerCamelCase_       :       Union[str, Any]   ,  lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       int				):
			__a						,					__a						,					__a						:  Optional[int]									=     compute_heads_importance(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  compute_entropy=lowerCamelCase_				)
			__a						:  Tuple									=     1 / loss  # instead of downsteam score use the LM loss
			logger.info('Pruning: original score: %f, threshold: %f'   ,  lowerCamelCase_   ,  original_score * args.masking_threshold				)

			__a						:  Tuple									=     torch.ones_like(lowerCamelCase_				)
			__a						:  int									=     max(1   ,  int(new_head_mask.numel() * args.masking_amount				)				)

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

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

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

						# Compute metric and head importance again
						__a						,					__a						,					__a						:  int									=     compute_heads_importance(
						    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  compute_entropy=lowerCamelCase_   ,  head_mask=lowerCamelCase_				)
						__a						:  List[Any]									=     1 / loss
						logger.info(
						    'Masking: current score: %f, remaining heads %d (%.1f percents)'   ,  lowerCamelCase_   ,  new_head_mask.sum()   ,  new_head_mask.sum() / new_head_mask.numel() * 1_0_0   ,  )

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

			return head_mask

def        UpperCAmelCase__		(						lowerCamelCase_       :       Dict   ,  lowerCamelCase_       :       Tuple   ,  lowerCamelCase_       :       Any   ,  lowerCamelCase_       :       Union[str, Any]				):
			__a						:  List[Any]									=     datetime.now()
			__a						,					__a						,					__a						:  List[str]									=     compute_heads_importance(
			    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  compute_entropy=lowerCamelCase_   ,  compute_importance=lowerCamelCase_   ,  head_mask=lowerCamelCase_				)
			__a						:  List[str]									=     1 / loss
			__a						:  List[Any]									=     datetime.now() - before_time

			__a						:  List[str]									=     sum(p.numel() for p in model.parameters()				)
			__a						:  Dict									=     {
			    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowerCamelCase_				)				)
			}

			for k, v in heads_to_prune.items():
						if isinstance(lowerCamelCase_   ,  lowerCamelCase_				):
									__a						:  Tuple									=     [
									    v,
									]

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

			__a						:  Tuple									=     datetime.now()
			__a						,					__a						,					__a						:  Tuple									=     compute_heads_importance(
			    lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  compute_entropy=lowerCamelCase_   ,  compute_importance=lowerCamelCase_   ,  head_mask=lowerCamelCase_   ,  actually_pruned=lowerCamelCase_   ,  )

			__a						:  Optional[Any]									=     1 / loss
			__a						:  List[Any]									=     datetime.now() - before_time

			logger.info(
			    'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  pruned_num_params / original_num_params * 1_0_0   ,  )
			logger.info('Pruning: score with masking: %f score with pruning: %f'   ,  lowerCamelCase_   ,  lowerCamelCase_				)
			logger.info('Pruning: speed ratio (original timing / new timing): %f percents'   ,  original_time / new_time * 1_0_0				)
			save_model(lowerCamelCase_   ,  args.output_dir				)

def        UpperCAmelCase__		(						):
			__a						:  List[str]									=     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--data_dir'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  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=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='Path to pretrained model or model identifier from huggingface.co/models'   ,  )
			parser.add_argument(
			    '--output_dir'   ,  default=lowerCamelCase_   ,  type=lowerCamelCase_   ,  required=lowerCamelCase_   ,  help='The output directory where the model predictions and checkpoints will be written.'   ,  )

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

			parser.add_argument(
			    '--max_seq_length'   ,  default=1_2_8   ,  type=lowerCamelCase_   ,  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=lowerCamelCase_   ,  help='Batch size.'				)

			parser.add_argument('--seed'   ,  type=lowerCamelCase_   ,  default=4_2				)
			parser.add_argument('--local_rank'   ,  type=lowerCamelCase_   ,  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=lowerCamelCase_   ,  default=''   ,  help='Can be used for distant debugging.'				)
			parser.add_argument('--server_port'   ,  type=lowerCamelCase_   ,  default=''   ,  help='Can be used for distant debugging.'				)
			__a						:  Optional[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=lowerCamelCase_				)
						ptvsd.wait_for_attach()

			# Setup devices and distributed training
			if args.local_rank == -1 or args.no_cuda:
						__a						:  List[str]									=     torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'				)
						__a						:  Tuple									=     0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank				)
						__a						:  Union[str, Any]									=     torch.device('cuda'   ,  args.local_rank				)
						__a						:  Any									=     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				)				)				)

			__a						:  Optional[Any]									=     GPTaLMHeadModel.from_pretrained(args.model_name_or_path				)

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

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

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

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

			# 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:
						__a						:  Union[str, Any]									=     mask_heads(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)
						prune_heads(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)


if __name__ == "__main__":
							main()











'''simple docstring'''



class 							_a				:





       '''simple docstring'''






       def __init__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						val
             SCREAMING_SNAKE_CASE   :      Any    					=						None
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						None







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             if self.val:
                   if val < self.val:
                         if self.left is None:
                               SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(A  )
                         else:
                               self.left.insert(A  )
                   elif val > self.val:
                         if self.right is None:
                               SCREAMING_SNAKE_CASE   :      int    					=						Node(A  )
                         else:
                               self.right.insert(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      int    					=						val




def   lowercase__(			__UpperCamelCase:					Optional[int]      ,__UpperCamelCase:					List[str]    ):

      """simple docstring"""






      if root:
            inorder(root.left      ,__UpperCamelCase    )
            res.append(root.val    )
            inorder(root.right      ,__UpperCamelCase    )




def   lowercase__(			__UpperCamelCase:					List[Any]    ):

      """simple docstring"""






      if len(__UpperCamelCase    ) == 0:
            return arr
      SCREAMING_SNAKE_CASE   :      Optional[int]    					=						Node(arr[0]    )
      for i in range(1      ,len(__UpperCamelCase    )    ):
            root.insert(arr[i]    )
      # Traverse BST in order.
      SCREAMING_SNAKE_CASE   :      Dict    					=						[]
      inorder(__UpperCamelCase      ,__UpperCamelCase    )
      return res


if __name__ == "__main__":
      print(tree_sort([1_0, 1, 3, 2, 9, 1_4, 1_3]))


