



'''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    (__A    ,      __A):



     """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    (__A    ,      __A=False):



     """simple docstring"""



     _a           =    2
     if unlogit:
          _a           =    torch.pow(__A    ,      __A)
     _a           =    p * torch.log(__A)
     _a           =    0
     return -plogp.sum(dim=-1)




def lowerCAmelCase    (__A):



     """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    (__A    ,      __A    ,      __A    ,      __A=True    ,      __A=True    ,      __A=None    ,      __A=False):



     """simple docstring"""



     _a    ,       _a           =    model.config.num_hidden_layers, model.config.num_attention_heads
     _a           =    torch.zeros(__A    ,      __A).to(args.device)
     _a           =    torch.zeros(__A    ,      __A).to(args.device)

     if head_mask is None:
          _a           =    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:
          _a           =    None

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

          # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
          _a           =    model(__A    ,      labels=__A    ,      head_mask=__A)
          #  (loss), lm_logits, presents, (all hidden_states), (attentions)
          _a    ,       _a    ,       _a           =    (
              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):
                    _a           =    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:
          _a           =    2
          _a           =    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:
          _a           =    (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''')
     _a           =    torch.zeros(head_importance.numel()    ,      dtype=torch.long    ,      device=args.device)
     _a           =    torch.arange(
         head_importance.numel()    ,      device=args.device)
     _a           =    head_ranks.view_as(__A)
     print_ad_tensor(__A)
     return attn_entropy, head_importance, total_loss




def lowerCAmelCase    (__A    ,      __A    ,      __A):



     """simple docstring"""



     _a    ,       _a    ,       _a           =    compute_heads_importance(__A    ,      __A    ,      __A    ,      compute_entropy=__A)
     _a           =    1 / loss  # instead of downsteam score use the LM loss
     logger.info('''Pruning: original score: %f, threshold: %f'''    ,      __A    ,      original_score * args.masking_threshold)

     _a           =    torch.ones_like(__A)
     _a           =    max(1    ,      int(new_head_mask.numel() * args.masking_amount))

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

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

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

          # Compute metric and head importance again
          _a    ,       _a    ,       _a           =    compute_heads_importance(
              __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      head_mask=__A)
          _a           =    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    (__A    ,      __A    ,      __A    ,      __A):



     """simple docstring"""



     _a           =    datetime.now()
     _a    ,       _a    ,       _a           =    compute_heads_importance(
         __A    ,      __A    ,      __A    ,      compute_entropy=__A    ,      compute_importance=__A    ,      head_mask=__A)
     _a           =    1 / loss
     _a           =    datetime.now() - before_time

     _a           =    sum(p.numel() for p in model.parameters())
     _a           =    {
         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):
               _a           =    [
                   v,
               ]

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

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

     _a           =    1 / loss
     _a           =    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    ():



     """simple docstring"""



     _a           =    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.''')
     _a           =    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:
          _a           =    torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''')
          _a           =    0 if args.no_cuda else torch.cuda.device_count()
     else:
          torch.cuda.set_device(args.local_rank)
          _a           =    torch.device('''cuda'''    ,      args.local_rank)
          _a           =    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           =    GPTaLMHeadModel.from_pretrained(args.model_name_or_path)

     # Distributed and parallel training
     model.to(args.device)
     if args.local_rank != -1:
          _a           =    nn.parallel.DistributedDataParallel(
              __A    ,      device_ids=[args.local_rank]    ,      output_device=args.local_rank    ,      find_unused_parameters=__A)
     elif args.n_gpu > 1:
          _a           =    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
     _a           =    np.concatenate(
         [
             np.loadtxt(args.data_dir    ,      dtype=np.intaa),
         ])
     _a           =    (torch.from_numpy(__A),)
     _a           =    TensorDataset(*__A)
     _a           =    RandomSampler(__A)
     _a           =    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:
          _a           =    mask_heads(__A    ,      __A    ,      __A)
          prune_heads(__A    ,      __A    ,      __A    ,      __A)


if __name__ == "__main__":
  main()




"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase								=		logging.get_logger(__name__)

_UpperCamelCase								=		{
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}
class 						__UpperCAmelCase    (__A     ):







			'''simple docstring'''
			_UpperCamelCase  :						Dict					     =				'glpn'





			def __init__(			self   ,      snake_case_=3   ,      snake_case_=4   ,      snake_case_=[2, 2, 2, 2]   ,      snake_case_=[8, 4, 2, 1]   ,      snake_case_=[32, 64, 160, 256]   ,      snake_case_=[7, 3, 3, 3]   ,      snake_case_=[4, 2, 2, 2]   ,      snake_case_=[1, 2, 5, 8]   ,      snake_case_=[4, 4, 4, 4]   ,      snake_case_="gelu"   ,      snake_case_=0.0   ,      snake_case_=0.0   ,      snake_case_=0.02   ,      snake_case_=0.1   ,      snake_case_=1E-6   ,      snake_case_=64   ,      snake_case_=10   ,      snake_case_=-1   ,      **snake_case_   ,      ):







										'''simple docstring'''


										super().__init__(**snake_case_							)

										A__       :     str								=			num_channels
										A__       :     List[Any]								=			num_encoder_blocks
										A__       :     List[str]								=			depths
										A__       :     int								=			sr_ratios
										A__       :     Optional[Any]								=			hidden_sizes
										A__       :     int								=			patch_sizes
										A__       :     str								=			strides
										A__       :     int								=			mlp_ratios
										A__       :     Optional[int]								=			num_attention_heads
										A__       :     Optional[Any]								=			hidden_act
										A__       :     Optional[Any]								=			hidden_dropout_prob
										A__       :     Optional[Any]								=			attention_probs_dropout_prob
										A__       :     List[str]								=			initializer_range
										A__       :     Dict								=			drop_path_rate
										A__       :     Any								=			layer_norm_eps
										A__       :     Any								=			decoder_hidden_size
										A__       :     Optional[Any]								=			max_depth
										A__       :     List[Any]								=			head_in_index





