




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							(       __UpperCamelCase		, __UpperCamelCase)      ->					Tuple:
     # save results
     if os.path.exists(__UpperCamelCase):
          if os.path.exists(os.path.join(__UpperCamelCase		, "config.json")) and os.path.isfile(
              os.path.join(__UpperCamelCase		, "config.json")):
               os.remove(os.path.join(__UpperCamelCase		, "config.json"))
          if os.path.exists(os.path.join(__UpperCamelCase		, "pytorch_model.bin")) and os.path.isfile(
              os.path.join(__UpperCamelCase		, "pytorch_model.bin")):
               os.remove(os.path.join(__UpperCamelCase		, "pytorch_model.bin"))
     else:
          os.makedirs(__UpperCamelCase)
     model.save_pretrained(__UpperCamelCase)

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase=False)      ->					Optional[int]:
     a     =				2
     if unlogit:
          a     =				torch.pow(__UpperCamelCase		, __UpperCamelCase)
     a     =				p * torch.log(__UpperCamelCase)
     a     =				0
     return -plogp.sum(dim=-1)

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

     if head_mask is None:
          a     =				torch.ones(__UpperCamelCase		, __UpperCamelCase).to(args.device)

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

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

     a     =				torch.ones_like(__UpperCamelCase)
     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(__UpperCamelCase) <= 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(__UpperCamelCase)
          a     =				new_head_mask.clone().detach()
          print_ad_tensor(__UpperCamelCase)

          # Compute metric and head importance again
          a  ,				a  ,				a     =				compute_heads_importance(
              __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, compute_entropy=__UpperCamelCase		, head_mask=__UpperCamelCase)
          a     =				1 / loss
          logger.info(
              "Masking: current score: %f, remaining heads %d (%.1f percents)"		, __UpperCamelCase		, new_head_mask.sum()		, new_head_mask.sum() / new_head_mask.numel() * 1_00		, )

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

     return head_mask

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[int]:
     a     =				datetime.now()
     a  ,				a  ,				a     =				compute_heads_importance(
         __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, compute_entropy=__UpperCamelCase		, compute_importance=__UpperCamelCase		, head_mask=__UpperCamelCase)
     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(__UpperCamelCase))
     }

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

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

     a     =				datetime.now()
     a  ,				a  ,				a     =				compute_heads_importance(
         __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, compute_entropy=__UpperCamelCase		, compute_importance=__UpperCamelCase		, head_mask=__UpperCamelCase		, actually_pruned=__UpperCamelCase		, )

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

     logger.info(
         "Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)"		, __UpperCamelCase		, __UpperCamelCase		, pruned_num_params / original_num_params * 1_00		, )
     logger.info("Pruning: score with masking: %f score with pruning: %f"		, __UpperCamelCase		, __UpperCamelCase)
     logger.info("Pruning: speed ratio (original timing / new timing): %f percents"		, original_time / new_time * 1_00)
     save_model(__UpperCamelCase		, args.output_dir)

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

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

     parser.add_argument(
         "--max_seq_length"		, default=1_28		, type=__UpperCamelCase		, 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=__UpperCamelCase		, help="Batch size.")

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

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

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

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

     # 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(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)
          prune_heads(__UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)


if __name__ == "__main__":
   main()







# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

lowercase__				:      str	   = TypeVar("T")




class       a__	( Generic[T]				):







     def __init__(     self  ,		A = True			)  ->   None:



          '''simple docstring'''



          a     =				{}  # dictionary of lists
          a     =				directed







     def       lowerCAmelCase_    (     self  ,		A  ,		A			)  ->   GraphAdjacencyList[T]:



          '''simple docstring'''



          if not self.directed:  # For undirected graphs
               # if both source vertex and destination vertex are both present in the
               # adjacency list, add destination vertex to source vertex list of adjacent
               # vertices and add source vertex to destination vertex list of adjacent
               # vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    self.adj_list[destination_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination vertex
               # to source vertex list of adjacent vertices, then create a new vertex with
               # destination vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[source_vertex]
               # if only destination vertex is present in adjacency list, add source vertex
               # to destination vertex list of adjacent vertices, then create a new vertex
               # with source vertex as key and assign a list containing the source vertex
               # as it's first adjacent vertex.
               elif destination_vertex in self.adj_list:
                    self.adj_list[destination_vertex].append(A			)
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and assign a list
               # containing the destination vertex as it's first adjacent vertex also
               # create a new vertex with destination vertex as key and assign a list
               # containing the source vertex as it's first adjacent vertex.
               else:
                    a     =				[destination_vertex]
                    a     =				[source_vertex]
          else:  # For directed graphs
               # if both source vertex and destination vertex are present in adjacency
               # list, add destination vertex to source vertex list of adjacent vertices.
               if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
               # if only source vertex is present in adjacency list, add destination
               # vertex to source vertex list of adjacent vertices and create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               elif source_vertex in self.adj_list:
                    self.adj_list[source_vertex].append(A			)
                    a     =				[]
               # if only destination vertex is present in adjacency list, create a new
               # vertex with source vertex as key and assign a list containing destination
               # vertex as first adjacent vertex
               elif destination_vertex in self.adj_list:
                    a     =				[destination_vertex]
               # if both source vertex and destination vertex are not present in adjacency
               # list, create a new vertex with source vertex as key and a list containing
               # destination vertex as it's first adjacent vertex. Then create a new vertex
               # with destination vertex as key, which has no adjacent vertex
               else:
                    a     =				[destination_vertex]
                    a     =				[]

          return self







     def __repr__(     self			)  ->   str:



          '''simple docstring'''



          return pformat(self.adj_list			)

