





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


UpperCamelCase_												=	logging.getLogger(__name__)






def 				_UpperCAmelCase			(    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 				_UpperCAmelCase			(    A				,							A=False     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    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 				_UpperCAmelCase			(    A				,							A				,							A				,							A=True				,							A=True				,							A=None				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__			,							UpperCAmelCase__						=model.config.num_hidden_layers, model.config.num_attention_heads
   UpperCAmelCase__						=torch.zeros(A				,							A     ).to(args.device     )
   UpperCAmelCase__						=torch.zeros(A				,							A     ).to(args.device     )

   if head_mask is None:
      UpperCAmelCase__						=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:
      UpperCAmelCase__						=None

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

      # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
      UpperCAmelCase__						=model(A				,							labels=A				,							head_mask=A     )
      #  (loss), lm_logits, presents, (all hidden_states), (attentions)
      UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=(
          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     ):
            UpperCAmelCase__						=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:
      UpperCAmelCase__						=2
      UpperCAmelCase__						=torch.pow(torch.pow(A				,							A     ).sum(-1     )				,							1 / exponent     )
      head_importance /= norm_by_layer.unsqueeze(-1     ) + 1e-2_0

   if not args.dont_normalize_global_importance:
      UpperCAmelCase__						=(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"     )
   UpperCAmelCase__						=torch.zeros(head_importance.numel()				,							dtype=torch.long				,							device=args.device     )
   UpperCAmelCase__						=torch.arange(
       head_importance.numel()				,							device=args.device     )
   UpperCAmelCase__						=head_ranks.view_as(A     )
   print_ad_tensor(A     )
   return attn_entropy, head_importance, total_loss






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

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

   UpperCAmelCase__						=torch.ones_like(A     )
   UpperCAmelCase__						=max(1				,							int(new_head_mask.numel() * args.masking_amount     )     )

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

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

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

      # Compute metric and head importance again
      UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=compute_heads_importance(
          A				,							A				,							A				,							compute_entropy=A				,							head_mask=A     )
      UpperCAmelCase__						=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 				_UpperCAmelCase			(    A				,							A				,							A				,							A     ):




   '''simple docstring'''

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

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

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

   UpperCAmelCase__						=datetime.now()
   UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=compute_heads_importance(
       A				,							A				,							A				,							compute_entropy=A				,							compute_importance=A				,							head_mask=A				,							actually_pruned=A				,							)

   UpperCAmelCase__						=1 / loss
   UpperCAmelCase__						=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 				_UpperCAmelCase			(    ):




   '''simple docstring'''

   UpperCAmelCase__						=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."     )
   UpperCAmelCase__						=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:
      UpperCAmelCase__						=torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"     )
      UpperCAmelCase__						=0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank     )
      UpperCAmelCase__						=torch.device("cuda"				,							args.local_rank     )
      UpperCAmelCase__						=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     )     )     )

   UpperCAmelCase__						=GPTaLMHeadModel.from_pretrained(args.model_name_or_path     )

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


if __name__ == "__main__":
    main()










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=requests.get(
       F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,							headers={"User-agent": "A random string"}				,							)
   if response.status_code == 429:
      raise requests.HTTPError

   UpperCAmelCase__						=response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(A     )}

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          item: data["data"]["children"][id_]["data"][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))



