'''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


SCREAMING_SNAKE_CASE_:					List[str]  =logging.getLogger(__name__)





def      lowerCAmelCase_    ( snake_case_	:			Optional[int] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[Any]:


     '''simple docstring'''




     if os.path.exists(snake_case_      ):
          if os.path.exists(os.path.join(snake_case_ ,   "config.json"      )      ) and os.path.isfile(
              os.path.join(snake_case_ ,   "config.json"      )      ):
               os.remove(os.path.join(snake_case_ ,   "config.json"      )      )
          if os.path.exists(os.path.join(snake_case_ ,   "pytorch_model.bin"      )      ) and os.path.isfile(
              os.path.join(snake_case_ ,   "pytorch_model.bin"      )      ):
               os.remove(os.path.join(snake_case_ ,   "pytorch_model.bin"      )      )
     else:
          os.makedirs(snake_case_      )
     model.save_pretrained(snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			List[Any]=False      )   ->				Tuple:


     '''simple docstring'''




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





def      lowerCAmelCase_    ( snake_case_	:			List[str]      )   ->				Union[str, Any]:


     '''simple docstring'''




     logger.info("lv, h >\t" + "\t".join(f"""{x + 1}""" for x in range(len(snake_case_      )      )      )      )
     for row in range(len(snake_case_      )      ):
          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_    ( snake_case_	:			Dict ,   snake_case_	:			Optional[int] ,   snake_case_	:			int ,   snake_case_	:			List[Any]=True ,   snake_case_	:			List[Any]=True ,   snake_case_	:			List[str]=None ,   snake_case_	:			int=False      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       model.config.num_hidden_layers, model.config.num_attention_heads
     UpperCAmelCase_	   =       torch.zeros(snake_case_ ,   snake_case_      ).to(args.device      )
     UpperCAmelCase_	   =       torch.zeros(snake_case_ ,   snake_case_      ).to(args.device      )

     if head_mask is None:
          UpperCAmelCase_	   =       torch.ones(snake_case_ ,   snake_case_      ).to(args.device      )

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





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Any ,   snake_case_	:			str      )   ->				int:


     '''simple docstring'''




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

     UpperCAmelCase_	   =       torch.ones_like(snake_case_      )
     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(snake_case_      ) <= 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(snake_case_      )
          UpperCAmelCase_	   =       new_head_mask.clone().detach()
          print_ad_tensor(snake_case_      )

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

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

     return head_mask





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Optional[Any] ,   snake_case_	:			Optional[Any] ,   snake_case_	:			int      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       datetime.now()
     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       compute_heads_importance(
         snake_case_ ,   snake_case_ ,   snake_case_ ,   compute_entropy=snake_case_ ,   compute_importance=snake_case_ ,   head_mask=snake_case_      )
     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(snake_case_      )      )
     }

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

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

     UpperCAmelCase_	   =       datetime.now()
     UpperCAmelCase_   ,			UpperCAmelCase_   ,			UpperCAmelCase_	   =       compute_heads_importance(
         snake_case_ ,   snake_case_ ,   snake_case_ ,   compute_entropy=snake_case_ ,   compute_importance=snake_case_ ,   head_mask=snake_case_ ,   actually_pruned=snake_case_ ,   )

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

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





def      lowerCAmelCase_    ( )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--data_dir" ,   default=snake_case_ ,   type=snake_case_ ,   required=snake_case_ ,   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=snake_case_ ,   type=snake_case_ ,   required=snake_case_ ,   help="Path to pretrained model or model identifier from huggingface.co/models" ,   )
     parser.add_argument(
         "--output_dir" ,   default=snake_case_ ,   type=snake_case_ ,   required=snake_case_ ,   help="The output directory where the model predictions and checkpoints will be written." ,   )

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

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

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

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

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

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

     # 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(snake_case_ ,   snake_case_ ,   snake_case_      )
          prune_heads(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )


if __name__ == "__main__":
    main()





'''simple docstring'''





import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 	__A				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

         # scale the initial noise by the standard deviation required by the scheduler
         UpperCAmelCase_	   =       latents * self.scheduler.init_noise_sigma

         self.scheduler.set_timesteps(__a			)

         # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
         UpperCAmelCase_	   =       "eta" in set(inspect.signature(self.scheduler.step			).parameters.keys()			)

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

         for t in self.progress_bar(self.scheduler.timesteps			):
              UpperCAmelCase_	   =       self.scheduler.scale_model_input(__a       ,      __a			)
              # predict the noise residual
              UpperCAmelCase_	   =       self.unet(__a       ,      __a			).sample
              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_	   =       self.scheduler.step(__a       ,      __a       ,      __a       ,      **__a			).prev_sample

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

         UpperCAmelCase_	   =       (image / 2 + 0.5).clamp(0       ,      1			)
         UpperCAmelCase_	   =       image.cpu().permute(0       ,      2       ,      3       ,      1			).numpy()
         if output_type == "pil":
              UpperCAmelCase_	   =       self.numpy_to_pil(__a			)

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




