

'''simple docstring'''





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase = None    ,__UpperCamelCase = None    ,__UpperCamelCase = None    ,)       ->			Optional[Any]:
					if config_name_or_path is None:
										lowerCamelCase_  				=      'facebook/rag-token-base' if model_type == 'rag_token' else 'facebook/rag-sequence-base'

					if generator_tokenizer_name_or_path is None:
										lowerCamelCase_  				=      generator_name_or_path

					if question_encoder_tokenizer_name_or_path is None:
										lowerCamelCase_  				=      question_encoder_name_or_path

					lowerCamelCase_  				=      RagTokenForGeneration if model_type == 'rag_token' else RagSequenceForGeneration

					# Save model.
					lowerCamelCase_  				=      RagConfig.from_pretrained(__UpperCamelCase	)
					lowerCamelCase_  				=      AutoConfig.from_pretrained(__UpperCamelCase	)
					lowerCamelCase_  				=      AutoConfig.from_pretrained(__UpperCamelCase	)

					lowerCamelCase_  				=      gen_config
					lowerCamelCase_  				=      question_encoder_config

					lowerCamelCase_  				=      model_class.from_pretrained_question_encoder_generator(
					    __UpperCamelCase    ,__UpperCamelCase    ,config=__UpperCamelCase	)
					rag_model.save_pretrained(__UpperCamelCase	)

					# Sanity check.
					model_class.from_pretrained(__UpperCamelCase	)

					# Save tokenizers.
					lowerCamelCase_  				=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
					gen_tokenizer.save_pretrained(dest_dir / 'generator_tokenizer/'	)
					lowerCamelCase_  				=      AutoTokenizer.from_pretrained(__UpperCamelCase	)
					question_encoder_tokenizer.save_pretrained(dest_dir / 'question_encoder_tokenizer/'	)


if __name__ == "__main__":
				A_	= argparse.ArgumentParser()
				parser.add_argument(
				    "--model_type",
				    choices=["rag_sequence", "rag_token"],
				    required=True,
				    type=str,
				    help="RAG model type: rag_sequence, rag_token",
				)
				parser.add_argument("--dest", type=str, required=True, help="Path to the output checkpoint directory.")
				parser.add_argument("--generator_name_or_path", type=str, required=True, help="Generator model identifier")
				parser.add_argument(
				    "--question_encoder_name_or_path", type=str, required=True, help="Question encoder model identifier"
				)

				parser.add_argument(
				    "--generator_tokenizer_name_or_path",
				    type=str,
				    help="Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``",
				)
				parser.add_argument(
				    "--question_encoder_tokenizer_name_or_path",
				    type=str,
				    help="Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``",
				)
				parser.add_argument(
				    "--config_name_or_path",
				    type=str,
				    help=(
				        "Identifier of the model config to use, if not provided, resolves to a base config for a given"
				        " ``model_type``"
				    ),
				)

				A_	= parser.parse_args()

				A_	= Path(args.dest)
				dest_dir.mkdir(exist_ok=True)

				consolidate(
				    args.model_type,
				    args.generator_name_or_path,
				    args.question_encoder_name_or_path,
				    dest_dir,
				    args.config_name_or_path,
				    args.generator_tokenizer_name_or_path,
				    args.question_encoder_tokenizer_name_or_path,
				)






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   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







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







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=False   ):
      '''simple docstring'''







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







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      logger.info('''lv, h >\t''' + '''\t'''.join(f"{x + 1}" for x in range(len(SCREAMING_SNAKE_CASE   )   )   )   )
      for row in range(len(SCREAMING_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   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=False   ):
      '''simple docstring'''







      A_       ,A_							       =		model.config.num_hidden_layers, model.config.num_attention_heads
      A_							       =		torch.zeros(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).to(args.device   )
      A_							       =		torch.zeros(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).to(args.device   )

      if head_mask is None:
            A_							       =		torch.ones(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).to(args.device   )

      head_mask.requires_grad_(requires_grad=SCREAMING_SNAKE_CASE   )
      # 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(SCREAMING_SNAKE_CASE		,				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(SCREAMING_SNAKE_CASE		,				labels=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE   )
            #  (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(SCREAMING_SNAKE_CASE   ):
                        A_							       =		entropy(attn.detach()		,				SCREAMING_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(SCREAMING_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:
            A_							       =		2
            A_							       =		torch.pow(torch.pow(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ).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(SCREAMING_SNAKE_CASE   )
      if compute_importance:
            logger.info('''Head importance scores'''   )
            print_ad_tensor(SCREAMING_SNAKE_CASE   )
      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(SCREAMING_SNAKE_CASE   )
      print_ad_tensor(SCREAMING_SNAKE_CASE   )
      return attn_entropy, head_importance, total_loss







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







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

      A_							       =		torch.ones_like(SCREAMING_SNAKE_CASE   )
      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(SCREAMING_SNAKE_CASE   ) <= 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(SCREAMING_SNAKE_CASE   )
            A_							       =		new_head_mask.clone().detach()
            print_ad_tensor(SCREAMING_SNAKE_CASE   )

            # Compute metric and head importance again
            A_       ,A_       ,A_							       =		compute_heads_importance(
                SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				compute_entropy=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE   )
            A_							       =		1 / loss
            logger.info(
                '''Masking: current score: %f, remaining heads %d (%.1f percents)'''		,				SCREAMING_SNAKE_CASE		,				new_head_mask.sum()		,				new_head_mask.sum() / new_head_mask.numel() * 100		,				)

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

      return head_mask







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		datetime.now()
      A_       ,A_       ,A_							       =		compute_heads_importance(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				compute_entropy=SCREAMING_SNAKE_CASE		,				compute_importance=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE   )
      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(SCREAMING_SNAKE_CASE   )   )
      }

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

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

      A_							       =		datetime.now()
      A_       ,A_       ,A_							       =		compute_heads_importance(
          SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				compute_entropy=SCREAMING_SNAKE_CASE		,				compute_importance=SCREAMING_SNAKE_CASE		,				head_mask=SCREAMING_SNAKE_CASE		,				actually_pruned=SCREAMING_SNAKE_CASE		,				)

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

      logger.info(
          '''Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)'''		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				pruned_num_params / original_num_params * 100		,				)
      logger.info('''Pruning: score with masking: %f score with pruning: %f'''		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
      logger.info('''Pruning: speed ratio (original timing / new timing): %f percents'''		,				original_time / new_time * 100   )
      save_model(SCREAMING_SNAKE_CASE		,				args.output_dir   )







def 							_lowerCamelCase   (			):
      '''simple docstring'''







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

      # Other parameters
      parser.add_argument(
          '''--config_name'''		,				default=''''''		,				type=SCREAMING_SNAKE_CASE		,				help='''Pretrained config name or path if not the same as model_name_or_path'''		,				)
      parser.add_argument(
          '''--tokenizer_name'''		,				default=''''''		,				type=SCREAMING_SNAKE_CASE		,				help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''		,				)
      parser.add_argument(
          '''--cache_dir'''		,				default=SCREAMING_SNAKE_CASE		,				type=SCREAMING_SNAKE_CASE		,				help='''Where do you want to store the pre-trained models downloaded from s3'''		,				)
      parser.add_argument(
          '''--data_subset'''		,				type=SCREAMING_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=SCREAMING_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=SCREAMING_SNAKE_CASE		,				help='''Amount to heads to masking at each masking step.'''   )
      parser.add_argument('''--metric_name'''		,				default='''acc'''		,				type=SCREAMING_SNAKE_CASE		,				help='''Metric to use for head masking.'''   )

      parser.add_argument(
          '''--max_seq_length'''		,				default=128		,				type=SCREAMING_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=SCREAMING_SNAKE_CASE		,				help='''Batch size.'''   )

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

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

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

      # Compute head entropy and importance score
      compute_heads_importance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_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:
            A_							       =		mask_heads(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )
            prune_heads(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
      main()





