





'''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


a   	=    logging.getLogger(__name__)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       str:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=True				,						__UpperCAmelCase=True				,						__UpperCAmelCase=None				,						__UpperCAmelCase=False   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							model.config.num_hidden_layers, model.config.num_attention_heads
   __SCREAMING_SNAKE_CASE   				=							torch.zeros(__UpperCAmelCase				,						__UpperCAmelCase   ).to(args.device   )
   __SCREAMING_SNAKE_CASE   				=							torch.zeros(__UpperCAmelCase				,						__UpperCAmelCase   ).to(args.device   )

   if head_mask is None:
      __SCREAMING_SNAKE_CASE   				=							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:
      __SCREAMING_SNAKE_CASE   				=							None

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

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

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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







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

   __SCREAMING_SNAKE_CASE   				=							torch.ones_like(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							max(1				,						int(new_head_mask.numel() * args.masking_amount   )   )

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

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

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

      # Compute metric and head importance again
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							compute_heads_importance(
          __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						compute_entropy=__UpperCAmelCase				,						head_mask=__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							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() * 100				,						)

   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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







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

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

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

   __SCREAMING_SNAKE_CASE   				=							datetime.now()
   __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							compute_heads_importance(
       __UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						compute_entropy=__UpperCAmelCase				,						compute_importance=__UpperCAmelCase				,						head_mask=__UpperCAmelCase				,						actually_pruned=__UpperCAmelCase				,						)

   __SCREAMING_SNAKE_CASE   				=							1 / loss
   __SCREAMING_SNAKE_CASE   				=							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 * 100				,						)
   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 * 100   )
   save_model(__UpperCAmelCase				,						args.output_dir   )

def     __magic_name__							(				)    ->       int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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=128				,						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."""   )
   __SCREAMING_SNAKE_CASE   				=							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:
      __SCREAMING_SNAKE_CASE   				=							torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""   )
      __SCREAMING_SNAKE_CASE   				=							0 if args.no_cuda else torch.cuda.device_count()
   else:
      torch.cuda.set_device(args.local_rank   )
      __SCREAMING_SNAKE_CASE   				=							torch.device("""cuda"""				,						args.local_rank   )
      __SCREAMING_SNAKE_CASE   				=							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   )   )   )

   __SCREAMING_SNAKE_CASE   				=							GPTaLMHeadModel.from_pretrained(args.model_name_or_path   )

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


if __name__ == "__main__":
    main()











'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase  =			{
    """configuration_groupvit""": [
        """GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """GroupViTConfig""",
        """GroupViTOnnxConfig""",
        """GroupViTTextConfig""",
        """GroupViTVisionConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GroupViTModel""",
	    """GroupViTPreTrainedModel""",
	    """GroupViTTextModel""",
	    """GroupViTVisionModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFGroupViTModel""",
	    """TFGroupViTPreTrainedModel""",
	    """TFGroupViTTextModel""",
	    """TFGroupViTVisionModel""",
	]

if TYPE_CHECKING:
	from .configuration_groupvit import (
	    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    GroupViTConfig,
	    GroupViTOnnxConfig,
	    GroupViTTextConfig,
	    GroupViTVisionConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_groupvit import (
		    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GroupViTModel,
		    GroupViTPreTrainedModel,
		    GroupViTTextModel,
		    GroupViTVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_groupvit import (
		    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFGroupViTModel,
		    TFGroupViTPreTrainedModel,
		    TFGroupViTTextModel,
		    TFGroupViTVisionModel,
		)

else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

