


from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase_				:			Dict   =	logging.get_logger(__name__)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      snake_case__    :					Any					       =						['''pixel_values''']
      def __init__(	self    :	Tuple					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							SCREAMING_SNAKE_CASE__    :	int = 3_2					,							SCREAMING_SNAKE_CASE__    :	Dict=PILImageResampling.BILINEAR					,							SCREAMING_SNAKE_CASE__    :	bool = True					,							**SCREAMING_SNAKE_CASE__    :	Any					,							)		->      None:
             a_ :		int							=  do_resize
             a_ :		Optional[int]							=  do_rescale
             a_ :		List[str]							=  size_divisor
             a_ :		Optional[int]							=  resample
             super().__init__(**SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Optional[ChannelDimension] = None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      np.ndarray:
             a_							,		a_ :		Any							=  get_image_size(SCREAMING_SNAKE_CASE__					)
             # Rounds the height and width down to the closest multiple of size_divisor
             a_ :		Optional[Any]							=  height // size_divisor * size_divisor
             a_ :		Dict							=  width // size_divisor * size_divisor
             a_ :		Tuple							=  resize(SCREAMING_SNAKE_CASE__					,							(new_h, new_w)					,							resample=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
             return image
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	np.ndarray					,							SCREAMING_SNAKE_CASE__    :	float					,							SCREAMING_SNAKE_CASE__    :	Optional[ChannelDimension] = None					,							**SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      np.ndarray:
             return rescale(image=SCREAMING_SNAKE_CASE__					,							scale=SCREAMING_SNAKE_CASE__					,							data_format=SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					,							SCREAMING_SNAKE_CASE__    :	Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[int] = None					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Optional[bool] = None					,							SCREAMING_SNAKE_CASE__    :	Optional[Union[TensorType, str]] = None					,							SCREAMING_SNAKE_CASE__    :	ChannelDimension = ChannelDimension.FIRST					,							**SCREAMING_SNAKE_CASE__    :	Tuple					,							)		->      BatchFeature:
             a_ :		Tuple							=  do_resize if do_resize is not None else self.do_resize
             a_ :		Any							=  do_rescale if do_rescale is not None else self.do_rescale
             a_ :		List[Any]							=  size_divisor if size_divisor is not None else self.size_divisor
             a_ :		int							=  resample if resample is not None else self.resample

             if do_resize and size_divisor is None:
                    raise ValueError('size_divisor is required for resizing'					)

             a_ :		Any							=  make_list_of_images(SCREAMING_SNAKE_CASE__					)

             if not valid_images(SCREAMING_SNAKE_CASE__					):
                    raise ValueError('Invalid image(s)'					)

             # All transformations expect numpy arrays.
             a_ :		Optional[Any]							=  [to_numpy_array(SCREAMING_SNAKE_CASE__					) for img in images]

             if do_resize:
                    a_ :		Optional[Any]							=  [self.resize(SCREAMING_SNAKE_CASE__					,							size_divisor=SCREAMING_SNAKE_CASE__					,							resample=SCREAMING_SNAKE_CASE__					) for image in images]

             if do_rescale:
                    a_ :		Union[str, Any]							=  [self.rescale(SCREAMING_SNAKE_CASE__					,							scale=1 / 2_5_5					) for image in images]

             a_ :		Dict							=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					) for image in images]

             a_ :		List[Any]							=  {'pixel_values': images}
             return BatchFeature(data=SCREAMING_SNAKE_CASE__					,							tensor_type=SCREAMING_SNAKE_CASE__					)






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_				:			str   =	logging.getLogger(__name__)







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Dict  ,   __A			:      List[str]    )     ->		Tuple:

       """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 	SCREAMING_SNAKE_CASE_						(      __A			:      str  ,   __A			:      Dict=False    )     ->		Any:

       """simple docstring"""

       a_ :		Optional[Any]							=  2
       if unlogit:
              a_ :		List[str]							=  torch.pow(__A  ,   __A    )
       a_ :		Tuple							=  p * torch.log(__A    )
       a_ :		Union[str, Any]							=  0
       return -plogp.sum(dim=-1    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Any    )     ->		Tuple:

       """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 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      Dict  ,   __A			:      Union[str, Any]  ,   __A			:      List[str]=True  ,   __A			:      str=True  ,   __A			:      int=None  ,   __A			:      List[str]=False    )     ->		List[Any]:

       """simple docstring"""

       a_							,		a_ :		List[str]							=  model.config.num_hidden_layers, model.config.num_attention_heads
       a_ :		Tuple							=  torch.zeros(__A  ,   __A    ).to(args.device    )
       a_ :		Optional[int]							=  torch.zeros(__A  ,   __A    ).to(args.device    )

       if head_mask is None:
              a_ :		Tuple							=  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:
              a_ :		List[str]							=  None

       a_ :		Optional[Any]							=  0.0
       a_ :		Optional[int]							=  0.0
       for step, inputs in enumerate(tqdm(__A  ,   desc='Iteration'  ,   disable=args.local_rank not in [-1, 0]    )    ):
              a_ :		Any							=  tuple(t.to(args.device    ) for t in inputs    )
              ((a_)							,		) :		Dict							=  inputs

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              a_ :		Tuple							=  model(__A  ,   labels=__A  ,   head_mask=__A    )
              #  (loss), lm_logits, presents, (all hidden_states), (attentions)
              a_							,		a_							,		a_ :		Optional[Any]							=  (
                  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    ):
                            a_ :		List[str]							=  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:
              a_ :		int							=  2
              a_ :		Dict							=  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:
              a_ :		Dict							=  (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'    )
       a_ :		Optional[Any]							=  torch.zeros(head_importance.numel()  ,   dtype=torch.long  ,   device=args.device    )
       a_ :		Tuple							=  torch.arange(
           head_importance.numel()  ,   device=args.device    )
       a_ :		Optional[Any]							=  head_ranks.view_as(__A    )
       print_ad_tensor(__A    )
       return attn_entropy, head_importance, total_loss







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Union[str, Any]  ,   __A			:      List[Any]  ,   __A			:      str    )     ->		Union[str, Any]:

       """simple docstring"""

       a_							,		a_							,		a_ :		Any							=  compute_heads_importance(__A  ,   __A  ,   __A  ,   compute_entropy=__A    )
       a_ :		List[str]							=  1 / loss  # instead of downsteam score use the LM loss
       logger.info('Pruning: original score: %f, threshold: %f'  ,   __A  ,   original_score * args.masking_threshold    )

       a_ :		List[Any]							=  torch.ones_like(__A    )
       a_ :		Optional[Any]							=  max(1  ,   int(new_head_mask.numel() * args.masking_amount    )    )

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

              if len(__A    ) <= num_to_mask:
                     print('BREAK BY num_to_mask'    )
                     break

              # mask heads
              a_ :		Any							=  current_heads_to_mask[:num_to_mask]
              logger.info('Heads to mask: %s'  ,   str(current_heads_to_mask.tolist()    )    )
              a_ :		Optional[Any]							=  new_head_mask.view(-1    )
              a_ :		Optional[int]							=  0.0
              a_ :		List[str]							=  new_head_mask.view_as(__A    )
              a_ :		Dict							=  new_head_mask.clone().detach()
              print_ad_tensor(__A    )

              # Compute metric and head importance again
              a_							,		a_							,		a_ :		int							=  compute_heads_importance(
                  __A  ,   __A  ,   __A  ,   compute_entropy=__A  ,   head_mask=__A    )
              a_ :		Optional[int]							=  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() * 1_00  ,   )

       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 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      int  ,   __A			:      Union[str, Any]  ,   __A			:      Optional[Any]    )     ->		Optional[Any]:

       """simple docstring"""

       a_ :		Dict							=  datetime.now()
       a_							,		a_							,		a_ :		Union[str, Any]							=  compute_heads_importance(
           __A  ,   __A  ,   __A  ,   compute_entropy=__A  ,   compute_importance=__A  ,   head_mask=__A    )
       a_ :		Union[str, Any]							=  1 / loss
       a_ :		List[Any]							=  datetime.now() - before_time

       a_ :		str							=  sum(p.numel() for p in model.parameters()    )
       a_ :		Any							=  {
           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    ):
                     a_ :		List[str]							=  [
                         v,
                     ]

       assert sum(len(__A    ) for h in heads_to_prune.values()    ) == (1 - head_mask.long()).sum().item()
       model.prune_heads(__A    )
       a_ :		str							=  sum(p.numel() for p in model.parameters()    )

       a_ :		Union[str, Any]							=  datetime.now()
       a_							,		a_							,		a_ :		int							=  compute_heads_importance(
           __A  ,   __A  ,   __A  ,   compute_entropy=__A  ,   compute_importance=__A  ,   head_mask=__A  ,   actually_pruned=__A  ,   )

       a_ :		int							=  1 / loss
       a_ :		str							=  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 * 1_00  ,   )
       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 * 1_00    )
       save_model(__A  ,   args.output_dir    )







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Tuple:

       """simple docstring"""

       a_ :		Union[str, Any]							=  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=1_28  ,   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.'    )
       a_ :		List[Any]							=  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:
              a_ :		str							=  torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'    )
              a_ :		List[Any]							=  0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank    )
              a_ :		Any							=  torch.device('cuda'  ,   args.local_rank    )
              a_ :		Union[str, Any]							=  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_ :		Union[str, Any]							=  GPTaLMHeadModel.from_pretrained(args.model_name_or_path    )

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


if __name__ == "__main__":
   main()


