




"""simple docstring"""

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import ConditionalDetrImageProcessor





class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''

 def __init__(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	Union[str, Any]=7   ,							_UpperCAmelCase	:	int=3   ,							_UpperCAmelCase	:	List[Any]=30   ,							_UpperCAmelCase	:	str=400   ,							_UpperCAmelCase	:	Union[str, Any]=True   ,							_UpperCAmelCase	:	int=None   ,							_UpperCAmelCase	:	List[Any]=True   ,							_UpperCAmelCase	:	List[str]=[0.5, 0.5, 0.5]   ,							_UpperCAmelCase	:	Optional[int]=[0.5, 0.5, 0.5]   ,							_UpperCAmelCase	:	Optional[Any]=True   ,							_UpperCAmelCase	:	Union[str, Any]=1 / 255   ,							_UpperCAmelCase	:	Union[str, Any]=True   ,							):
       # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
       _A        =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
       _A        =       parent
       _A        =       batch_size
       _A        =       num_channels
       _A        =       min_resolution
       _A        =       max_resolution
       _A        =       do_resize
       _A        =       size
       _A        =       do_normalize
       _A        =       image_mean
       _A        =       image_std
       _A        =       do_rescale
       _A        =       rescale_factor
       _A        =       do_pad

 def       lowerCAmelCase_							(			self	:	int		):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_rescale": self.do_rescale,
           "rescale_factor": self.rescale_factor,
           "do_pad": self.do_pad,
       }



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	int=False		):
       if not batched:
             _A        =       image_inputs[0]
             if isinstance(__lowercase   ,							Image.Image		):
                   _A        =       image.size
             else:
                   _A        =       image.shape[1], image.shape[2]
             if w < h:
                   _A        =       int(self.size['shortest_edge'] * h / w		)
                   _A        =       self.size['''shortest_edge''']
             elif w > h:
                   _A        =       self.size['''shortest_edge''']
                   _A        =       int(self.size['shortest_edge'] * w / h		)
             else:
                   _A        =       self.size['''shortest_edge''']
                   _A        =       self.size['''shortest_edge''']

       else:
             _A        =       []
             for image in image_inputs:
                   _A        =       self.get_expected_values([image]		)
                   expected_values.append((expected_height, expected_width)		)
             _A        =       max(__lowercase   ,							key=lambda _UpperCAmelCase		: item[0]		)[0]
             _A        =       max(__lowercase   ,							key=lambda _UpperCAmelCase		: item[1]		)[1]

       return expected_height, expected_width





@require_torch
@require_vision
class     lowercase_	(					__lowerCamelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     ConditionalDetrImageProcessor if is_vision_available() else None

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       ConditionalDetrImageProcessingTester(self		)

 @property
 def       lowerCAmelCase_							(			self	:	int		):
       return self.image_processor_tester.prepare_image_processor_dict()

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       self.assertTrue(hasattr(__lowercase   ,							'image_mean'		)		)
       self.assertTrue(hasattr(__lowercase   ,							'image_std'		)		)
       self.assertTrue(hasattr(__lowercase   ,							'do_normalize'		)		)
       self.assertTrue(hasattr(__lowercase   ,							'do_resize'		)		)
       self.assertTrue(hasattr(__lowercase   ,							'size'		)		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       _A        =       self.image_processing_class.from_dict(self.image_processor_dict		)
       self.assertEqual(image_processor.size   ,							{'shortest_edge': 18, 'longest_edge': 1_333}		)
       self.assertEqual(image_processor.do_pad   ,							__lowercase		)

       _A        =       self.image_processing_class.from_dict(
           self.image_processor_dict   ,							size=42   ,							max_size=84   ,							pad_and_return_pixel_mask=__lowercase		)
       self.assertEqual(image_processor.size   ,							{'shortest_edge': 42, 'longest_edge': 84}		)
       self.assertEqual(image_processor.do_pad   ,							__lowercase		)

 def       lowerCAmelCase_							(			self	:	List[str]		):
       pass

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PIL images
       _A        =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=__lowercase		)
       for image in image_inputs:
             self.assertIsInstance(__lowercase   ,							Image.Image		)

       # Test not batched input
       _A        =       image_processing(image_inputs[0]   ,							return_tensors='pt'		).pixel_values

       _A        =       self.image_processor_tester.get_expected_values(__lowercase		)

       self.assertEqual(
           encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

       # Test batched
       _A        =       self.image_processor_tester.get_expected_values(__lowercase   ,							batched=__lowercase		)

       _A        =       image_processing(__lowercase   ,							return_tensors='pt'		).pixel_values
       self.assertEqual(
           encoded_images.shape   ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,							)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random numpy tensors
       _A        =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=__lowercase   ,							numpify=__lowercase		)
       for image in image_inputs:
             self.assertIsInstance(__lowercase   ,							np.ndarray		)

       # Test not batched input
       _A        =       image_processing(image_inputs[0]   ,							return_tensors='pt'		).pixel_values

       _A        =       self.image_processor_tester.get_expected_values(__lowercase		)

       self.assertEqual(
           encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

       # Test batched
       _A        =       image_processing(__lowercase   ,							return_tensors='pt'		).pixel_values

       _A        =       self.image_processor_tester.get_expected_values(__lowercase   ,							batched=__lowercase		)

       self.assertEqual(
           encoded_images.shape   ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,							)

 def       lowerCAmelCase_							(			self	:	Any		):
       # Initialize image_processing
       _A        =       self.image_processing_class(**self.image_processor_dict		)
       # create random PyTorch tensors
       _A        =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=__lowercase   ,							torchify=__lowercase		)
       for image in image_inputs:
             self.assertIsInstance(__lowercase   ,							torch.Tensor		)

       # Test not batched input
       _A        =       image_processing(image_inputs[0]   ,							return_tensors='pt'		).pixel_values

       _A        =       self.image_processor_tester.get_expected_values(__lowercase		)

       self.assertEqual(
           encoded_images.shape   ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,							)

       # Test batched
       _A        =       image_processing(__lowercase   ,							return_tensors='pt'		).pixel_values

       _A        =       self.image_processor_tester.get_expected_values(__lowercase   ,							batched=__lowercase		)

       self.assertEqual(
           encoded_images.shape   ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               expected_height,
               expected_width,
           )   ,							)

 @slow
 def       lowerCAmelCase_							(			self	:	int		):
       # prepare image and target
       _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
       with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'   ,							'r'		) as f:
             _A        =       json.loads(f.read()		)

       _A        =       {'''image_id''': 39_769, '''annotations''': target}

       # encode them
       _A        =       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'		)
       _A        =       image_processing(images=__lowercase   ,							annotations=__lowercase   ,							return_tensors='pt'		)

       # verify pixel values
       _A        =       torch.Size([1, 3, 800, 1_066]		)
       self.assertEqual(encoding['pixel_values'].shape   ,							__lowercase		)

       _A        =       torch.tensor([0.2796, 0.3138, 0.3481]		)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,							__lowercase   ,							atol=1E-4		)		)

       # verify area
       _A        =       torch.tensor([5887.9600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,							__lowercase		)		)
       # verify boxes
       _A        =       torch.Size([6, 4]		)
       self.assertEqual(encoding['labels'][0]['boxes'].shape   ,							__lowercase		)
       _A        =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,							__lowercase   ,							atol=1E-3		)		)
       # verify image_id
       _A        =       torch.tensor([39_769]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,							__lowercase		)		)
       # verify is_crowd
       _A        =       torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,							__lowercase		)		)
       # verify class_labels
       _A        =       torch.tensor([75, 75, 63, 65, 17, 17]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,							__lowercase		)		)
       # verify orig_size
       _A        =       torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,							__lowercase		)		)
       # verify size
       _A        =       torch.tensor([800, 1_066]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,							__lowercase		)		)



 @slow
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       # prepare image, target and masks_path
       _A        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
       with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'   ,							'r'		) as f:
             _A        =       json.loads(f.read()		)

       _A        =       {'''file_name''': '''000000039769.png''', '''image_id''': 39_769, '''segments_info''': target}

       _A        =       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'		)

       # encode them
       _A        =       ConditionalDetrImageProcessor(format='coco_panoptic'		)
       _A        =       image_processing(images=__lowercase   ,							annotations=__lowercase   ,							masks_path=__lowercase   ,							return_tensors='pt'		)

       # verify pixel values
       _A        =       torch.Size([1, 3, 800, 1_066]		)
       self.assertEqual(encoding['pixel_values'].shape   ,							__lowercase		)

       _A        =       torch.tensor([0.2796, 0.3138, 0.3481]		)
       self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]   ,							__lowercase   ,							atol=1E-4		)		)

       # verify area
       _A        =       torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5879.6562, 7634.1147]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['area']   ,							__lowercase		)		)
       # verify boxes
       _A        =       torch.Size([6, 4]		)
       self.assertEqual(encoding['labels'][0]['boxes'].shape   ,							__lowercase		)
       _A        =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]   ,							__lowercase   ,							atol=1E-3		)		)
       # verify image_id
       _A        =       torch.tensor([39_769]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']   ,							__lowercase		)		)
       # verify is_crowd
       _A        =       torch.tensor([0, 0, 0, 0, 0, 0]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']   ,							__lowercase		)		)
       # verify class_labels
       _A        =       torch.tensor([17, 17, 63, 75, 75, 93]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']   ,							__lowercase		)		)
       # verify masks
       _A        =       822_873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()   ,							__lowercase		)
       # verify orig_size
       _A        =       torch.tensor([480, 640]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']   ,							__lowercase		)		)
       # verify size
       _A        =       torch.tensor([800, 1_066]		)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']   ,							__lowercase		)		)







"""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 						_snake_case					( _snake_case							: str    ,  _snake_case							: Tuple							)   -> 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 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Optional[int]=False							)   -> Tuple:






      '''simple docstring'''


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






def 						_snake_case					( _snake_case							: Optional[Any]							)   -> int:






      '''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 						_snake_case					( _snake_case							: List[str]    ,  _snake_case							: Dict    ,  _snake_case							: List[str]    ,  _snake_case							: Union[str, Any]=True    ,  _snake_case							: Any=True    ,  _snake_case							: List[str]=None    ,  _snake_case							: List[Any]=False							)   -> int:






      '''simple docstring'''


      _A       ,   _A        =       model.config.num_hidden_layers, model.config.num_attention_heads
      _A        =       torch.zeros(_snake_case    ,  _snake_case							).to(args.device							)
      _A        =       torch.zeros(_snake_case    ,  _snake_case							).to(args.device							)

      if head_mask is None:
            _A        =       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:
            _A        =       None

      _A        =       0.0
      _A        =       0.0
      for step, inputs in enumerate(tqdm(_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(_snake_case    ,  labels=_snake_case    ,  head_mask=_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(_snake_case							):
                        _A        =       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:
            _A        =       2
            _A        =       torch.pow(torch.pow(_snake_case    ,  _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(_snake_case							)
      if compute_importance:
            logger.info('Head importance scores'							)
            print_ad_tensor(_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(_snake_case							)
      print_ad_tensor(_snake_case							)
      return attn_entropy, head_importance, total_loss






def 						_snake_case					( _snake_case							: Any    ,  _snake_case							: Tuple    ,  _snake_case							: List[Any]							)   -> List[str]:






      '''simple docstring'''


      _A       ,   _A       ,   _A        =       compute_heads_importance(_snake_case    ,  _snake_case    ,  _snake_case    ,  compute_entropy=_snake_case							)
      _A        =       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							)

      _A        =       torch.ones_like(_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(_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(_snake_case							)
            _A        =       new_head_mask.clone().detach()
            print_ad_tensor(_snake_case							)

            # Compute metric and head importance again
            _A       ,   _A       ,   _A        =       compute_heads_importance(
                _snake_case    ,  _snake_case    ,  _snake_case    ,  compute_entropy=_snake_case    ,  head_mask=_snake_case							)
            _A        =       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 						_snake_case					( _snake_case							: Union[str, Any]    ,  _snake_case							: Any    ,  _snake_case							: Dict    ,  _snake_case							: Optional[Any]							)   -> Union[str, Any]:






      '''simple docstring'''


      _A        =       datetime.now()
      _A       ,   _A       ,   _A        =       compute_heads_importance(
          _snake_case    ,  _snake_case    ,  _snake_case    ,  compute_entropy=_snake_case    ,  compute_importance=_snake_case    ,  head_mask=_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(_snake_case							)							)
      }

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

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

      _A        =       datetime.now()
      _A       ,   _A       ,   _A        =       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    ,  )

      _A        =       1 / loss
      _A        =       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 						_snake_case					( )   -> Dict:






      '''simple docstring'''


      _A        =       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.'							)
      _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=_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(
                _snake_case    ,  device_ids=[args.local_rank]    ,  output_device=args.local_rank    ,  find_unused_parameters=_snake_case							)
      elif args.n_gpu > 1:
            _A        =       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
      _A        =       np.concatenate(
          [
              np.loadtxt(args.data_dir    ,  dtype=np.intaa							),
          ]							)
      _A        =       (torch.from_numpy(_snake_case							),)
      _A        =       TensorDataset(*_snake_case							)
      _A        =       RandomSampler(_snake_case							)
      _A        =       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:
            _A        =       mask_heads(_snake_case    ,  _snake_case    ,  _snake_case							)
            prune_heads(_snake_case    ,  _snake_case    ,  _snake_case    ,  _snake_case							)


if __name__ == "__main__":
       main()

