





import inspect
import unittest

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTMSNForImageClassification, ViTMSNModel
		from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import ViTImageProcessor


class 			lowercase				:



						"""simple docstring"""

						def __init__(    self			:		str ,				lowerCamelCase_			:		List[str] ,				lowerCamelCase_			:		Optional[int]=13 ,				lowerCamelCase_			:		Optional[Any]=30 ,				lowerCamelCase_			:		str=2 ,				lowerCamelCase_			:		Tuple=3 ,				lowerCamelCase_			:		Union[str, Any]=True ,				lowerCamelCase_			:		Union[str, Any]=True ,				lowerCamelCase_			:		int=32 ,				lowerCamelCase_			:		Union[str, Any]=5 ,				lowerCamelCase_			:		Optional[Any]=4 ,				lowerCamelCase_			:		Dict=37 ,				lowerCamelCase_			:		Optional[int]="gelu" ,				lowerCamelCase_			:		Union[str, Any]=0.1 ,				lowerCamelCase_			:		Dict=0.1 ,				lowerCamelCase_			:		List[str]=10 ,				lowerCamelCase_			:		Optional[int]=0.02 ,				lowerCamelCase_			:		List[str]=None ,				):





										'''simple docstring'''







										_snake_case :			Tuple  						=   parent
										_snake_case :			Union[str, Any]  						=   batch_size
										_snake_case :			Tuple  						=   image_size
										_snake_case :			str  						=   patch_size
										_snake_case :			Dict  						=   num_channels
										_snake_case :			List[str]  						=   is_training
										_snake_case :			Optional[Any]  						=   use_labels
										_snake_case :			Optional[Any]  						=   hidden_size
										_snake_case :			List[str]  						=   num_hidden_layers
										_snake_case :			str  						=   num_attention_heads
										_snake_case :			str  						=   intermediate_size
										_snake_case :			int  						=   hidden_act
										_snake_case :			Dict  						=   hidden_dropout_prob
										_snake_case :			List[Any]  						=   attention_probs_dropout_prob
										_snake_case :			int  						=   type_sequence_label_size
										_snake_case :			int  						=   initializer_range
										_snake_case :			int  						=   scope

										# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										_snake_case :			int  						=   (image_size // patch_size) ** 2
										_snake_case :			Optional[Any]  						=   num_patches + 1







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			str  						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

										_snake_case :			Dict  						=   None
										if self.use_labels:
														_snake_case :			int  						=   ids_tensor([self.batch_size] ,				self.type_sequence_label_size					)

										_snake_case :			Union[str, Any]  						=   self.get_config()

										return config, pixel_values, labels







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										return ViTMSNConfig(
										    image_size=self.image_size ,				patch_size=self.patch_size ,				num_channels=self.num_channels ,				hidden_size=self.hidden_size ,				num_hidden_layers=self.num_hidden_layers ,				num_attention_heads=self.num_attention_heads ,				intermediate_size=self.intermediate_size ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				initializer_range=self.initializer_range ,				)







						def 		__UpperCAmelCase       (    self			:		List[Any] ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Tuple ,				lowerCamelCase_			:		Any					):





										'''simple docstring'''







										_snake_case :			List[Any]  						=   ViTMSNModel(config=lowerCamelCase_					)
										model.to(lowerCamelCase_					)
										model.eval()
										_snake_case :			int  						=   model(lowerCamelCase_					)
										self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)					)







						def 		__UpperCAmelCase       (    self			:		str ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			str  						=   self.type_sequence_label_size
										_snake_case :			List[Any]  						=   ViTMSNForImageClassification(lowerCamelCase_					)
										model.to(lowerCamelCase_					)
										model.eval()
										_snake_case :			List[Any]  						=   model(lowerCamelCase_ ,				labels=lowerCamelCase_					)
										print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'					)
										print('Labels: {labels}'					)
										self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)					)

										# test greyscale images
										_snake_case :			List[Any]  						=   1
										_snake_case :			Tuple  						=   ViTMSNForImageClassification(lowerCamelCase_					)
										model.to(lowerCamelCase_					)
										model.eval()

										_snake_case :			List[str]  						=   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
										_snake_case :			Union[str, Any]  						=   model(lowerCamelCase_					)
										self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.type_sequence_label_size)					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   self.prepare_config_and_inputs()
										_snake_case						,			_snake_case						,			_snake_case :			List[Any]  						=   config_and_inputs
										_snake_case :			List[Any]  						=   {'pixel_values': pixel_values}
										return config, inputs_dict



@require_torch
class 			lowercase				(							a_		,			a_		,			unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Optional[int]								=	(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
						_UpperCamelCase  :	str								=	(
						    {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_UpperCamelCase  :	Optional[Any]								=	False
						_UpperCamelCase  :	Union[str, Any]								=	False
						_UpperCamelCase  :	str								=	False
						_UpperCamelCase  :	Tuple								=	False







						def 		__UpperCAmelCase       (    self			:		Tuple					):





										'''simple docstring'''







										_snake_case :			Any  						=   ViTMSNModelTester(self					)
										_snake_case :			List[Any]  						=   ConfigTester(self ,				config_class=lowerCamelCase_ ,				has_text_modality=lowerCamelCase_ ,				hidden_size=37					)







						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										self.config_tester.run_common_tests()







						@unittest.skip(reason='ViTMSN does not use inputs_embeds'					)
						def 		__UpperCAmelCase       (    self			:		List[Any]					):





										'''simple docstring'''







										pass







						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case						,			_snake_case :			List[Any]  						=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_snake_case :			List[str]  						=   model_class(lowerCamelCase_					)
														self.assertIsInstance(model.get_input_embeddings() ,				(nn.Module)					)
														_snake_case :			List[Any]  						=   model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(lowerCamelCase_ ,				nn.Linear					)					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case						,			_snake_case :			Dict  						=   self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_snake_case :			Union[str, Any]  						=   model_class(lowerCamelCase_					)
														_snake_case :			Any  						=   inspect.signature(model.forward					)
														# signature.parameters is an OrderedDict => so arg_names order is deterministic
														_snake_case :			Optional[Any]  						=   [*signature.parameters.keys()]

														_snake_case :			Union[str, Any]  						=   ['pixel_values']
														self.assertListEqual(arg_names[:1] ,				lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowerCamelCase_					)







						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_					)







						@slow
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														_snake_case :			List[str]  						=   ViTMSNModel.from_pretrained(lowerCamelCase_					)
														self.assertIsNotNone(lowerCamelCase_					)



def       A__(		):
				_snake_case :			List[str]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
				return image


@require_torch
@require_vision
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						@cached_property
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'					) if is_vision_available() else None







						@slow
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										torch.manual_seed(2					)
										_snake_case :			Optional[int]  						=   ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'					).to(lowerCamelCase_					)

										_snake_case :			Optional[Any]  						=   self.default_image_processor
										_snake_case :			Any  						=   prepare_img()
										_snake_case :			Optional[Any]  						=   image_processor(images=lowerCamelCase_ ,				return_tensors='pt'					).to(lowerCamelCase_					)

										# forward pass
										with torch.no_grad():
														_snake_case :			Tuple  						=   model(**lowerCamelCase_					)

										# verify the logits
										_snake_case :			int  						=   torch.Size((1, 10_00)					)
										self.assertEqual(outputs.logits.shape ,				lowerCamelCase_					)

										_snake_case :			List[Any]  						=   torch.tensor([-0.0803, -0.4454, -0.2375]					).to(lowerCamelCase_					)

										self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				lowerCamelCase_ ,				atol=1e-4					)					)













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_		:				Any				=						logging.getLogger(__name__)

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				# save results
				if os.path.exists(__lowerCAmelCase ):
								if os.path.exists(os.path.join(__lowerCAmelCase	,			'config.json' ) ) and os.path.isfile(
								    os.path.join(__lowerCAmelCase	,			'config.json' ) ):
												os.remove(os.path.join(__lowerCAmelCase	,			'config.json' ) )
								if os.path.exists(os.path.join(__lowerCAmelCase	,			'pytorch_model.bin' ) ) and os.path.isfile(
								    os.path.join(__lowerCAmelCase	,			'pytorch_model.bin' ) ):
												os.remove(os.path.join(__lowerCAmelCase	,			'pytorch_model.bin' ) )
				else:
								os.makedirs(__lowerCAmelCase )
				model.save_pretrained(__lowerCAmelCase )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase=False ):
				_snake_case :			int  						=   2
				if unlogit:
								_snake_case :			Dict  						=   torch.pow(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Optional[int]  						=   p * torch.log(__lowerCAmelCase )
				_snake_case :			List[str]  						=   0
				return -plogp.sum(dim=-1 )

def       A__(		__lowerCAmelCase ):
				logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(__lowerCAmelCase ) ) ) )
				for row in range(len(__lowerCAmelCase ) ):
								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       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase=True	,			__lowerCAmelCase=True	,			__lowerCAmelCase=None	,			__lowerCAmelCase=False ):
				_snake_case						,			_snake_case :			List[str]  						=   model.config.num_hidden_layers, model.config.num_attention_heads
				_snake_case :			Optional[int]  						=   torch.zeros(__lowerCAmelCase	,			__lowerCAmelCase ).to(args.device )
				_snake_case :			Union[str, Any]  						=   torch.zeros(__lowerCAmelCase	,			__lowerCAmelCase ).to(args.device )

				if head_mask is None:
								_snake_case :			List[str]  						=   torch.ones(__lowerCAmelCase	,			__lowerCAmelCase ).to(args.device )

				head_mask.requires_grad_(requires_grad=__lowerCAmelCase )
				# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
				if actually_pruned:
								_snake_case :			List[str]  						=   None

				_snake_case :			str  						=   0.0
				_snake_case :			List[str]  						=   0.0
				for step, inputs in enumerate(tqdm(__lowerCAmelCase	,			desc='Iteration'	,			disable=args.local_rank not in [-1, 0] ) ):
								_snake_case :			Dict  						=   tuple(t.to(args.device ) for t in inputs )
								((_snake_case)						,			) :			int  						=   inputs

								# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
								_snake_case :			List[str]  						=   model(__lowerCAmelCase	,			labels=__lowerCAmelCase	,			head_mask=__lowerCAmelCase )
								#  (loss), lm_logits, presents, (all hidden_states), (attentions)
								_snake_case						,			_snake_case						,			_snake_case :			Dict  						=   (
								    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(__lowerCAmelCase ):
																_snake_case :			int  						=   entropy(attn.detach()	,			__lowerCAmelCase )
																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(__lowerCAmelCase ).float().detach().sum().data

				# Normalize
				attn_entropy /= tot_tokens
				head_importance /= tot_tokens
				# Layerwise importance normalization
				if not args.dont_normalize_importance_by_layer:
								_snake_case :			List[str]  						=   2
								_snake_case :			Any  						=   torch.pow(torch.pow(__lowerCAmelCase	,			__lowerCAmelCase ).sum(-1 )	,			1 / exponent )
								head_importance /= norm_by_layer.unsqueeze(-1 ) + 1E-20

				if not args.dont_normalize_global_importance:
								_snake_case :			Union[str, Any]  						=   (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

				# Print matrices
				if compute_entropy:
								logger.info('Attention entropies' )
								print_ad_tensor(__lowerCAmelCase )
				if compute_importance:
								logger.info('Head importance scores' )
								print_ad_tensor(__lowerCAmelCase )
				logger.info('Head ranked by importance scores' )
				_snake_case :			Tuple  						=   torch.zeros(head_importance.numel()	,			dtype=torch.long	,			device=args.device )
				_snake_case :			Optional[Any]  						=   torch.arange(
				    head_importance.numel()	,			device=args.device )
				_snake_case :			Union[str, Any]  						=   head_ranks.view_as(__lowerCAmelCase )
				print_ad_tensor(__lowerCAmelCase )
				return attn_entropy, head_importance, total_loss

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

				_snake_case :			Optional[Any]  						=   torch.ones_like(__lowerCAmelCase )
				_snake_case :			Tuple  						=   max(1	,			int(new_head_mask.numel() * args.masking_amount ) )

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

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

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

								# Compute metric and head importance again
								_snake_case						,			_snake_case						,			_snake_case :			List[str]  						=   compute_heads_importance(
								    __lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			compute_entropy=__lowerCAmelCase	,			head_mask=__lowerCAmelCase )
								_snake_case :			Union[str, Any]  						=   1 / loss
								logger.info(
								    'Masking: current score: %f, remaining heads %d (%.1f percents)'	,			__lowerCAmelCase	,			new_head_mask.sum()	,			new_head_mask.sum() / new_head_mask.numel() * 1_00	,			)

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

				return head_mask

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   datetime.now()
				_snake_case						,			_snake_case						,			_snake_case :			List[Any]  						=   compute_heads_importance(
				    __lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			compute_entropy=__lowerCAmelCase	,			compute_importance=__lowerCAmelCase	,			head_mask=__lowerCAmelCase )
				_snake_case :			str  						=   1 / loss
				_snake_case :			Optional[int]  						=   datetime.now() - before_time

				_snake_case :			Optional[int]  						=   sum(p.numel() for p in model.parameters() )
				_snake_case :			Optional[Any]  						=   {
				    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__lowerCAmelCase ) )
				}

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

				assert sum(len(__lowerCAmelCase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
				model.prune_heads(__lowerCAmelCase )
				_snake_case :			Optional[Any]  						=   sum(p.numel() for p in model.parameters() )

				_snake_case :			List[str]  						=   datetime.now()
				_snake_case						,			_snake_case						,			_snake_case :			Union[str, Any]  						=   compute_heads_importance(
				    __lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			compute_entropy=__lowerCAmelCase	,			compute_importance=__lowerCAmelCase	,			head_mask=__lowerCAmelCase	,			actually_pruned=__lowerCAmelCase	,			)

				_snake_case :			Dict  						=   1 / loss
				_snake_case :			str  						=   datetime.now() - before_time

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

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

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

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

				parser.add_argument('--seed'	,			type=__lowerCAmelCase	,			default=42 )
				parser.add_argument('--local_rank'	,			type=__lowerCAmelCase	,			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=__lowerCAmelCase	,			default=''	,			help='Can be used for distant debugging.' )
				parser.add_argument('--server_port'	,			type=__lowerCAmelCase	,			default=''	,			help='Can be used for distant debugging.' )
				_snake_case :			List[str]  						=   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=__lowerCAmelCase )
								ptvsd.wait_for_attach()

				# Setup devices and distributed training
				if args.local_rank == -1 or args.no_cuda:
								_snake_case :			Optional[Any]  						=   torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
								_snake_case :			Any  						=   0 if args.no_cuda else torch.cuda.device_count()
				else:
								torch.cuda.set_device(args.local_rank )
								_snake_case :			Union[str, Any]  						=   torch.device('cuda'	,			args.local_rank )
								_snake_case :			Dict  						=   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 ) ) )

				_snake_case :			Any  						=   GPTaLMHeadModel.from_pretrained(args.model_name_or_path )

				# Distributed and parallel training
				model.to(args.device )
				if args.local_rank != -1:
								_snake_case :			List[str]  						=   nn.parallel.DistributedDataParallel(
								    __lowerCAmelCase	,			device_ids=[args.local_rank]	,			output_device=args.local_rank	,			find_unused_parameters=__lowerCAmelCase )
				elif args.n_gpu > 1:
								_snake_case :			int  						=   nn.DataParallel(__lowerCAmelCase )

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

				# Prepare dataset
				_snake_case :			str  						=   np.concatenate(
				    [
				        np.loadtxt(args.data_dir	,			dtype=np.intaa ),
				    ] )
				_snake_case :			Dict  						=   (torch.from_numpy(__lowerCAmelCase ),)
				_snake_case :			List[Any]  						=   TensorDataset(*__lowerCAmelCase )
				_snake_case :			List[str]  						=   RandomSampler(__lowerCAmelCase )
				_snake_case :			List[str]  						=   DataLoader(__lowerCAmelCase	,			sampler=__lowerCAmelCase	,			batch_size=args.batch_size )

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

				# 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:
								_snake_case :			Union[str, Any]  						=   mask_heads(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )
								prune_heads(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )


if __name__ == "__main__":
		main()






