
'''simple docstring'''







import inspect
import unittest

from transformers import ResNetConfig
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_backbone_common import BackboneTesterMixin
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
							from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor






class 					lowerCAmelCase__  :






					def __init__(  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=32							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=10							,				__SCREAMING_SNAKE_CASE=[10, 20, 30, 40]							,				__SCREAMING_SNAKE_CASE=[1, 1, 2, 1]							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE=True							,				__SCREAMING_SNAKE_CASE="relu"							,				__SCREAMING_SNAKE_CASE=3							,				__SCREAMING_SNAKE_CASE=None							,				__SCREAMING_SNAKE_CASE=["stage2", "stage3", "stage4"]							,				__SCREAMING_SNAKE_CASE=[2, 3, 4]							,				):


											"""simple docstring"""





											lowercase_      :	Any						=			parent
											lowercase_      :	Union[str, Any]						=			batch_size
											lowercase_      :	Dict						=			image_size
											lowercase_      :	Union[str, Any]						=			num_channels
											lowercase_      :	Any						=			embeddings_size
											lowercase_      :	str						=			hidden_sizes
											lowercase_      :	Any						=			depths
											lowercase_      :	List[str]						=			is_training
											lowercase_      :	Union[str, Any]						=			use_labels
											lowercase_      :	str						=			hidden_act
											lowercase_      :	Optional[Any]						=			num_labels
											lowercase_      :	List[str]						=			scope
											lowercase_      :	Dict						=			len(__SCREAMING_SNAKE_CASE						)
											lowercase_      :	int						=			out_features
											lowercase_      :	List[Any]						=			out_indices






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Any						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											lowercase_      :	Union[str, Any]						=			None
											if self.use_labels:
																	lowercase_      :	Optional[Any]						=			ids_tensor([self.batch_size]							,				self.num_labels						)

											lowercase_      :	Any						=			self.get_config()

											return config, pixel_values, labels






					def       _snake_case       (  self						):


											"""simple docstring"""





											return ResNetConfig(
											    num_channels=self.num_channels							,				embeddings_size=self.embeddings_size							,				hidden_sizes=self.hidden_sizes							,				depths=self.depths							,				hidden_act=self.hidden_act							,				num_labels=self.num_labels							,				out_features=self.out_features							,				out_indices=self.out_indices							,				)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			ResNetModel(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	List[str]						=			model(__SCREAMING_SNAKE_CASE						)
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape							,				(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)							,				)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	Any						=			self.num_labels
											lowercase_      :	List[Any]						=			ResNetForImageClassification(__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Optional[Any]						=			model(__SCREAMING_SNAKE_CASE							,				labels=__SCREAMING_SNAKE_CASE						)
											self.parent.assertEqual(result.logits.shape							,				(self.batch_size, self.num_labels)						)






					def       _snake_case       (  self							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			ResNetBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	Optional[int]						=			model(__SCREAMING_SNAKE_CASE						)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps						)							,				len(config.out_features						)						)
											self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,				[self.batch_size, self.hidden_sizes[1], 4, 4]						)

											# verify channels
											self.parent.assertEqual(len(model.channels						)							,				len(config.out_features						)						)
											self.parent.assertListEqual(model.channels							,				config.hidden_sizes[1:]						)

											# verify backbone works with out_features=None
											lowercase_      :	Optional[Any]						=			None
											lowercase_      :	Dict						=			ResNetBackbone(config=__SCREAMING_SNAKE_CASE						)
											model.to(__SCREAMING_SNAKE_CASE						)
											model.eval()
											lowercase_      :	List[Any]						=			model(__SCREAMING_SNAKE_CASE						)

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps						)							,				1						)
											self.parent.assertListEqual(list(result.feature_maps[0].shape						)							,				[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

											# verify channels
											self.parent.assertEqual(len(model.channels						)							,				1						)
											self.parent.assertListEqual(model.channels							,				[config.hidden_sizes[-1]]						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[int]						=			self.prepare_config_and_inputs()
											lowercase_		,					lowercase_		,					lowercase_      :	Optional[int]						=			config_and_inputs
											lowercase_      :	List[Any]						=			{'''pixel_values''': pixel_values}
											return config, inputs_dict







@require_torch
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(
					    (
					        ResNetModel,
					        ResNetForImageClassification,
					        ResNetBackbone,
					    )
					    if is_torch_available()
					    else ()
					)
					lowerCAmelCase_	  =	(
					    {'''feature-extraction''': ResNetModel, '''image-classification''': ResNetForImageClassification}
					    if is_torch_available()
					    else {}
					)

					lowerCAmelCase_	  =	True
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	False






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[Any]						=			ResNetModelTester(self						)
											lowercase_      :	Dict						=			ConfigTester(self							,				config_class=__SCREAMING_SNAKE_CASE							,				has_text_modality=__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






					def       _snake_case       (  self						):


											"""simple docstring"""





											return






					@unittest.skip(reason='''ResNet does not use inputs_embeds'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					@unittest.skip(reason='''ResNet does not support input and output embeddings'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





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

											for model_class in self.all_model_classes:
																	lowercase_      :	str						=			model_class(__SCREAMING_SNAKE_CASE						)
																	lowercase_      :	int						=			inspect.signature(model.forward						)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowercase_      :	List[str]						=			[*signature.parameters.keys()]

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






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE						)






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_		,					lowercase_      :	Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	lowercase_      :	Any						=			model_class(config=__SCREAMING_SNAKE_CASE						)
																	for name, module in model.named_modules():
																							if isinstance(__SCREAMING_SNAKE_CASE							,				(nn.BatchNormad, nn.GroupNorm)						):
																													self.assertTrue(
																													    torch.all(module.weight == 1						)							,				msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,				)
																													self.assertTrue(
																													    torch.all(module.bias == 0						)							,				msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''							,				)






					def       _snake_case       (  self						):


											"""simple docstring"""





											def check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						):
																	lowercase_      :	Tuple						=			model_class(__SCREAMING_SNAKE_CASE						)
																	model.to(__SCREAMING_SNAKE_CASE						)
																	model.eval()

																	with torch.no_grad():
																							lowercase_      :	Dict						=			model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)						)

																	lowercase_      :	Tuple						=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																	lowercase_      :	List[Any]						=			self.model_tester.num_stages
																	self.assertEqual(len(__SCREAMING_SNAKE_CASE						)							,				expected_num_stages + 1						)

																	# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-2:]						)							,				[self.model_tester.image_size // 4, self.model_tester.image_size // 4]							,				)

											lowercase_		,					lowercase_      :	Optional[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()
											lowercase_      :	Dict						=			['''basic''', '''bottleneck''']
											for model_class in self.all_model_classes:
																	for layer_type in layers_type:
																							lowercase_      :	str						=			layer_type
																							lowercase_      :	Tuple						=			True
																							check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)

																							# check that output_hidden_states also work using config
																							del inputs_dict["output_hidden_states"]
																							lowercase_      :	Union[str, Any]						=			True

																							check_hidden_states_output(__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE							,				__SCREAMING_SNAKE_CASE						)






					@unittest.skip(reason='''ResNet does not use feedforward chunking'''						)
					def       _snake_case       (  self						):


											"""simple docstring"""





											pass






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE						)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase_      :	int						=			ResNetModel.from_pretrained(__SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(__SCREAMING_SNAKE_CASE						)







def    snake_case_						(							):



						"""simple docstring"""





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






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






					@cached_property
					def       _snake_case       (  self						):


											"""simple docstring"""





											return (
											    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
											    if is_vision_available()
											    else None
											)






					@slow
					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	List[str]						=			ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__SCREAMING_SNAKE_CASE						)

											lowercase_      :	List[str]						=			self.default_image_processor
											lowercase_      :	str						=			prepare_img()
											lowercase_      :	Any						=			image_processor(images=__SCREAMING_SNAKE_CASE							,				return_tensors='''pt'''						).to(__SCREAMING_SNAKE_CASE						)

											# forward pass
											with torch.no_grad():
																	lowercase_      :	Dict						=			model(**__SCREAMING_SNAKE_CASE						)

											# verify the logits
											lowercase_      :	Any						=			torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape							,				__SCREAMING_SNAKE_CASE						)

											lowercase_      :	Dict						=			torch.tensor([-11.1_069, -9.7_877, -8.3_777]						).to(__SCREAMING_SNAKE_CASE						)

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







@require_torch
class 					lowerCAmelCase__  (    lowerCamelCase_       ,		unittest.TestCase	):
					lowerCAmelCase_	  =	(ResNetBackbone,) if is_torch_available() else ()
					lowerCAmelCase_	  =	False
					lowerCAmelCase_	  =	ResNetConfig






					def       _snake_case       (  self						):


											"""simple docstring"""





											lowercase_      :	Optional[Any]						=			ResNetModelTester(self						)





'''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, SequentialSampler, Subset
from torch.utils.data.distributed import DistributedSampler
from tqdm import tqdm

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    GlueDataset,
    default_data_collator,
    glue_compute_metrics,
    glue_output_modes,
    glue_processors,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowercase					:    Dict       				=			logging.getLogger(__name__)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					List[str]				):



						"""simple docstring"""





						lowercase_      :	List[Any]						=			p * torch.log(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[Any]						=			0
						return -plogp.sum(dim=-1				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					int				):



						"""simple docstring"""





						logger.info('''lv, h >\t''' + '''\t'''.join(F'''{x + 1}''' for x in range(len(__SCREAMING_SNAKE_CASE				)				)				)				)
						for row in range(len(__SCREAMING_SNAKE_CASE				)				):
												if tensor.dtype != torch.long:
																		logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:.5f}''' for x in tensor[row].cpu().data				)				)
												else:
																		logger.info(F'''layer {row + 1}:\t''' + '''\t'''.join(F'''{x:d}''' for x in tensor[row].cpu().data				)				)
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Union[str, Any] , __SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					int , __SCREAMING_SNAKE_CASE   :					Tuple=True , __SCREAMING_SNAKE_CASE   :					int=True , __SCREAMING_SNAKE_CASE   :					str=None , __SCREAMING_SNAKE_CASE   :					Union[str, Any]=False				):



						"""simple docstring"""





						lowercase_		,					lowercase_      :	List[str]						=			model.config.num_hidden_layers, model.config.num_attention_heads
						lowercase_      :	Union[str, Any]						=			torch.zeros(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).to(args.device				)
						lowercase_      :	str						=			torch.zeros(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).to(args.device				)

						if head_mask is None:
												lowercase_      :	int						=			torch.ones(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).to(args.device				)

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

						lowercase_      :	Dict						=			None
						lowercase_      :	List[Any]						=			None
						lowercase_      :	Any						=			0.0

						for step, inputs in enumerate(tqdm(__SCREAMING_SNAKE_CASE , desc='''Iteration''' , disable=args.local_rank not in [-1, 0]				)				):
												for k, v in inputs.items():
																		lowercase_      :	List[Any]						=			v.to(args.device				)

												# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
												lowercase_      :	str						=			model(**__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE				)
												lowercase_		,					lowercase_		,					lowercase_      :	int						=			(
												    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

												if compute_entropy:
																		for layer, attn in enumerate(__SCREAMING_SNAKE_CASE				):
																								lowercase_      :	Any						=			entropy(attn.detach()				) * inputs['''attention_mask'''].float().unsqueeze(1				)
																								attn_entropy[layer] += masked_entropy.sum(-1				).sum(0				).detach()

												if compute_importance:
																		head_importance += head_mask.grad.abs().detach()

												# Also store our logits/labels if we want to compute metrics afterwards
												if preds is None:
																		lowercase_      :	List[str]						=			logits.detach().cpu().numpy()
																		lowercase_      :	int						=			inputs['''labels'''].detach().cpu().numpy()
												else:
																		lowercase_      :	Optional[int]						=			np.append(__SCREAMING_SNAKE_CASE , logits.detach().cpu().numpy() , axis=0				)
																		lowercase_      :	Dict						=			np.append(__SCREAMING_SNAKE_CASE , inputs['''labels'''].detach().cpu().numpy() , axis=0				)

												tot_tokens += inputs["attention_mask"].float().detach().sum().data

						# Normalize
						attn_entropy /= tot_tokens
						head_importance /= tot_tokens
						# Layerwise importance normalization
						if not args.dont_normalize_importance_by_layer:
												lowercase_      :	Union[str, Any]						=			2
												lowercase_      :	int						=			torch.pow(torch.pow(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				).sum(-1				) , 1 / exponent				)
												head_importance /= norm_by_layer.unsqueeze(-1				) + 1E-2_0

						if not args.dont_normalize_global_importance:
												lowercase_      :	str						=			(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

						# Print/save matrices
						np.save(os.path.join(args.output_dir , '''attn_entropy.npy'''				) , attn_entropy.detach().cpu().numpy()				)
						np.save(os.path.join(args.output_dir , '''head_importance.npy'''				) , head_importance.detach().cpu().numpy()				)

						logger.info('''Attention entropies'''				)
						print_ad_tensor(__SCREAMING_SNAKE_CASE				)
						logger.info('''Head importance scores'''				)
						print_ad_tensor(__SCREAMING_SNAKE_CASE				)
						logger.info('''Head ranked by importance scores'''				)
						lowercase_      :	Any						=			torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device				)
						lowercase_      :	Tuple						=			torch.arange(
						    head_importance.numel() , device=args.device				)
						lowercase_      :	Optional[int]						=			head_ranks.view_as(__SCREAMING_SNAKE_CASE				)
						print_ad_tensor(__SCREAMING_SNAKE_CASE				)

						return attn_entropy, head_importance, preds, labels
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					Tuple , __SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					List[Any]				):



						"""simple docstring"""





						lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	Tuple						=			compute_heads_importance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , compute_entropy=__SCREAMING_SNAKE_CASE				)
						lowercase_      :	str						=			np.argmax(__SCREAMING_SNAKE_CASE , axis=1				) if args.output_mode == '''classification''' else np.squeeze(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	int						=			glue_compute_metrics(args.task_name , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)[args.metric_name]
						logger.info('''Pruning: original score: %f, threshold: %f''' , __SCREAMING_SNAKE_CASE , original_score * args.masking_threshold				)

						lowercase_      :	Union[str, Any]						=			torch.ones_like(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Dict						=			max(1 , int(new_head_mask.numel() * args.masking_amount				)				)

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

												if len(__SCREAMING_SNAKE_CASE				) <= num_to_mask:
																		break

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

												# Compute metric and head importance again
												lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	Optional[int]						=			compute_heads_importance(
												    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , compute_entropy=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE				)
												lowercase_      :	List[str]						=			np.argmax(__SCREAMING_SNAKE_CASE , axis=1				) if args.output_mode == '''classification''' else np.squeeze(__SCREAMING_SNAKE_CASE				)
												lowercase_      :	str						=			glue_compute_metrics(args.task_name , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)[args.metric_name]
												logger.info(
												    '''Masking: current score: %f, remaining heads %d (%.1f percents)''' , __SCREAMING_SNAKE_CASE , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )

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

						return head_mask
def    snake_case_						(							__SCREAMING_SNAKE_CASE   :					str , __SCREAMING_SNAKE_CASE   :					Dict , __SCREAMING_SNAKE_CASE   :					List[str] , __SCREAMING_SNAKE_CASE   :					int				):



						"""simple docstring"""





						lowercase_      :	int						=			datetime.now()
						lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	List[Any]						=			compute_heads_importance(
						    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , compute_entropy=__SCREAMING_SNAKE_CASE , compute_importance=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE				)
						lowercase_      :	str						=			np.argmax(__SCREAMING_SNAKE_CASE , axis=1				) if args.output_mode == '''classification''' else np.squeeze(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Optional[int]						=			glue_compute_metrics(args.task_name , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)[args.metric_name]
						lowercase_      :	Union[str, Any]						=			datetime.now() - before_time

						lowercase_      :	Union[str, Any]						=			sum(p.numel() for p in model.parameters()				)
						lowercase_      :	Union[str, Any]						=			{
						    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__SCREAMING_SNAKE_CASE				)				)
						}

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

						lowercase_      :	str						=			datetime.now()
						lowercase_		,					lowercase_		,					lowercase_		,					lowercase_      :	Optional[int]						=			compute_heads_importance(
						    __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , compute_entropy=__SCREAMING_SNAKE_CASE , compute_importance=__SCREAMING_SNAKE_CASE , head_mask=__SCREAMING_SNAKE_CASE , actually_pruned=__SCREAMING_SNAKE_CASE , )
						lowercase_      :	Union[str, Any]						=			np.argmax(__SCREAMING_SNAKE_CASE , axis=1				) if args.output_mode == '''classification''' else np.squeeze(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Union[str, Any]						=			glue_compute_metrics(args.task_name , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)[args.metric_name]
						lowercase_      :	Dict						=			datetime.now() - before_time

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



						"""simple docstring"""





						lowercase_      :	Optional[int]						=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--data_dir''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help='''The input data dir. Should contain the .tsv files (or other data files) for the task.''' , )
						parser.add_argument(
						    '''--model_name_or_path''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help='''Path to pretrained model or model identifier from huggingface.co/models''' , )
						parser.add_argument(
						    '''--task_name''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help='''The name of the task to train selected in the list: ''' + ''', '''.join(glue_processors.keys()				) , )
						parser.add_argument(
						    '''--output_dir''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , help='''The output directory where the model predictions and checkpoints will be written.''' , )

						# Other parameters
						parser.add_argument(
						    '''--config_name''' , default='''''' , type=__SCREAMING_SNAKE_CASE , help='''Pretrained config name or path if not the same as model_name_or_path''' , )
						parser.add_argument(
						    '''--tokenizer_name''' , default='''''' , type=__SCREAMING_SNAKE_CASE , help='''Pretrained tokenizer name or path if not the same as model_name_or_path''' , )
						parser.add_argument(
						    '''--cache_dir''' , default=__SCREAMING_SNAKE_CASE , type=__SCREAMING_SNAKE_CASE , help='''Where do you want to store the pre-trained models downloaded from huggingface.co''' , )
						parser.add_argument(
						    '''--data_subset''' , type=__SCREAMING_SNAKE_CASE , default=-1 , help='''If > 0: limit the data to a subset of data_subset instances.'''				)
						parser.add_argument(
						    '''--overwrite_output_dir''' , action='''store_true''' , help='''Whether to overwrite data in output directory'''				)
						parser.add_argument(
						    '''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets'''				)

						parser.add_argument(
						    '''--dont_normalize_importance_by_layer''' , action='''store_true''' , help='''Don\'t normalize importance score by layers'''				)
						parser.add_argument(
						    '''--dont_normalize_global_importance''' , action='''store_true''' , help='''Don\'t normalize all importance scores between 0 and 1''' , )

						parser.add_argument(
						    '''--try_masking''' , action='''store_true''' , help='''Whether to try to mask head until a threshold of accuracy.'''				)
						parser.add_argument(
						    '''--masking_threshold''' , default=0.9 , type=__SCREAMING_SNAKE_CASE , help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).''' , )
						parser.add_argument(
						    '''--masking_amount''' , default=0.1 , type=__SCREAMING_SNAKE_CASE , help='''Amount to heads to masking at each masking step.'''				)
						parser.add_argument('''--metric_name''' , default='''acc''' , type=__SCREAMING_SNAKE_CASE , help='''Metric to use for head masking.'''				)

						parser.add_argument(
						    '''--max_seq_length''' , default=128 , type=__SCREAMING_SNAKE_CASE , help=(
						        '''The maximum total input sequence length after WordPiece tokenization. \n'''
						        '''Sequences longer than this will be truncated, sequences shorter padded.'''
						    ) , )
						parser.add_argument('''--batch_size''' , default=1 , type=__SCREAMING_SNAKE_CASE , help='''Batch size.'''				)

						parser.add_argument('''--seed''' , type=__SCREAMING_SNAKE_CASE , default=42				)
						parser.add_argument('''--local_rank''' , type=__SCREAMING_SNAKE_CASE , default=-1 , help='''local_rank for distributed training on gpus'''				)
						parser.add_argument('''--no_cuda''' , action='''store_true''' , help='''Whether not to use CUDA when available'''				)
						parser.add_argument('''--server_ip''' , type=__SCREAMING_SNAKE_CASE , default='''''' , help='''Can be used for distant debugging.'''				)
						parser.add_argument('''--server_port''' , type=__SCREAMING_SNAKE_CASE , default='''''' , help='''Can be used for distant debugging.'''				)
						lowercase_      :	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=__SCREAMING_SNAKE_CASE				)
												ptvsd.wait_for_attach()

						# Setup devices and distributed training
						if args.local_rank == -1 or args.no_cuda:
												lowercase_      :	Optional[int]						=			torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''				)
												lowercase_      :	Dict						=			0 if args.no_cuda else torch.cuda.device_count()
						else:
												torch.cuda.set_device(args.local_rank				)
												lowercase_      :	Tuple						=			torch.device('''cuda''' , args.local_rank				)
												lowercase_      :	Optional[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				)				)				)
						# Set the verbosity to info of the Transformers logger (on main process only):
						if is_main_process(args.local_rank				):
												transformers.utils.logging.set_verbosity_info()
												transformers.utils.logging.enable_default_handler()
												transformers.utils.logging.enable_explicit_format()

						# Set seeds
						set_seed(args.seed				)

						# Prepare GLUE task
						lowercase_      :	Dict						=			args.task_name.lower()
						if args.task_name not in glue_processors:
												raise ValueError('''Task not found: %s''' % (args.task_name)				)
						lowercase_      :	Optional[Any]						=			glue_processors[args.task_name]()
						lowercase_      :	Any						=			glue_output_modes[args.task_name]
						lowercase_      :	Dict						=			processor.get_labels()
						lowercase_      :	Tuple						=			len(__SCREAMING_SNAKE_CASE				)

						# Load pretrained model and tokenizer
						#
						# Distributed training:
						# The .from_pretrained methods guarantee that only one local process can concurrently
						# download model & vocab.

						lowercase_      :	Union[str, Any]						=			AutoConfig.from_pretrained(
						    args.config_name if args.config_name else args.model_name_or_path , num_labels=__SCREAMING_SNAKE_CASE , finetuning_task=args.task_name , output_attentions=__SCREAMING_SNAKE_CASE , cache_dir=args.cache_dir , )
						lowercase_      :	str						=			AutoTokenizer.from_pretrained(
						    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path , cache_dir=args.cache_dir , )
						lowercase_      :	Tuple						=			AutoModelForSequenceClassification.from_pretrained(
						    args.model_name_or_path , from_tf=bool('''.ckpt''' in args.model_name_or_path				) , config=__SCREAMING_SNAKE_CASE , cache_dir=args.cache_dir , )

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

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

						# Prepare dataset for the GLUE task
						lowercase_      :	Dict						=			GlueDataset(__SCREAMING_SNAKE_CASE , tokenizer=__SCREAMING_SNAKE_CASE , mode='''dev'''				)
						if args.data_subset > 0:
												lowercase_      :	Tuple						=			Subset(__SCREAMING_SNAKE_CASE , list(range(min(args.data_subset , len(__SCREAMING_SNAKE_CASE				)				)				)				)				)
						lowercase_      :	List[Any]						=			SequentialSampler(__SCREAMING_SNAKE_CASE				) if args.local_rank == -1 else DistributedSampler(__SCREAMING_SNAKE_CASE				)
						lowercase_      :	Any						=			DataLoader(
						    __SCREAMING_SNAKE_CASE , sampler=__SCREAMING_SNAKE_CASE , batch_size=args.batch_size , collate_fn=__SCREAMING_SNAKE_CASE				)

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

						# Try head masking (set heads to zero until the score goes under a threshole)
						# and head pruning (remove masked heads and see the effect on the network)
						if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
												lowercase_      :	List[Any]						=			mask_heads(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)
												prune_heads(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
							main()



