import collections
import inspect
import unittest

from transformers import SwinConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
						from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import AutoImageProcessor
class 	_a  :



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_=13       ,  lowerCAmelCase_=32       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=3       ,  lowerCAmelCase_=16       ,  lowerCAmelCase_=[1, 2, 1]       ,  lowerCAmelCase_=[2, 2, 4]       ,  lowerCAmelCase_=2       ,  lowerCAmelCase_=2.0       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0.0       ,  lowerCAmelCase_=0.0       ,  lowerCAmelCase_=0.1       ,  lowerCAmelCase_="gelu"       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=0.0_2       ,  lowerCAmelCase_=1e-5       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=10       ,  lowerCAmelCase_=8       ,  lowerCAmelCase_=["stage1", "stage2"]       ,  lowerCAmelCase_=[1, 2]       ,  ):
		_lowercase   =parent
		_lowercase   =batch_size
		_lowercase   =image_size
		_lowercase   =patch_size
		_lowercase   =num_channels
		_lowercase   =embed_dim
		_lowercase   =depths
		_lowercase   =num_heads
		_lowercase   =window_size
		_lowercase   =mlp_ratio
		_lowercase   =qkv_bias
		_lowercase   =hidden_dropout_prob
		_lowercase   =attention_probs_dropout_prob
		_lowercase   =drop_path_rate
		_lowercase   =hidden_act
		_lowercase   =use_absolute_embeddings
		_lowercase   =patch_norm
		_lowercase   =layer_norm_eps
		_lowercase   =initializer_range
		_lowercase   =is_training
		_lowercase   =scope
		_lowercase   =use_labels
		_lowercase   =type_sequence_label_size
		_lowercase   =encoder_stride
		_lowercase   =out_features
		_lowercase   =out_indices


	def __lowerCAmelCase      (   self       ):
		_lowercase   =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		_lowercase   =None
		if self.use_labels:
			_lowercase   =ids_tensor([self.batch_size]       ,  self.type_sequence_label_size       )

		_lowercase   =self.get_config()

		return config, pixel_values, labels


	def __lowerCAmelCase      (   self       ):
		return SwinConfig(
		    image_size=self.image_size       ,  patch_size=self.patch_size       ,  num_channels=self.num_channels       ,  embed_dim=self.embed_dim       ,  depths=self.depths       ,  num_heads=self.num_heads       ,  window_size=self.window_size       ,  mlp_ratio=self.mlp_ratio       ,  qkv_bias=self.qkv_bias       ,  hidden_dropout_prob=self.hidden_dropout_prob       ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,  drop_path_rate=self.drop_path_rate       ,  hidden_act=self.hidden_act       ,  use_absolute_embeddings=self.use_absolute_embeddings       ,  path_norm=self.patch_norm       ,  layer_norm_eps=self.layer_norm_eps       ,  initializer_range=self.initializer_range       ,  encoder_stride=self.encoder_stride       ,  out_features=self.out_features       ,  out_indices=self.out_indices       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =SwinModel(config=lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       )

		_lowercase   =((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths       ) - 1))
		_lowercase   =int(config.embed_dim * 2 ** (len(config.depths       ) - 1)       )

		self.parent.assertEqual(result.last_hidden_state.shape       ,  (self.batch_size, expected_seq_len, expected_dim)       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =SwinBackbone(config=lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       )

		# verify hidden states
		self.parent.assertEqual(len(result.feature_maps       )       ,  len(config.out_features       )       )
		self.parent.assertListEqual(list(result.feature_maps[0].shape       )       ,  [self.batch_size, model.channels[0], 16, 16]       )

		# verify channels
		self.parent.assertEqual(len(model.channels       )       ,  len(config.out_features       )       )

		# verify backbone works with out_features=None
		_lowercase   =None
		_lowercase   =SwinBackbone(config=lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       )

		# verify feature maps
		self.parent.assertEqual(len(result.feature_maps       )       ,  1       )
		self.parent.assertListEqual(list(result.feature_maps[0].shape       )       ,  [self.batch_size, model.channels[-1], 4, 4]       )

		# verify channels
		self.parent.assertEqual(len(model.channels       )       ,  1       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =SwinForMaskedImageModeling(config=lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(
		    result.logits.shape       ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)       )

		# test greyscale images
		_lowercase   =1
		_lowercase   =SwinForMaskedImageModeling(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()

		_lowercase   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, 1, self.image_size, self.image_size)       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =self.type_sequence_label_size
		_lowercase   =SwinForImageClassification(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()
		_lowercase   =model(lowerCAmelCase_       ,  labels=lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )

		# test greyscale images
		_lowercase   =1
		_lowercase   =SwinForImageClassification(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()

		_lowercase   =floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
		_lowercase   =model(lowerCAmelCase_       )
		self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.type_sequence_label_size)       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.prepare_config_and_inputs()
		(
		    (
		    _lowercase
		)   ,  (
		    _lowercase
		)   ,  (
		    _lowercase
		)   ,  
		)   =config_and_inputs
		_lowercase   ={"pixel_values": pixel_values}
		return config, inputs_dict

@require_torch
class 	_a  (							lowerCamelCase_							,		lowerCamelCase_							,		unittest.TestCase					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(
	    (
	        SwinModel,
	        SwinBackbone,
	        SwinForImageClassification,
	        SwinForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	__SCREAMING_SNAKE_CASE              =					(
	    {'feature-extraction': SwinModel, 'image-classification': SwinForImageClassification}
	    if is_torch_available()
	    else {}
	)
	__SCREAMING_SNAKE_CASE              =					True

	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False
	__SCREAMING_SNAKE_CASE              =					False


	def __lowerCAmelCase      (   self       ):
		_lowercase   =SwinModelTester(self       )
		_lowercase   =ConfigTester(self       ,  config_class=lowerCAmelCase_       ,  embed_dim=37       )


	def __lowerCAmelCase      (   self       ):
		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 __lowerCAmelCase      (   self       ):
		return


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*lowerCAmelCase_       )


	@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."       )
	def __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		super().test_training_gradient_checkpointing()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_backbone(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase_       )


	@unittest.skip(reason="Swin does not use inputs_embeds"       )
	def __lowerCAmelCase      (   self       ):
		pass


	@unittest.skip(reason="Swin Transformer does not use feedforward chunking"       )
	def __lowerCAmelCase      (   self       ):
		pass


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			_lowercase   =model_class(lowerCAmelCase_       )
			self.assertIsInstance(model.get_input_embeddings()       ,  (nn.Module)       )
			_lowercase   =model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(lowerCAmelCase_       ,  nn.Linear       )       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

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

			_lowercase   =["pixel_values"]
			self.assertListEqual(arg_names[:1]       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =True

		for model_class in self.all_model_classes:
			_lowercase   =True
			_lowercase   =False
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.attentions
			_lowercase   =len(self.model_tester.depths       )
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			# check that output_attentions also work using config
			del inputs_dict["output_attentions"]
			_lowercase   =True
			_lowercase   =config.window_size**2
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )
			_lowercase   =outputs.attentions
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			self.assertListEqual(
			    list(attentions[0].shape[-3:]       )       ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,  )
			_lowercase   =len(lowerCAmelCase_       )

			# Check attention is always last and order is fine
			_lowercase   =True
			_lowercase   =True
			_lowercase   =model_class(lowerCAmelCase_       )
			model.to(lowerCAmelCase_       )
			model.eval()
			with torch.no_grad():
				_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )

			# also another +1 for reshaped_hidden_states
			_lowercase   =1 if model_class.__name__ == "SwinBackbone" else 2
			self.assertEqual(out_len + added_hidden_states       ,  len(lowerCAmelCase_       )       )

			_lowercase   =outputs.attentions

			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			self.assertListEqual(
			    list(self_attentions[0].shape[-3:]       )       ,  [self.model_tester.num_heads[0], window_size_squared, window_size_squared]       ,  )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ):
		_lowercase   =model_class(lowerCAmelCase_       )
		model.to(lowerCAmelCase_       )
		model.eval()

		with torch.no_grad():
			_lowercase   =model(**self._prepare_for_class(lowerCAmelCase_       ,  lowerCAmelCase_       )       )

		_lowercase   =outputs.hidden_states

		_lowercase   =getattr(
		    self.model_tester       ,  "expected_num_hidden_layers"       ,  len(self.model_tester.depths       ) + 1       )
		self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

		# Swin has a different seq_length
		_lowercase   =(
		    config.patch_size
		    if isinstance(config.patch_size       ,  collections.abc.Iterable       )
		    else (config.patch_size, config.patch_size)
		)

		_lowercase   =(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

		self.assertListEqual(
		    list(hidden_states[0].shape[-2:]       )       ,  [num_patches, self.model_tester.embed_dim]       ,  )

		if not model_class.__name__ == "SwinBackbone":
			_lowercase   =outputs.reshaped_hidden_states
			self.assertEqual(len(lowerCAmelCase_       )       ,  lowerCAmelCase_       )

			_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =reshaped_hidden_states[0].shape
			_lowercase   =(
			    reshaped_hidden_states[0].view(lowerCAmelCase_       ,  lowerCAmelCase_       ,  height * width       ).permute(0       ,  2       ,  1       )
			)
			self.assertListEqual(
			    list(reshaped_hidden_states.shape[-2:]       )       ,  [num_patches, self.model_tester.embed_dim]       ,  )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		_lowercase   =(
		    self.model_tester.image_size
		    if isinstance(self.model_tester.image_size       ,  collections.abc.Iterable       )
		    else (self.model_tester.image_size, self.model_tester.image_size)
		)

		for model_class in self.all_model_classes:
			_lowercase   =True
			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			_lowercase   =True

			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()
		_lowercase   =3

		_lowercase   =(
		    self.model_tester.image_size
		    if isinstance(self.model_tester.image_size       ,  collections.abc.Iterable       )
		    else (self.model_tester.image_size, self.model_tester.image_size)
		)
		_lowercase   =(
		    config.patch_size
		    if isinstance(config.patch_size       ,  collections.abc.Iterable       )
		    else (config.patch_size, config.patch_size)
		)

		_lowercase   =image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
		_lowercase   =image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

		for model_class in self.all_model_classes:
			_lowercase   =True
			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  (padded_height, padded_width)       )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			_lowercase   =True
			self.check_hidden_states_output(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  (padded_height, padded_width)       )


	@slow
	def __lowerCAmelCase      (   self       ):
		for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			_lowercase   =SwinModel.from_pretrained(lowerCAmelCase_       )
			self.assertIsNotNone(lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   ,  _lowercase   =self.model_tester.prepare_config_and_inputs_for_common()

		_lowercase   =_config_zero_init(lowerCAmelCase_       )
		for model_class in self.all_model_classes:
			_lowercase   =model_class(config=lowerCAmelCase_       )
			for name, param in model.named_parameters():
				if "embeddings" not in name and param.requires_grad:
					self.assertIn(
					    ((param.data.mean() * 1e9).round() / 1e9).item()       ,  [0.0, 1.0]       ,  msg=F'''Parameter {name} of model {model_class} seems not properly initialized'''       ,  )

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



	"""simple docstring"""



	@cached_property
	def __lowerCAmelCase      (   self       ):
		return (
		    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"       )
		    if is_vision_available()
		    else None
		)


	@slow
	def __lowerCAmelCase      (   self       ):
		_lowercase   =SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"       ).to(lowerCAmelCase_       )
		_lowercase   =self.default_image_processor

		_lowercase   =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
		_lowercase   =image_processor(images=lowerCAmelCase_       ,  return_tensors="pt"       ).to(lowerCAmelCase_       )

		# forward pass
		with torch.no_grad():
			_lowercase   =model(**lowerCAmelCase_       )

		# verify the logits
		_lowercase   =torch.Size((1, 1000)       )
		self.assertEqual(outputs.logits.shape       ,  lowerCAmelCase_       )
		_lowercase   =torch.tensor([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]       ).to(lowerCAmelCase_       )
		self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,  lowerCAmelCase_       ,  atol=1e-4       )       )

@require_torch
class 	_a  (							unittest.TestCase							,		lowerCamelCase_					):



	"""simple docstring"""



	__SCREAMING_SNAKE_CASE              =					(SwinBackbone,) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE              =					SwinConfig


	def __lowerCAmelCase      (   self       ):
		_lowercase   =SwinModelTester(self       )





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


lowerCAmelCase__  	=      logging.getLogger(__name__)

def 	__lowerCamelCase							(				__a    : Optional[Any]		)      ->       Optional[int]:
	_lowercase   =p * torch.log(__a		)
	_lowercase   =0
	return -plogp.sum(dim=-1		)

def 	__lowerCamelCase							(				__a    : Optional[Any]		)      ->       Tuple:
	logger.info("lv, h >\t" + "\t".join(f'''{x + 1}''' for x in range(len(__a		)		)		)		)
	for row in range(len(__a		)		):
		if tensor.dtype != torch.long:
			logger.info(f'''layer {row + 1}:\t''' + "\t".join(f'''{x:.5f}''' for x in tensor[row].cpu().data		)		)
		else:
			logger.info(f'''layer {row + 1}:\t''' + "\t".join(f'''{x:d}''' for x in tensor[row].cpu().data		)		)

def 	__lowerCamelCase							(				__a    : Optional[Any]      ,							__a    : Dict      ,							__a    : Any      ,							__a    : Any=True      ,							__a    : Optional[Any]=True      ,							__a    : Union[str, Any]=None      ,							__a    : Union[str, Any]=False		)      ->       Union[str, Any]:
	_lowercase   ,  _lowercase   =model.config.num_hidden_layers, model.config.num_attention_heads
	_lowercase   =torch.zeros(__a      ,							__a		).to(args.device		)
	_lowercase   =torch.zeros(__a      ,							__a		).to(args.device		)

	if head_mask is None:
		_lowercase   =torch.ones(__a      ,							__a		).to(args.device		)

	head_mask.requires_grad_(requires_grad=__a		)
	# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
	if actually_pruned:
		_lowercase   =None

	_lowercase   =None
	_lowercase   =None
	_lowercase   =0.0

	for step, inputs in enumerate(tqdm(__a      ,							desc="Iteration"      ,							disable=args.local_rank not in [-1, 0]		)		):
		for k, v in inputs.items():
			_lowercase   =v.to(args.device		)

		# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
		_lowercase   =model(**__a      ,							head_mask=__a		)
		_lowercase   ,  _lowercase   ,  _lowercase   =(
		    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(__a		):
				_lowercase   =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   =logits.detach().cpu().numpy()
			_lowercase   =inputs["labels"].detach().cpu().numpy()
		else:
			_lowercase   =np.append(__a      ,							logits.detach().cpu().numpy()      ,							axis=0		)
			_lowercase   =np.append(__a      ,							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   =2
		_lowercase   =torch.pow(torch.pow(__a      ,							__a		).sum(-1		)      ,							1 / exponent		)
		head_importance /= norm_by_layer.unsqueeze(-1		) + 1E-20

	if not args.dont_normalize_global_importance:
		_lowercase   =(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(__a		)
	logger.info("Head importance scores"		)
	print_ad_tensor(__a		)
	logger.info("Head ranked by importance scores"		)
	_lowercase   =torch.zeros(head_importance.numel()      ,							dtype=torch.long      ,							device=args.device		)
	_lowercase   =torch.arange(
	    head_importance.numel()      ,							device=args.device		)
	_lowercase   =head_ranks.view_as(__a		)
	print_ad_tensor(__a		)

	return attn_entropy, head_importance, preds, labels

def 	__lowerCamelCase							(				__a    : str      ,							__a    : int      ,							__a    : Optional[Any]		)      ->       Union[str, Any]:
	_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =compute_heads_importance(__a      ,							__a      ,							__a      ,							compute_entropy=__a		)
	_lowercase   =np.argmax(__a      ,							axis=1		) if args.output_mode == "classification" else np.squeeze(__a		)
	_lowercase   =glue_compute_metrics(args.task_name      ,							__a      ,							__a		)[args.metric_name]
	logger.info("Pruning: original score: %f, threshold: %f"      ,							__a      ,							original_score * args.masking_threshold		)

	_lowercase   =torch.ones_like(__a		)
	_lowercase   =max(1      ,							int(new_head_mask.numel() * args.masking_amount		)		)

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

		if len(__a		) <= num_to_mask:
			break

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

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

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

	return head_mask

def 	__lowerCamelCase							(				__a    : Tuple      ,							__a    : Tuple      ,							__a    : Optional[Any]      ,							__a    : Union[str, Any]		)      ->       List[str]:
	_lowercase   =datetime.now()
	_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =compute_heads_importance(
	    __a      ,							__a      ,							__a      ,							compute_entropy=__a      ,							compute_importance=__a      ,							head_mask=__a		)
	_lowercase   =np.argmax(__a      ,							axis=1		) if args.output_mode == "classification" else np.squeeze(__a		)
	_lowercase   =glue_compute_metrics(args.task_name      ,							__a      ,							__a		)[args.metric_name]
	_lowercase   =datetime.now() - before_time

	_lowercase   =sum(p.numel() for p in model.parameters()		)
	_lowercase   ={
	    layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__a		)		)
	}

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

	_lowercase   =datetime.now()
	_lowercase   ,  _lowercase   ,  _lowercase   ,  _lowercase   =compute_heads_importance(
	    __a      ,							__a      ,							__a      ,							compute_entropy=__a      ,							compute_importance=__a      ,							head_mask=__a      ,							actually_pruned=__a      ,							)
	_lowercase   =np.argmax(__a      ,							axis=1		) if args.output_mode == "classification" else np.squeeze(__a		)
	_lowercase   =glue_compute_metrics(args.task_name      ,							__a      ,							__a		)[args.metric_name]
	_lowercase   =datetime.now() - before_time

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

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

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

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

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

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

	parser.add_argument("--seed"      ,							type=__a      ,							default=42		)
	parser.add_argument("--local_rank"      ,							type=__a      ,							default=-1      ,							help="local_rank for distributed training on gpus"		)
	parser.add_argument("--no_cuda"      ,							action="store_true"      ,							help="Whether not to use CUDA when available"		)
	parser.add_argument("--server_ip"      ,							type=__a      ,							default=""      ,							help="Can be used for distant debugging."		)
	parser.add_argument("--server_port"      ,							type=__a      ,							default=""      ,							help="Can be used for distant debugging."		)
	_lowercase   =parser.parse_args()

	if args.server_ip and args.server_port:
		# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
		import ptvsd

		print("Waiting for debugger attach"		)
		ptvsd.enable_attach(address=(args.server_ip, args.server_port)      ,							redirect_output=__a		)
		ptvsd.wait_for_attach()

	# Setup devices and distributed training
	if args.local_rank == -1 or args.no_cuda:
		_lowercase   =torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"		)
		_lowercase   =0 if args.no_cuda else torch.cuda.device_count()
	else:
		torch.cuda.set_device(args.local_rank		)
		_lowercase   =torch.device("cuda"      ,							args.local_rank		)
		_lowercase   =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   =args.task_name.lower()
	if args.task_name not in glue_processors:
		raise ValueError("Task not found: %s" % (args.task_name)		)
	_lowercase   =glue_processors[args.task_name]()
	_lowercase   =glue_output_modes[args.task_name]
	_lowercase   =processor.get_labels()
	_lowercase   =len(__a		)

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

	_lowercase   =AutoConfig.from_pretrained(
	    args.config_name if args.config_name else args.model_name_or_path      ,							num_labels=__a      ,							finetuning_task=args.task_name      ,							output_attentions=__a      ,							cache_dir=args.cache_dir      ,							)
	_lowercase   =AutoTokenizer.from_pretrained(
	    args.tokenizer_name if args.tokenizer_name else args.model_name_or_path      ,							cache_dir=args.cache_dir      ,							)
	_lowercase   =AutoModelForSequenceClassification.from_pretrained(
	    args.model_name_or_path      ,							from_tf=bool(".ckpt" in args.model_name_or_path		)      ,							config=__a      ,							cache_dir=args.cache_dir      ,							)

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

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

	# Prepare dataset for the GLUE task
	_lowercase   =GlueDataset(__a      ,							tokenizer=__a      ,							mode="dev"		)
	if args.data_subset > 0:
		_lowercase   =Subset(__a      ,							list(range(min(args.data_subset      ,							len(__a		)		)		)		)		)
	_lowercase   =SequentialSampler(__a		) if args.local_rank == -1 else DistributedSampler(__a		)
	_lowercase   =DataLoader(
	    __a      ,							sampler=__a      ,							batch_size=args.batch_size      ,							collate_fn=__a		)

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

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


if __name__ == "__main__":
						main()




