



from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers.models.swin.modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
      )


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor







class       _snake_case				:






   '''simple docstring'''






   def __init__(    self:     Any  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     Optional[int]=13  ,lowerCamelCase_:     Dict=32  ,lowerCamelCase_:     List[str]=2  ,lowerCamelCase_:     str=3  ,lowerCamelCase_:     Dict=16  ,lowerCamelCase_:     Union[str, Any]=[1, 2, 1]  ,lowerCamelCase_:     Optional[int]=[2, 2, 4]  ,lowerCamelCase_:     Optional[Any]=2  ,lowerCamelCase_:     Union[str, Any]=2.0  ,lowerCamelCase_:     Optional[int]=True  ,lowerCamelCase_:     List[Any]=0.0  ,lowerCamelCase_:     int=0.0  ,lowerCamelCase_:     int=0.1  ,lowerCamelCase_:     Tuple="gelu"  ,lowerCamelCase_:     Dict=False  ,lowerCamelCase_:     str=True  ,lowerCamelCase_:     str=0.0_2  ,lowerCamelCase_:     Tuple=1e-5  ,lowerCamelCase_:     Optional[Any]=True  ,lowerCamelCase_:     Tuple=None  ,lowerCamelCase_:     Optional[int]=True  ,lowerCamelCase_:     List[Any]=10  ,lowerCamelCase_:     List[str]=8  ,)	->      Optional[Any]:
       UpperCAmelCase_     :       Any  		=					parent
       UpperCAmelCase_     :       List[Any]  		=					batch_size
       UpperCAmelCase_     :       Union[str, Any]  		=					image_size
       UpperCAmelCase_     :       List[str]  		=					patch_size
       UpperCAmelCase_     :       Any  		=					num_channels
       UpperCAmelCase_     :       int  		=					embed_dim
       UpperCAmelCase_     :       List[str]  		=					depths
       UpperCAmelCase_     :       Union[str, Any]  		=					num_heads
       UpperCAmelCase_     :       List[Any]  		=					window_size
       UpperCAmelCase_     :       Optional[int]  		=					mlp_ratio
       UpperCAmelCase_     :       Tuple  		=					qkv_bias
       UpperCAmelCase_     :       Union[str, Any]  		=					hidden_dropout_prob
       UpperCAmelCase_     :       List[Any]  		=					attention_probs_dropout_prob
       UpperCAmelCase_     :       Optional[Any]  		=					drop_path_rate
       UpperCAmelCase_     :       Optional[Any]  		=					hidden_act
       UpperCAmelCase_     :       Optional[Any]  		=					use_absolute_embeddings
       UpperCAmelCase_     :       Dict  		=					patch_norm
       UpperCAmelCase_     :       Any  		=					layer_norm_eps
       UpperCAmelCase_     :       Tuple  		=					initializer_range
       UpperCAmelCase_     :       Optional[Any]  		=					is_training
       UpperCAmelCase_     :       List[str]  		=					scope
       UpperCAmelCase_     :       List[str]  		=					use_labels
       UpperCAmelCase_     :       Optional[Any]  		=					type_sequence_label_size
       UpperCAmelCase_     :       Any  		=					encoder_stride


   def       A__    (    self:     Tuple							)	->      str:
       UpperCAmelCase_     :       Optional[int]  		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

       UpperCAmelCase_     :       Optional[Any]  		=					None
       if self.use_labels:
           UpperCAmelCase_     :       str  		=					ids_tensor([self.batch_size]  ,self.type_sequence_label_size							)

       UpperCAmelCase_     :       List[str]  		=					self.get_config()

       return config, pixel_values, labels


   def       A__    (    self:     Any							)	->      Union[str, Any]:
       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  ,)


   def       A__    (    self:     int  ,lowerCamelCase_:     Optional[int]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     int							)	->      Optional[Any]:
       UpperCAmelCase_     :       int  		=					TFSwinModel(config=lowerCAmelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					model(lowerCAmelCase_							)

       UpperCAmelCase_     :       List[Any]  		=					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
       UpperCAmelCase_     :       Optional[int]  		=					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       A__    (    self:     List[Any]  ,lowerCamelCase_:     Union[str, Any]  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     int							)	->      Optional[int]:
       UpperCAmelCase_     :       Optional[int]  		=					TFSwinForMaskedImageModeling(config=lowerCAmelCase_							)
       UpperCAmelCase_     :       Optional[int]  		=					model(lowerCAmelCase_							)
       self.parent.assertEqual(
           result.logits.shape  ,(self.batch_size, self.num_channels, self.image_size, self.image_size)							)

       # test greyscale images
       UpperCAmelCase_     :       str  		=					1
       UpperCAmelCase_     :       Any  		=					TFSwinForMaskedImageModeling(lowerCAmelCase_							)

       UpperCAmelCase_     :       List[str]  		=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       UpperCAmelCase_     :       List[Any]  		=					model(lowerCAmelCase_							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, 1, self.image_size, self.image_size)							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     Optional[Any]  ,lowerCamelCase_:     List[Any]  ,lowerCamelCase_:     int							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.type_sequence_label_size
       UpperCAmelCase_     :       Union[str, Any]  		=					TFSwinForImageClassification(lowerCAmelCase_							)
       UpperCAmelCase_     :       Dict  		=					model(lowerCAmelCase_  ,labels=lowerCAmelCase_							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)							)

       # test greyscale images
       UpperCAmelCase_     :       str  		=					1
       UpperCAmelCase_     :       str  		=					TFSwinForImageClassification(lowerCAmelCase_							)
       UpperCAmelCase_     :       Union[str, Any]  		=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
       UpperCAmelCase_     :       List[str]  		=					model(lowerCAmelCase_							)
       self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size)							)






   def       A__    (    self:     Optional[Any]							)	->      Optional[Any]:
       UpperCAmelCase_     :       Optional[Any]  		=					self.prepare_config_and_inputs()
       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       List[str]  		=					config_and_inputs
       UpperCAmelCase_     :       Optional[Any]  		=					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_tf
class       _snake_case				(     lowerCamelCase_ ,							lowerCamelCase_ ,							unittest.TestCase ):






   '''simple docstring'''






   A__      : Tuple       =						(
       (
           TFSwinModel,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   A__      : List[str]       =						(
       {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
       if is_tf_available()
       else {}
   )

   A__      : Dict       =						False
   A__      : List[Any]       =						False
   A__      : Optional[int]       =						False
   A__      : Optional[int]       =						False


   def       A__    (    self:     str							)	->      Optional[Any]:
       UpperCAmelCase_     :       Tuple  		=					TFSwinModelTester(self							)
       UpperCAmelCase_     :       Any  		=					ConfigTester(self  ,config_class=lowerCAmelCase_  ,embed_dim=37							)


   def       A__    (    self:     Dict							)	->      List[str]:
       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       A__    (    self:     Optional[int]							)	->      Optional[int]:
       return


   def       A__    (    self:     Any							)	->      Any:
       UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCAmelCase_							)


   def       A__    (    self:     Any							)	->      Any:
       UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase_							)


   def       A__    (    self:     Tuple							)	->      Any:
       UpperCAmelCase_     :       Any  		=					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       A__    (    self:     Optional[Any]							)	->      List[str]:
       pass


   def       A__    (    self:     Optional[int]							)	->      List[str]:
       UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Tuple  		=					model_class(lowerCAmelCase_							)
           self.assertIsInstance(model.get_input_embeddings()  ,tf.keras.layers.Layer							)
           UpperCAmelCase_     :       List[Any]  		=					model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(lowerCAmelCase_  ,tf.keras.layers.Dense							)							)


   def       A__    (    self:     List[Any]							)	->      Optional[int]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Dict  		=					model_class(lowerCAmelCase_							)
           UpperCAmelCase_     :       str  		=					inspect.signature(model.call							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           UpperCAmelCase_     :       Optional[int]  		=					[*signature.parameters.keys()]

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


   def       A__    (    self:     Any							)	->      Any:
       UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       int  		=					True

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       List[str]  		=					True
           UpperCAmelCase_     :       int  		=					False
           UpperCAmelCase_     :       List[Any]  		=					True
           UpperCAmelCase_     :       Union[str, Any]  		=					model_class(lowerCAmelCase_							)
           UpperCAmelCase_     :       Any  		=					model(**self._prepare_for_class(lowerCAmelCase_  ,lowerCAmelCase_							)							)
           UpperCAmelCase_     :       List[Any]  		=					outputs.attentions
           UpperCAmelCase_     :       Union[str, Any]  		=					len(self.model_tester.depths							)
           self.assertEqual(len(lowerCAmelCase_							)  ,lowerCAmelCase_							)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           UpperCAmelCase_     :       Optional[int]  		=					True
           UpperCAmelCase_     :       List[str]  		=					config.window_size**2
           UpperCAmelCase_     :       List[Any]  		=					model_class(lowerCAmelCase_							)
           UpperCAmelCase_     :       Union[str, Any]  		=					model(**self._prepare_for_class(lowerCAmelCase_  ,lowerCAmelCase_							)							)
           UpperCAmelCase_     :       Dict  		=					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]  ,)
           UpperCAmelCase_     :       List[str]  		=					len(lowerCAmelCase_							)

           # Check attention is always last and order is fine
           UpperCAmelCase_     :       Any  		=					True
           UpperCAmelCase_     :       int  		=					True
           UpperCAmelCase_     :       List[str]  		=					model_class(lowerCAmelCase_							)
           UpperCAmelCase_     :       List[str]  		=					model(**self._prepare_for_class(lowerCAmelCase_  ,lowerCAmelCase_							)							)

           if hasattr(self.model_tester  ,"""num_hidden_states_types"""							):
               UpperCAmelCase_     :       Union[str, Any]  		=					self.model_tester.num_hidden_states_types
           else:
               # also another +1 for reshaped_hidden_states
               UpperCAmelCase_     :       List[str]  		=					2
           self.assertEqual(out_len + added_hidden_states  ,len(lowerCAmelCase_							)							)

           UpperCAmelCase_     :       Any  		=					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       A__    (    self:     List[str]  ,lowerCamelCase_:     int  ,lowerCamelCase_:     Tuple  ,lowerCamelCase_:     int  ,lowerCamelCase_:     int							)	->      List[Any]:
       UpperCAmelCase_     :       List[Any]  		=					model_class(lowerCAmelCase_							)
       UpperCAmelCase_     :       str  		=					model(**self._prepare_for_class(lowerCAmelCase_  ,lowerCAmelCase_							)							)
       UpperCAmelCase_     :       List[Any]  		=					outputs.hidden_states

       UpperCAmelCase_     :       Tuple  		=					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
       UpperCAmelCase_     :       str  		=					to_atuple(config.patch_size							)

       UpperCAmelCase_     :       Tuple  		=					(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]  ,)

       UpperCAmelCase_     :       str  		=					outputs.reshaped_hidden_states
       self.assertEqual(len(lowerCAmelCase_							)  ,lowerCAmelCase_							)

       UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_     :       Optional[int]  		=					reshaped_hidden_states[0].shape

       UpperCAmelCase_     :       Union[str, Any]  		=					tf.reshape(reshaped_hidden_states[0]  ,(batch_size, num_channels, height * width)							)
       UpperCAmelCase_     :       Union[str, Any]  		=					tf.transpose(lowerCAmelCase_  ,(0, 2, 1)							)

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


   def       A__    (    self:     Optional[Any]							)	->      Any:
       UpperCAmelCase_		, UpperCAmelCase_     :       str  		=					self.model_tester.prepare_config_and_inputs_for_common()

       UpperCAmelCase_     :       Union[str, Any]  		=					to_atuple(self.model_tester.image_size							)

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       Tuple  		=					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"]
           UpperCAmelCase_     :       Union[str, Any]  		=					True

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


   def       A__    (    self:     List[Any]							)	->      Optional[Any]:
       UpperCAmelCase_		, UpperCAmelCase_     :       Optional[Any]  		=					self.model_tester.prepare_config_and_inputs_for_common()
       UpperCAmelCase_     :       List[Any]  		=					3

       UpperCAmelCase_     :       str  		=					to_atuple(self.model_tester.image_size							)
       UpperCAmelCase_     :       List[Any]  		=					to_atuple(config.patch_size							)

       UpperCAmelCase_     :       Union[str, Any]  		=					image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       UpperCAmelCase_     :       Union[str, Any]  		=					image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
           UpperCAmelCase_     :       List[str]  		=					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"]
           UpperCAmelCase_     :       List[str]  		=					True
           self.check_hidden_states_output(lowerCAmelCase_  ,lowerCAmelCase_  ,lowerCAmelCase_  ,(padded_height, padded_width)							)






   @slow
   def       A__    (    self:     List[Any]							)	->      Optional[Any]:
       for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCAmelCase_     :       Optional[int]  		=					TFSwinModel.from_pretrained(lowerCAmelCase_							)
           self.assertIsNotNone(lowerCAmelCase_							)







@require_vision
@require_tf
class       _snake_case				(     unittest.TestCase ):






   '''simple docstring'''






   @cached_property
   def       A__    (    self:     Optional[int]							)	->      Optional[int]:
       return (
           AutoImageProcessor.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							)
           if is_vision_available()
           else None
       )






   @slow
   def       A__    (    self:     Any							)	->      Union[str, Any]:
       UpperCAmelCase_     :       Union[str, Any]  		=					TFSwinForImageClassification.from_pretrained("""microsoft/swin-tiny-patch4-window7-224"""							)
       UpperCAmelCase_     :       Optional[int]  		=					self.default_image_processor

       UpperCAmelCase_     :       Tuple  		=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
       UpperCAmelCase_     :       Optional[int]  		=					image_processor(images=lowerCAmelCase_  ,return_tensors="""tf"""							)

       # forward pass
       UpperCAmelCase_     :       Optional[Any]  		=					model(lowerCAmelCase_							)

       # verify the logits
       UpperCAmelCase_     :       Any  		=					tf.TensorShape((1, 1000)							)
       self.assertEqual(outputs.logits.shape  ,lowerCAmelCase_							)
       UpperCAmelCase_     :       List[Any]  		=					tf.constant([-0.0_9_4_8, -0.6_4_5_4, -0.0_9_2_1]							)
       self.assertTrue(np.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, 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()




