





"""simple docstring"""





import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

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


if is_torch_available():
       import torch

       from transformers import (
           FlaubertForMultipleChoice,
           FlaubertForQuestionAnswering,
           FlaubertForQuestionAnsweringSimple,
           FlaubertForSequenceClassification,
           FlaubertForTokenClassification,
           FlaubertModel,
           FlaubertWithLMHeadModel,
       )
       from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		lowercase					(					_UpperCAmelCase					):





      def __init__(       self      ,						lowercase      ,						lowercase=13      ,						lowercase=7      ,						lowercase=True      ,						lowercase=True      ,						lowercase=True      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						lowercase=2      ,						lowercase=99      ,						lowercase=0      ,						lowercase=32      ,						lowercase=5      ,						lowercase=4      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=512      ,						lowercase=12      ,						lowercase=2      ,						lowercase=0.02      ,						lowercase=3      ,						lowercase=4      ,						lowercase="last"      ,						lowercase=None      ,						lowercase=None      ,						)		->     int:
            lowerCAmelCase  						=       parent
            lowerCAmelCase  						=       batch_size
            lowerCAmelCase  						=       seq_length
            lowerCAmelCase  						=       is_training
            lowerCAmelCase  						=       use_input_lengths
            lowerCAmelCase  						=       use_token_type_ids
            lowerCAmelCase  						=       use_labels
            lowerCAmelCase  						=       gelu_activation
            lowerCAmelCase  						=       sinusoidal_embeddings
            lowerCAmelCase  						=       causal
            lowerCAmelCase  						=       asm
            lowerCAmelCase  						=       n_langs
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_special
            lowerCAmelCase  						=       hidden_size
            lowerCAmelCase  						=       num_hidden_layers
            lowerCAmelCase  						=       num_attention_heads
            lowerCAmelCase  						=       hidden_dropout_prob
            lowerCAmelCase  						=       attention_probs_dropout_prob
            lowerCAmelCase  						=       max_position_embeddings
            lowerCAmelCase  						=       type_vocab_size
            lowerCAmelCase  						=       type_sequence_label_size
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       num_labels
            lowerCAmelCase  						=       num_choices
            lowerCAmelCase  						=       summary_type
            lowerCAmelCase  						=       use_proj
            lowerCAmelCase  						=       scope





      def     _snake_case			(       self )		->     int:
            lowerCAmelCase  						=       ids_tensor([self.batch_size, self.seq_length]      ,						self.vocab_size )
            lowerCAmelCase  						=       random_attention_mask([self.batch_size, self.seq_length] )

            lowerCAmelCase  						=       None
            if self.use_input_lengths:
                  lowerCAmelCase  						=       (
                      ids_tensor([self.batch_size]      ,						vocab_size=2 ) + self.seq_length - 2
                  )  # small variation of seq_length

            lowerCAmelCase  						=       None
            if self.use_token_type_ids:
                  lowerCAmelCase  						=       ids_tensor([self.batch_size, self.seq_length]      ,						self.n_langs )

            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       None
            lowerCAmelCase  						=       None
            if self.use_labels:
                  lowerCAmelCase  						=       ids_tensor([self.batch_size]      ,						self.type_sequence_label_size )
                  lowerCAmelCase  						=       ids_tensor([self.batch_size, self.seq_length]      ,						self.num_labels )
                  lowerCAmelCase  						=       ids_tensor([self.batch_size]      ,						2 ).float()
                  lowerCAmelCase  						=       ids_tensor([self.batch_size]      ,						self.num_choices )

            lowerCAmelCase  						=       self.get_config()

            return (
                config,
                input_ids,
                token_type_ids,
                input_lengths,
                sequence_labels,
                token_labels,
                is_impossible_labels,
                choice_labels,
                input_mask,
            )





      def     _snake_case			(       self )		->     List[Any]:
            return FlaubertConfig(
                vocab_size=self.vocab_size      ,						n_special=self.n_special      ,						emb_dim=self.hidden_size      ,						n_layers=self.num_hidden_layers      ,						n_heads=self.num_attention_heads      ,						dropout=self.hidden_dropout_prob      ,						attention_dropout=self.attention_probs_dropout_prob      ,						gelu_activation=self.gelu_activation      ,						sinusoidal_embeddings=self.sinusoidal_embeddings      ,						asm=self.asm      ,						causal=self.causal      ,						n_langs=self.n_langs      ,						max_position_embeddings=self.max_position_embeddings      ,						initializer_range=self.initializer_range      ,						summary_type=self.summary_type      ,						use_proj=self.use_proj      ,						)





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       FlaubertModel(config=lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       model(lowercase      ,						lengths=lowercase      ,						langs=lowercase )
            lowerCAmelCase  						=       model(lowercase      ,						langs=lowercase )
            lowerCAmelCase  						=       model(lowercase )
            self.parent.assertEqual(result.last_hidden_state.shape      ,						(self.batch_size, self.seq_length, self.hidden_size) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Tuple:
            lowerCAmelCase  						=       FlaubertWithLMHeadModel(lowercase )
            model.to(lowercase )
            model.eval()

            lowerCAmelCase  						=       model(lowercase      ,						token_type_ids=lowercase      ,						labels=lowercase )
            self.parent.assertEqual(result.loss.shape      ,						() )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.vocab_size) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     str:
            lowerCAmelCase  						=       FlaubertForQuestionAnsweringSimple(lowercase )
            model.to(lowercase )
            model.eval()

            lowerCAmelCase  						=       model(lowercase )

            lowerCAmelCase  						=       model(lowercase      ,						start_positions=lowercase      ,						end_positions=lowercase )
            self.parent.assertEqual(result.start_logits.shape      ,						(self.batch_size, self.seq_length) )
            self.parent.assertEqual(result.end_logits.shape      ,						(self.batch_size, self.seq_length) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Dict:
            lowerCAmelCase  						=       FlaubertForQuestionAnswering(lowercase )
            model.to(lowercase )
            model.eval()

            lowerCAmelCase  						=       model(lowercase )

            lowerCAmelCase  						=       model(
                lowercase      ,						start_positions=lowercase      ,						end_positions=lowercase      ,						cls_index=lowercase      ,						is_impossible=lowercase      ,						p_mask=lowercase      ,						)

            lowerCAmelCase  						=       model(
                lowercase      ,						start_positions=lowercase      ,						end_positions=lowercase      ,						cls_index=lowercase      ,						is_impossible=lowercase      ,						)

            ((lowerCAmelCase)						,		)  						=       result_with_labels.to_tuple()

            lowerCAmelCase  						=       model(lowercase      ,						start_positions=lowercase      ,						end_positions=lowercase )

            ((lowerCAmelCase)						,		)  						=       result_with_labels.to_tuple()

            self.parent.assertEqual(result_with_labels.loss.shape      ,						() )
            self.parent.assertEqual(result.start_top_log_probs.shape      ,						(self.batch_size, model.config.start_n_top) )
            self.parent.assertEqual(result.start_top_index.shape      ,						(self.batch_size, model.config.start_n_top) )
            self.parent.assertEqual(
                result.end_top_log_probs.shape      ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
            self.parent.assertEqual(
                result.end_top_index.shape      ,						(self.batch_size, model.config.start_n_top * model.config.end_n_top) )
            self.parent.assertEqual(result.cls_logits.shape      ,						(self.batch_size,) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     int:
            lowerCAmelCase  						=       FlaubertForSequenceClassification(lowercase )
            model.to(lowercase )
            model.eval()

            lowerCAmelCase  						=       model(lowercase )
            lowerCAmelCase  						=       model(lowercase      ,						labels=lowercase )

            self.parent.assertEqual(result.loss.shape      ,						() )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.type_sequence_label_size) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     int:
            lowerCAmelCase  						=       self.num_labels
            lowerCAmelCase  						=       FlaubertForTokenClassification(lowercase )
            model.to(lowercase )
            model.eval()

            lowerCAmelCase  						=       model(lowercase      ,						attention_mask=lowercase      ,						labels=lowercase )
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.seq_length, self.num_labels) )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						)		->     Tuple:
            lowerCAmelCase  						=       self.num_choices
            lowerCAmelCase  						=       FlaubertForMultipleChoice(config=lowercase )
            model.to(lowercase )
            model.eval()
            lowerCAmelCase  						=       input_ids.unsqueeze(1 ).expand(-1      ,						self.num_choices      ,						-1 ).contiguous()
            lowerCAmelCase  						=       token_type_ids.unsqueeze(1 ).expand(-1      ,						self.num_choices      ,						-1 ).contiguous()
            lowerCAmelCase  						=       input_mask.unsqueeze(1 ).expand(-1      ,						self.num_choices      ,						-1 ).contiguous()
            lowerCAmelCase  						=       model(
                lowercase      ,						attention_mask=lowercase      ,						token_type_ids=lowercase      ,						labels=lowercase      ,						)
            self.parent.assertEqual(result.logits.shape      ,						(self.batch_size, self.num_choices) )





      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase  						=       self.prepare_config_and_inputs()
            (
                (
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		(
                lowerCAmelCase
            )						,		
            )  						=       config_and_inputs
            lowerCAmelCase  						=       {
                """input_ids""": input_ids,
                """token_type_ids""": token_type_ids,
                """lengths""": input_lengths,
                """attention_mask""": input_mask,
            }
            return config, inputs_dict







@require_torch
class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase     ,  unittest.TestCase					):
      _SCREAMING_SNAKE_CASE					=      (
          (
              FlaubertModel,
              FlaubertWithLMHeadModel,
              FlaubertForQuestionAnswering,
              FlaubertForQuestionAnsweringSimple,
              FlaubertForSequenceClassification,
              FlaubertForTokenClassification,
              FlaubertForMultipleChoice,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE					=      (
          {
              'feature-extraction': FlaubertModel,
              'fill-mask': FlaubertWithLMHeadModel,
              'question-answering': FlaubertForQuestionAnsweringSimple,
              'text-classification': FlaubertForSequenceClassification,
              'token-classification': FlaubertForTokenClassification,
              'zero-shot': FlaubertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase      ,						lowercase )		->     Optional[int]:
            if (
                pipeline_test_casse_name == "QAPipelineTests"
                and tokenizer_name is not None
                and not tokenizer_name.endswith("""Fast""" )
            ):
                  # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
                  # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
                  # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
                  return True

            return False





      def     _snake_case			(       self      ,						lowercase      ,						lowercase      ,						lowercase=False )		->     Optional[Any]:
            lowerCAmelCase  						=       super()._prepare_for_class(lowercase      ,						lowercase      ,						return_labels=lowercase )

            if return_labels:
                  if model_class.__name__ == "FlaubertForQuestionAnswering":
                        lowerCAmelCase  						=       torch.zeros(
                            self.model_tester.batch_size      ,						dtype=torch.long      ,						device=lowercase )
                        lowerCAmelCase  						=       torch.zeros(
                            self.model_tester.batch_size      ,						dtype=torch.long      ,						device=lowercase )

            return inputs_dict





      def     _snake_case			(       self )		->     List[str]:
            lowerCAmelCase  						=       FlaubertModelTester(self )
            lowerCAmelCase  						=       ConfigTester(self      ,						config_class=lowercase      ,						emb_dim=37 )





      def     _snake_case			(       self )		->     Tuple:
            self.config_tester.run_common_tests()





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_model(*lowercase )





      def     _snake_case			(       self )		->     Tuple:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_lm_head(*lowercase )





      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_simple_qa(*lowercase )





      def     _snake_case			(       self )		->     Union[str, Any]:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_qa(*lowercase )





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase )





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_token_classif(*lowercase )





      def     _snake_case			(       self )		->     Any:
            lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_flaubert_multiple_choice(*lowercase )





      @slow
      def     _snake_case			(       self )		->     Tuple:
            for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  lowerCAmelCase  						=       FlaubertModel.from_pretrained(lowercase )
                  self.assertIsNotNone(lowercase )





      @slow
      @require_torch_gpu
      def     _snake_case			(       self )		->     List[Any]:
            lowerCAmelCase						,		lowerCAmelCase  						=       self.model_tester.prepare_config_and_inputs_for_common()
            for model_class in self.all_model_classes:
                  # FlauBertForMultipleChoice behaves incorrectly in JIT environments.
                  if model_class == FlaubertForMultipleChoice:
                        return

                  lowerCAmelCase  						=       True
                  lowerCAmelCase  						=       model_class(config=lowercase )

                  lowerCAmelCase  						=       self._prepare_for_class(lowercase      ,						lowercase )
                  lowerCAmelCase  						=       torch.jit.trace(
                      lowercase      ,						(inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )

                  with tempfile.TemporaryDirectory() as tmp:
                        torch.jit.save(lowercase      ,						os.path.join(lowercase      ,						"""traced_model.pt""" ) )
                        lowerCAmelCase  						=       torch.jit.load(os.path.join(lowercase      ,						"""traced_model.pt""" )      ,						map_location=lowercase )
                        loaded(inputs_dict["""input_ids"""].to(lowercase )      ,						inputs_dict["""attention_mask"""].to(lowercase ) )







@require_torch
class 		lowercase					(					unittest.TestCase					):





      @slow
      def     _snake_case			(       self )		->     Optional[int]:
            lowerCAmelCase  						=       FlaubertModel.from_pretrained("""flaubert/flaubert_base_cased""" )
            lowerCAmelCase  						=       torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
            with torch.no_grad():
                  lowerCAmelCase  						=       model(lowercase )[0]
            lowerCAmelCase  						=       torch.Size((1, 11, 768) )
            self.assertEqual(output.shape      ,						lowercase )
            lowerCAmelCase  						=       torch.tensor(
                [[[-2.6_251, -1.4_298, -0.0_227], [-2.8_510, -1.6_387, 0.2_258], [-2.8_114, -1.1_832, -0.3_066]]] )

            self.assertTrue(torch.allclose(output[:, :3, :3]      ,						lowercase      ,						atol=1e-4 ) )











import argparse
import logging
import os
from datetime import datetime

import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm

from transformers import GPTaLMHeadModel


a_   :Optional[Any]        =      logging.getLogger(__name__)
def        lowercase_							(A : List[Any]		,					A : List[Any]      ):
       # save results
       if os.path.exists(A      ):
              if os.path.exists(os.path.join(A		,					'config.json'      )      ) and os.path.isfile(
                  os.path.join(A		,					'config.json'      )      ):
                     os.remove(os.path.join(A		,					'config.json'      )      )
              if os.path.exists(os.path.join(A		,					'pytorch_model.bin'      )      ) and os.path.isfile(
                  os.path.join(A		,					'pytorch_model.bin'      )      ):
                     os.remove(os.path.join(A		,					'pytorch_model.bin'      )      )
       else:
              os.makedirs(A      )
       model.save_pretrained(A      )
def        lowercase_							(A : Any		,					A : Optional[Any]=False      ):
       snake_case__							:	str   	=	2
       if unlogit:
              snake_case__							:	Dict   	=	torch.pow(A		,					A      )
       snake_case__							:	Any   	=	p * torch.log(A      )
       snake_case__							:	Tuple   	=	0
       return -plogp.sum(dim=-1      )
def        lowercase_							(A : List[str]      ):
       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        lowercase_							(A : Tuple		,					A : Optional[Any]		,					A : str		,					A : int=True		,					A : Optional[int]=True		,					A : Any=None		,					A : int=False      ):
       snake_case__	,				snake_case__							:	Optional[Any]   	=	model.config.num_hidden_layers, model.config.num_attention_heads
       snake_case__							:	int   	=	torch.zeros(A		,					A      ).to(args.device      )
       snake_case__							:	Any   	=	torch.zeros(A		,					A      ).to(args.device      )

       if head_mask is None:
              snake_case__							:	Dict   	=	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:
              snake_case__							:	Optional[int]   	=	None

       snake_case__							:	List[Any]   	=	0.0
       snake_case__							:	str   	=	0.0
       for step, inputs in enumerate(tqdm(A		,					desc='Iteration'		,					disable=args.local_rank not in [-1, 0]      )      ):
              snake_case__							:	Union[str, Any]   	=	tuple(t.to(args.device      ) for t in inputs      )
              ((snake_case__)	,				)							:	Optional[Any]   	=	inputs

              # Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
              snake_case__							:	Union[str, Any]   	=	model(A		,					labels=A		,					head_mask=A      )
              #  (loss), lm_logits, presents, (all hidden_states), (attentions)
              snake_case__	,				snake_case__	,				snake_case__							:	Dict   	=	(
                  outputs[0],
                  outputs[1],
                  outputs[-1],
              )  # Loss and logits are the first, attention the last
              loss.backward()  # Backpropagate to populate the gradients in the head mask
              total_loss += loss.detach().cpu().numpy()
              if compute_entropy:
                     for layer, attn in enumerate(A      ):
                            snake_case__							:	Optional[Any]   	=	entropy(attn.detach()		,					A      )
                            attn_entropy[layer] += masked_entropy.sum(-1      ).sum(0      ).sum(0      ).detach()

              if compute_importance:
                     head_importance += head_mask.grad.abs().detach()
              tot_tokens += torch.ones_like(A      ).float().detach().sum().data

       # Normalize
       attn_entropy /= tot_tokens
       head_importance /= tot_tokens
       # Layerwise importance normalization
       if not args.dont_normalize_importance_by_layer:
              snake_case__							:	Union[str, Any]   	=	2
              snake_case__							:	List[Any]   	=	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:
              snake_case__							:	Tuple   	=	(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

       # Print matrices
       if compute_entropy:
              logger.info('Attention entropies'      )
              print_ad_tensor(A      )
       if compute_importance:
              logger.info('Head importance scores'      )
              print_ad_tensor(A      )
       logger.info('Head ranked by importance scores'      )
       snake_case__							:	Tuple   	=	torch.zeros(head_importance.numel()		,					dtype=torch.long		,					device=args.device      )
       snake_case__							:	Union[str, Any]   	=	torch.arange(
           head_importance.numel()		,					device=args.device      )
       snake_case__							:	str   	=	head_ranks.view_as(A      )
       print_ad_tensor(A      )
       return attn_entropy, head_importance, total_loss
def        lowercase_							(A : Optional[int]		,					A : Dict		,					A : Optional[int]      ):
       snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	compute_heads_importance(A		,					A		,					A		,					compute_entropy=A      )
       snake_case__							:	Tuple   	=	1 / loss  # instead of downsteam score use the LM loss
       logger.info('Pruning: original score: %f, threshold: %f'		,					A		,					original_score * args.masking_threshold      )

       snake_case__							:	Optional[Any]   	=	torch.ones_like(A      )
       snake_case__							:	Union[str, Any]   	=	max(1		,					int(new_head_mask.numel() * args.masking_amount      )      )

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

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

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

              # Compute metric and head importance again
              snake_case__	,				snake_case__	,				snake_case__							:	Any   	=	compute_heads_importance(
                  A		,					A		,					A		,					compute_entropy=A		,					head_mask=A      )
              snake_case__							:	Dict   	=	1 / loss
              logger.info(
                  'Masking: current score: %f, remaining heads %d (%.1f percents)'		,					A		,					new_head_mask.sum()		,					new_head_mask.sum() / new_head_mask.numel() * 1_0_0		,					)

       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        lowercase_							(A : List[str]		,					A : Tuple		,					A : Optional[Any]		,					A : int      ):
       snake_case__							:	Any   	=	datetime.now()
       snake_case__	,				snake_case__	,				snake_case__							:	str   	=	compute_heads_importance(
           A		,					A		,					A		,					compute_entropy=A		,					compute_importance=A		,					head_mask=A      )
       snake_case__							:	Tuple   	=	1 / loss
       snake_case__							:	Dict   	=	datetime.now() - before_time

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

       for k, v in heads_to_prune.items():
              if isinstance(A		,					A      ):
                     snake_case__							:	Any   	=	[
                         v,
                     ]

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

       snake_case__							:	Tuple   	=	datetime.now()
       snake_case__	,				snake_case__	,				snake_case__							:	Dict   	=	compute_heads_importance(
           A		,					A		,					A		,					compute_entropy=A		,					compute_importance=A		,					head_mask=A		,					actually_pruned=A		,					)

       snake_case__							:	Any   	=	1 / loss
       snake_case__							:	int   	=	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 * 1_0_0		,					)
       logger.info('Pruning: score with masking: %f score with pruning: %f'		,					A		,					A      )
       logger.info('Pruning: speed ratio (original timing / new timing): %f percents'		,					original_time / new_time * 1_0_0      )
       save_model(A		,					args.output_dir      )
def        lowercase_							():
       snake_case__							:	str   	=	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(
           '--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 s3'		,					)
       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=1_2_8		,					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=4_2      )
       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.'      )
       snake_case__							:	Optional[int]   	=	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:
              snake_case__							:	List[Any]   	=	torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'      )
              snake_case__							:	Optional[Any]   	=	0 if args.no_cuda else torch.cuda.device_count()
       else:
              torch.cuda.set_device(args.local_rank      )
              snake_case__							:	int   	=	torch.device('cuda'		,					args.local_rank      )
              snake_case__							:	List[str]   	=	1
              torch.distributed.init_process_group(backend='nccl'      )  # Initializes the distributed backend

       # Setup logging
       logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN      )
       logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device		,					args.n_gpu		,					bool(args.local_rank != -1      )      )      )

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

       # Distributed and parallel training
       model.to(args.device      )
       if args.local_rank != -1:
              snake_case__							:	List[str]   	=	nn.parallel.DistributedDataParallel(
                  A		,					device_ids=[args.local_rank]		,					output_device=args.local_rank		,					find_unused_parameters=A      )
       elif args.n_gpu > 1:
              snake_case__							:	Optional[int]   	=	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
       snake_case__							:	Optional[Any]   	=	np.concatenate(
           [
               np.loadtxt(args.data_dir		,					dtype=np.intaa      ),
           ]      )
       snake_case__							:	List[str]   	=	(torch.from_numpy(A      ),)
       snake_case__							:	int   	=	TensorDataset(*A      )
       snake_case__							:	Union[str, Any]   	=	RandomSampler(A      )
       snake_case__							:	Any   	=	DataLoader(A		,					sampler=A		,					batch_size=args.batch_size      )

       # 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:
              snake_case__							:	Dict   	=	mask_heads(A		,					A		,					A      )
              prune_heads(A		,					A		,					A		,					A      )


if __name__ == "__main__":
 main()






