



import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
     GPTNeoXForCausalLM,
     GPTNeoXForQuestionAnswering,
     GPTNeoXForSequenceClassification,
     GPTNeoXForTokenClassification,
     GPTNeoXModel,
 )




class 					snake_case__						:






     """simple docstring"""


     def __init__(       self  :  int, _snake_case  :  str, _snake_case  :  List[str]=1_3, _snake_case  :  Union[str, Any]=7, _snake_case  :  Tuple=True, _snake_case  :  str=True, _snake_case  :  List[Any]=True, _snake_case  :  List[Any]=True, _snake_case  :  List[Any]=9_9, _snake_case  :  Any=6_4, _snake_case  :  Optional[Any]=5, _snake_case  :  Any=4, _snake_case  :  Optional[int]=3_7, _snake_case  :  Tuple="gelu", _snake_case  :  Any=0.1, _snake_case  :  Optional[Any]=0.1, _snake_case  :  Any=5_1_2, _snake_case  :  Optional[Any]=1_6, _snake_case  :  List[str]=2, _snake_case  :  Dict=0.0_2, _snake_case  :  Dict=3, _snake_case  :  int=4, _snake_case  :  Optional[int]=None, )   ->Any:
            snake_case__							:	List[Any]   	=	parent
            snake_case__							:	Tuple   	=	batch_size
            snake_case__							:	Optional[int]   	=	seq_length
            snake_case__							:	List[str]   	=	is_training
            snake_case__							:	Union[str, Any]   	=	use_input_mask
            snake_case__							:	Tuple   	=	use_token_type_ids
            snake_case__							:	Union[str, Any]   	=	use_labels
            snake_case__							:	Union[str, Any]   	=	vocab_size
            snake_case__							:	List[str]   	=	hidden_size
            snake_case__							:	Optional[Any]   	=	num_hidden_layers
            snake_case__							:	Tuple   	=	num_attention_heads
            snake_case__							:	Optional[Any]   	=	intermediate_size
            snake_case__							:	Optional[Any]   	=	hidden_act
            snake_case__							:	Tuple   	=	hidden_dropout_prob
            snake_case__							:	Tuple   	=	attention_probs_dropout_prob
            snake_case__							:	Union[str, Any]   	=	max_position_embeddings
            snake_case__							:	int   	=	type_vocab_size
            snake_case__							:	List[Any]   	=	type_sequence_label_size
            snake_case__							:	Any   	=	initializer_range
            snake_case__							:	str   	=	num_labels
            snake_case__							:	str   	=	num_choices
            snake_case__							:	List[Any]   	=	scope
            snake_case__							:	Any   	=	vocab_size - 1






     def    lowercase_   (       self  :  int					)   ->int:
            snake_case__							:	List[str]   	=	ids_tensor([self.batch_size, self.seq_length], self.vocab_size					)

            snake_case__							:	int   	=	None
            if self.use_input_mask:
                   snake_case__							:	Union[str, Any]   	=	random_attention_mask([self.batch_size, self.seq_length]					)

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

            snake_case__							:	Tuple   	=	self.get_config()

            return config, input_ids, input_mask, token_labels






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            return GPTNeoXConfig(
                vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=_snake_case, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, )






     def    lowercase_   (       self  :  Optional[Any]					)   ->Optional[Any]:
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	List[str]   	=	self.prepare_config_and_inputs()

            snake_case__							:	List[Any]   	=	True

            return config, input_ids, input_mask, token_labels






     def    lowercase_   (       self  :  Any, _snake_case  :  List[str], _snake_case  :  Optional[Any], _snake_case  :  Optional[Any]					)   ->List[str]:
            snake_case__							:	Any   	=	GPTNeoXModel(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[int]   	=	model(_snake_case, attention_mask=_snake_case					)
            snake_case__							:	List[str]   	=	model(_snake_case					)
            self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)					)






     def    lowercase_   (       self  :  Union[str, Any], _snake_case  :  Optional[Any], _snake_case  :  Tuple, _snake_case  :  List[str]					)   ->Optional[Any]:
            snake_case__							:	Any   	=	True
            snake_case__							:	Union[str, Any]   	=	GPTNeoXModel(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Any   	=	model(_snake_case, attention_mask=_snake_case					)
            self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)					)






     def    lowercase_   (       self  :  int, _snake_case  :  int, _snake_case  :  List[Any], _snake_case  :  List[str], _snake_case  :  List[str]					)   ->Tuple:
            snake_case__							:	Dict   	=	GPTNeoXForCausalLM(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	List[str]   	=	model(_snake_case, attention_mask=_snake_case, labels=_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)					)






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  List[str], _snake_case  :  Union[str, Any], _snake_case  :  Optional[Any], _snake_case  :  Any					)   ->List[str]:
            snake_case__							:	Optional[int]   	=	self.num_labels
            snake_case__							:	int   	=	GPTNeoXForQuestionAnswering(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	int   	=	model(_snake_case, attention_mask=_snake_case					)
            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    lowercase_   (       self  :  Any, _snake_case  :  Any, _snake_case  :  Dict, _snake_case  :  Optional[Any], _snake_case  :  str					)   ->Union[str, Any]:
            snake_case__							:	str   	=	self.num_labels
            snake_case__							:	List[Any]   	=	GPTNeoXForSequenceClassification(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Optional[int]   	=	ids_tensor([self.batch_size], self.type_sequence_label_size					)
            snake_case__							:	Any   	=	model(_snake_case, attention_mask=_snake_case, labels=_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)					)






     def    lowercase_   (       self  :  Dict, _snake_case  :  str, _snake_case  :  Optional[Any], _snake_case  :  Optional[int], _snake_case  :  List[Any]					)   ->str:
            snake_case__							:	Optional[int]   	=	self.num_labels
            snake_case__							:	List[str]   	=	GPTNeoXForTokenClassification(_snake_case					)
            model.to(_snake_case					)
            model.eval()
            snake_case__							:	Tuple   	=	model(_snake_case, attention_mask=_snake_case, labels=_snake_case					)
            self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)					)






     def    lowercase_   (       self  :  Any, _snake_case  :  List[str], _snake_case  :  str, _snake_case  :  int					)   ->str:
            snake_case__							:	Union[str, Any]   	=	True
            snake_case__							:	Union[str, Any]   	=	GPTNeoXForCausalLM(config=_snake_case					)
            model.to(_snake_case					)
            model.eval()

            # first forward pass
            snake_case__							:	Tuple   	=	model(_snake_case, attention_mask=_snake_case, use_cache=_snake_case					)
            snake_case__							:	Optional[Any]   	=	outputs.past_key_values

            # create hypothetical multiple next token and extent to next_input_ids
            snake_case__							:	Dict   	=	ids_tensor((self.batch_size, 3), config.vocab_size					)
            snake_case__							:	int   	=	ids_tensor((self.batch_size, 3), vocab_size=2					)

            # append to next input_ids and
            snake_case__							:	Any   	=	torch.cat([input_ids, next_tokens], dim=-1					)
            snake_case__							:	Union[str, Any]   	=	torch.cat([input_mask, next_mask], dim=-1					)

            snake_case__							:	str   	=	model(_snake_case, attention_mask=_snake_case, output_hidden_states=_snake_case					)
            snake_case__							:	int   	=	output_from_no_past['hidden_states'][0]
            snake_case__							:	Optional[Any]   	=	model(
                _snake_case, attention_mask=_snake_case, past_key_values=_snake_case, output_hidden_states=_snake_case, )['hidden_states'][0]

            # select random slice
            snake_case__							:	int   	=	ids_tensor((1,), output_from_past.shape[-1]					).item()
            snake_case__							:	int   	=	output_from_no_past[:, -3:, random_slice_idx].detach()
            snake_case__							:	Dict   	=	output_from_past[:, :, random_slice_idx].detach()

            self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

            # test that outputs are equal for slice
            self.parent.assertTrue(torch.allclose(_snake_case, _snake_case, atol=1e-3					)					)






     def    lowercase_   (       self  :  str					)   ->Union[str, Any]:
            snake_case__							:	str   	=	self.prepare_config_and_inputs()
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	int   	=	config_and_inputs
            snake_case__							:	Tuple   	=	{'input_ids': input_ids, 'attention_mask': input_mask}
            return config, inputs_dict




@require_torch
class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	(
         (
             GPTNeoXModel,
             GPTNeoXForCausalLM,
             GPTNeoXForQuestionAnswering,
             GPTNeoXForSequenceClassification,
             GPTNeoXForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     _SCREAMING_SNAKE_CASE						=	(GPTNeoXForCausalLM,) if is_torch_available() else ()
     _SCREAMING_SNAKE_CASE						=	(
         {
             """feature-extraction""": GPTNeoXModel,
             """question-answering""": GPTNeoXForQuestionAnswering,
             """text-classification""": GPTNeoXForSequenceClassification,
             """text-generation""": GPTNeoXForCausalLM,
             """token-classification""": GPTNeoXForTokenClassification,
             """zero-shot""": GPTNeoXForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False
     _SCREAMING_SNAKE_CASE						=	False






     def    lowercase_   (       self  :  Optional[int]					)   ->List[Any]:
            snake_case__							:	Optional[int]   	=	GPTNeoXModelTester(self					)
            snake_case__							:	Optional[Any]   	=	ConfigTester(self, config_class=_snake_case, hidden_size=6_4, num_attention_heads=8					)






     def    lowercase_   (       self  :  List[str]					)   ->Any:
            self.config_tester.run_common_tests()






     def    lowercase_   (       self  :  Union[str, Any]					)   ->List[str]:
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	Dict   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(_snake_case, _snake_case, _snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->int:
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	Optional[Any]   	=	self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(_snake_case, _snake_case, _snake_case					)






     def    lowercase_   (       self  :  Optional[Any]					)   ->str:
            # This regression test was failing with PyTorch < 1.3
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	str   	=	self.model_tester.prepare_config_and_inputs_for_decoder()

            snake_case__							:	Optional[Any]   	=	None

            self.model_tester.create_and_check_model_as_decoder(_snake_case, _snake_case, _snake_case					)






     def    lowercase_   (       self  :  Union[str, Any]					)   ->Tuple:
            snake_case__	,				snake_case__	,				snake_case__	,				snake_case__							:	int   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(_snake_case, _snake_case, _snake_case					)






     def    lowercase_   (       self  :  Any					)   ->List[Any]:
            snake_case__							:	Union[str, Any]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_causal_lm(*_snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->List[str]:
            snake_case__							:	Optional[int]   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_question_answering(*_snake_case					)






     def    lowercase_   (       self  :  Tuple					)   ->Dict:
            snake_case__							:	str   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_sequence_classification(*_snake_case					)






     def    lowercase_   (       self  :  List[str]					)   ->int:
            snake_case__							:	Tuple   	=	self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*_snake_case					)






     @unittest.skip(reason='Feed forward chunking is not implemented'					)
     def    lowercase_   (       self  :  Any					)   ->List[str]:
            pass






     @parameterized.expand([('linear',), ('dynamic',)]					)
     def    lowercase_   (       self  :  List[str], _snake_case  :  Tuple					)   ->List[Any]:
            snake_case__	,				snake_case__							:	List[str]   	=	self.model_tester.prepare_config_and_inputs_for_common()
            snake_case__							:	List[Any]   	=	ids_tensor([1, 1_0], config.vocab_size					)
            snake_case__							:	List[Any]   	=	ids_tensor([1, int(config.max_position_embeddings * 1.5					)], config.vocab_size					)

            set_seed(4_2					)  # Fixed seed at init time so the two models get the same random weights
            snake_case__							:	Dict   	=	GPTNeoXModel(_snake_case					)
            original_model.to(_snake_case					)
            original_model.eval()
            snake_case__							:	List[str]   	=	original_model(_snake_case					).last_hidden_state
            snake_case__							:	int   	=	original_model(_snake_case					).last_hidden_state

            set_seed(4_2					)  # Fixed seed at init time so the two models get the same random weights
            snake_case__							:	str   	=	{'type': scaling_type, 'factor': 1_0.0}
            snake_case__							:	str   	=	GPTNeoXModel(_snake_case					)
            scaled_model.to(_snake_case					)
            scaled_model.eval()
            snake_case__							:	str   	=	scaled_model(_snake_case					).last_hidden_state
            snake_case__							:	Union[str, Any]   	=	scaled_model(_snake_case					).last_hidden_state

            # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
            # maximum sequence length, so the outputs for the short input should match.
            if scaling_type == "dynamic":
                   self.assertTrue(torch.allclose(_snake_case, _snake_case, atol=1e-5					)					)
            else:
                   self.assertFalse(torch.allclose(_snake_case, _snake_case, atol=1e-5					)					)

            # The output should be different for long inputs
            self.assertFalse(torch.allclose(_snake_case, _snake_case, atol=1e-5					)					)




@require_torch
class 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     @slow
     def    lowercase_   (       self  :  List[Any]					)   ->Union[str, Any]:
            snake_case__							:	int   	=	AutoTokenizer.from_pretrained('EleutherAI/pythia-410m-deduped'					)
            for checkpointing in [True, False]:
                   snake_case__							:	Any   	=	GPTNeoXForCausalLM.from_pretrained('EleutherAI/pythia-410m-deduped'					)

                   if checkpointing:
                          model.gradient_checkpointing_enable()
                   else:
                          model.gradient_checkpointing_disable()
                   model.to(_snake_case					)

                   snake_case__							:	int   	=	tokenizer('My favorite food is', return_tensors='pt'					).to(_snake_case					)
                   # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
                   # See: https://github.com/huggingface/transformers/pull/24193
                   snake_case__							:	Union[str, Any]   	=	'My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI\'m not sure'

                   snake_case__							:	List[Any]   	=	model.generate(**_snake_case, do_sample=_snake_case, max_new_tokens=2_0					)
                   snake_case__							:	str   	=	tokenizer.batch_decode(_snake_case					)[0]

                   self.assertEqual(_snake_case, _snake_case					)











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()






