






"""simple docstring"""






import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class  __SCREAMING_SNAKE_CASE			(   UpperCAmelCase_	,     unittest.TestCase     ):


					'''simple docstring'''





					_a         =							LongformerTokenizer
					_a         =							True
					_a         =							LongformerTokenizerFast
					_a         =							True



					def 	snake_case ( self							:						int  )->      str:
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase__	:	Dict				=[
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								lowerCamelCase__	:	Tuple				=dict(zip(_snake_case,						range(len(_snake_case  )  )  )  )
								lowerCamelCase__	:	Tuple				=['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								lowerCamelCase__	:	Any				={'''unk_token''': '''<unk>'''}

								lowerCamelCase__	:	Tuple				=os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''vocab_file''']  )
								lowerCamelCase__	:	Union[str, Any]				=os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file,						'''w''',						encoding='''utf-8'''  ) as fp:
											fp.write(json.dumps(_snake_case  ) + '''\n'''  )
								with open(self.merges_file,						'''w''',						encoding='''utf-8'''  ) as fp:
											fp.write('''\n'''.join(_snake_case  )  )



					def 	snake_case ( self							:						int,						**lowerCamelCase							:						Union[str, Any]  )->      Tuple:
								kwargs.update(self.special_tokens_map  )
								return self.tokenizer_class.from_pretrained(self.tmpdirname,						**_snake_case  )



					def 	snake_case ( self							:						Optional[int],						**lowerCamelCase							:						List[Any]  )->      List[str]:
								kwargs.update(self.special_tokens_map  )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,						**_snake_case  )



					def 	snake_case ( self							:						Any,						lowerCamelCase							:						Optional[Any]  )->      List[Any]:
								lowerCamelCase__	:	int				='''lower newer'''
								lowerCamelCase__	:	List[str]				='''lower newer'''
								return input_text, output_text



					def 	snake_case ( self							:						Any  )->      Dict:
								lowerCamelCase__	:	Tuple				=self.tokenizer_class(self.vocab_file,						self.merges_file,						**self.special_tokens_map  )
								lowerCamelCase__	:	List[str]				='''lower newer'''
								lowerCamelCase__	:	Any				=['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
								lowerCamelCase__	:	int				=tokenizer.tokenize(_snake_case  )  # , add_prefix_space=True)
								self.assertListEqual(_snake_case,						_snake_case  )

								lowerCamelCase__	:	List[Any]				=tokens + [tokenizer.unk_token]
								lowerCamelCase__	:	Optional[int]				=[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case  ),						_snake_case  )



					def 	snake_case ( self							:						List[str]  )->      Tuple:
								lowerCamelCase__	:	str				=self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('''Hello world!''',						add_special_tokens=_snake_case  ),						[0, 3_1414, 232, 328, 2]  )
								self.assertListEqual(
								    tokenizer.encode('''Hello world! cécé herlolip 418''',						add_special_tokens=_snake_case  ),						[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2],						)



					@slow
					def 	snake_case ( self							:						List[Any]  )->      str:
								lowerCamelCase__	:	str				=self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''  )

								lowerCamelCase__	:	str				=tokenizer.encode('''sequence builders''',						add_special_tokens=_snake_case  )
								lowerCamelCase__	:	Any				=tokenizer.encode('''multi-sequence build''',						add_special_tokens=_snake_case  )

								lowerCamelCase__	:	List[str]				=tokenizer.encode(
								    '''sequence builders''',						add_special_tokens=_snake_case,						add_prefix_space=_snake_case  )
								lowerCamelCase__	:	str				=tokenizer.encode(
								    '''sequence builders''',						'''multi-sequence build''',						add_special_tokens=_snake_case,						add_prefix_space=_snake_case  )

								lowerCamelCase__	:	int				=tokenizer.build_inputs_with_special_tokens(_snake_case  )
								lowerCamelCase__	:	int				=tokenizer.build_inputs_with_special_tokens(_snake_case,						_snake_case  )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode



					def 	snake_case ( self							:						List[str]  )->      int:
								lowerCamelCase__	:	List[str]				=self.get_tokenizer()

								lowerCamelCase__	:	str				='''Encode this sequence.'''
								lowerCamelCase__	:	List[Any]				=tokenizer.byte_encoder[''' '''.encode('''utf-8'''  )[0]]

								# Testing encoder arguments
								lowerCamelCase__	:	Dict				=tokenizer.encode(_snake_case,						add_special_tokens=_snake_case,						add_prefix_space=_snake_case  )
								lowerCamelCase__	:	Tuple				=tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
								self.assertNotEqual(_snake_case,						_snake_case  )

								lowerCamelCase__	:	Tuple				=tokenizer.encode(_snake_case,						add_special_tokens=_snake_case,						add_prefix_space=_snake_case  )
								lowerCamelCase__	:	int				=tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
								self.assertEqual(_snake_case,						_snake_case  )

								tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}  )
								lowerCamelCase__	:	List[str]				=tokenizer.encode(_snake_case,						add_special_tokens=_snake_case  )
								lowerCamelCase__	:	Tuple				=tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
								self.assertNotEqual(_snake_case,						_snake_case  )

								# Testing spaces after special tokens
								lowerCamelCase__	:	Optional[int]				='''<mask>'''
								tokenizer.add_special_tokens(
								    {'''mask_token''': AddedToken(_snake_case,						lstrip=_snake_case,						rstrip=_snake_case  )}  )  # mask token has a left space
								lowerCamelCase__	:	Tuple				=tokenizer.convert_tokens_to_ids(_snake_case  )

								lowerCamelCase__	:	Tuple				='''Encode <mask> sequence'''
								lowerCamelCase__	:	Optional[int]				='''Encode <mask>sequence'''

								lowerCamelCase__	:	Optional[Any]				=tokenizer.encode(_snake_case  )
								lowerCamelCase__	:	Optional[Any]				=encoded.index(_snake_case  )
								lowerCamelCase__	:	int				=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
								self.assertEqual(_snake_case,						_snake_case  )

								lowerCamelCase__	:	Any				=tokenizer.encode(_snake_case  )
								lowerCamelCase__	:	str				=encoded.index(_snake_case  )
								lowerCamelCase__	:	int				=tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
								self.assertNotEqual(_snake_case,						_snake_case  )



					def 	snake_case ( self							:						Dict  )->      Tuple:
								pass



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


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
														lowerCamelCase__	:	List[Any]				=self.rust_tokenizer_class.from_pretrained(_snake_case,						**_snake_case  )
														lowerCamelCase__	:	str				=self.tokenizer_class.from_pretrained(_snake_case,						**_snake_case  )
														lowerCamelCase__	:	Optional[Any]				='''A, <mask> AllenNLP sentence.'''
														lowerCamelCase__	:	int				=tokenizer_r.encode_plus(_snake_case,						add_special_tokens=_snake_case,						return_token_type_ids=_snake_case  )
														lowerCamelCase__	:	int				=tokenizer_p.encode_plus(_snake_case,						add_special_tokens=_snake_case,						return_token_type_ids=_snake_case  )

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['''token_type_ids''']  ),						sum(tokens_p['''token_type_ids''']  )  )

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['''attention_mask''']  ) / len(tokens_r['''attention_mask''']  ),						sum(tokens_p['''attention_mask''']  ) / len(tokens_p['''attention_mask''']  ),						)

														lowerCamelCase__	:	Dict				=tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
														lowerCamelCase__	:	int				=tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']  )

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['''input_ids'''],						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
														self.assertSequenceEqual(tokens_r['''input_ids'''],						[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

														self.assertSequenceEqual(
														    _snake_case,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )
														self.assertSequenceEqual(
														    _snake_case,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']  )



					def 	snake_case ( self							:						List[Any]  )->      Any:


								for trim_offsets, add_prefix_space in itertools.product([True, False],						repeat=2  ):
											lowerCamelCase__	:	Optional[int]				=self.rust_tokenizer_class.from_pretrained(
											    self.tmpdirname,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )

											lowerCamelCase__	:	Any				=json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
											lowerCamelCase__	:	Optional[int]				=json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()  )

											self.assertEqual(pre_tokenizer_state['''add_prefix_space'''],						_snake_case  )

											self.assertEqual(post_processor_state['''add_prefix_space'''],						_snake_case  )
											self.assertEqual(post_processor_state['''trim_offsets'''],						_snake_case  )




					def 	snake_case ( self							:						Optional[Any]  )->      str:


								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
														lowerCamelCase__	:	Union[str, Any]				='''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
														lowerCamelCase__	:	Union[str, Any]				=F'''{text_of_1_token} {text_of_1_token}'''

														lowerCamelCase__	:	Optional[int]				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	List[str]				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(0, len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(len(_snake_case  ) + 1, len(_snake_case  ) + 1 + len(_snake_case  )),						)

														lowerCamelCase__	:	Union[str, Any]				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	Optional[int]				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(0, len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(len(_snake_case  ) + 1, len(_snake_case  ) + 1 + len(_snake_case  )),						)

														lowerCamelCase__	:	Optional[int]				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	List[str]				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(0, len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(len(_snake_case  ), len(_snake_case  ) + 1 + len(_snake_case  )),						)

														lowerCamelCase__	:	List[Any]				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	Optional[int]				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(0, len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(len(_snake_case  ), len(_snake_case  ) + 1 + len(_snake_case  )),						)

														lowerCamelCase__	:	List[str]				=F''' {text}'''

														# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
														#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
														# )
														# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
														# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
														# self.assertEqual(
														#     encoding.offset_mapping[1],
														#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
														# )

														lowerCamelCase__	:	Tuple				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	Any				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(1, 1 + len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(1 + len(_snake_case  ) + 1, 1 + len(_snake_case  ) + 1 + len(_snake_case  )),						)

														lowerCamelCase__	:	Optional[int]				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	List[str]				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(0, 1 + len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(1 + len(_snake_case  ), 1 + len(_snake_case  ) + 1 + len(_snake_case  )),						)

														lowerCamelCase__	:	Tuple				=self.rust_tokenizer_class.from_pretrained(
														    _snake_case,						use_fast=_snake_case,						add_prefix_space=_snake_case,						trim_offsets=_snake_case  )
														lowerCamelCase__	:	List[str]				=tokenizer_r(_snake_case,						return_offsets_mapping=_snake_case,						add_special_tokens=_snake_case  )
														self.assertEqual(encoding.offset_mapping[0],						(0, 1 + len(_snake_case  ))  )
														self.assertEqual(
														    encoding.offset_mapping[1],						(1 + len(_snake_case  ), 1 + len(_snake_case  ) + 1 + len(_snake_case  )),						)









"""simple docstring"""






import argparse
import logging
import os
from datetime import datetime

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

from transformers import GPTaLMHeadModel


_lowercase				:				List[str]   		=		logging.getLogger(__name__)






def   snake_case__     (	__lowerCamelCase       :					Any							,	__lowerCamelCase       :					str			):
			"""simple docstring"""
			# save results
			if os.path.exists(__lowerCamelCase			):
						if os.path.exists(os.path.join(__lowerCamelCase							,	'''config.json'''			)			) and os.path.isfile(
						    os.path.join(__lowerCamelCase							,	'''config.json'''			)			):
									os.remove(os.path.join(__lowerCamelCase							,	'''config.json'''			)			)
						if os.path.exists(os.path.join(__lowerCamelCase							,	'''pytorch_model.bin'''			)			) and os.path.isfile(
						    os.path.join(__lowerCamelCase							,	'''pytorch_model.bin'''			)			):
									os.remove(os.path.join(__lowerCamelCase							,	'''pytorch_model.bin'''			)			)
			else:
						os.makedirs(__lowerCamelCase			)
			model.save_pretrained(__lowerCamelCase			)






def   snake_case__     (	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					Dict=False			):
			"""simple docstring"""
			lowerCamelCase__	:	Union[str, Any]				=2
			if unlogit:
						lowerCamelCase__	:	Any				=torch.pow(__lowerCamelCase							,	__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=p * torch.log(__lowerCamelCase			)
			lowerCamelCase__	:	Tuple				=0
			return -plogp.sum(dim=-1			)






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






def   snake_case__     (	__lowerCamelCase       :					int							,	__lowerCamelCase       :					Optional[int]							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					Any=True							,	__lowerCamelCase       :					List[Any]=True							,	__lowerCamelCase       :					List[str]=None							,	__lowerCamelCase       :					Tuple=False			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=model.config.num_hidden_layers, model.config.num_attention_heads
			lowerCamelCase__	:	Optional[Any]				=torch.zeros(__lowerCamelCase							,	__lowerCamelCase			).to(args.device			)
			lowerCamelCase__	:	Optional[Any]				=torch.zeros(__lowerCamelCase							,	__lowerCamelCase			).to(args.device			)

			if head_mask is None:
						lowerCamelCase__	:	List[Any]				=torch.ones(__lowerCamelCase							,	__lowerCamelCase			).to(args.device			)

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

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

						# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
						lowerCamelCase__	:	Dict				=model(__lowerCamelCase							,	labels=__lowerCamelCase							,	head_mask=__lowerCamelCase			)
						#  (loss), lm_logits, presents, (all hidden_states), (attentions)
						lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Tuple				=(
						    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(__lowerCamelCase			):
												lowerCamelCase__	:	Any				=entropy(attn.detach()							,	__lowerCamelCase			)
												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(__lowerCamelCase			).float().detach().sum().data

			# Normalize
			attn_entropy /= tot_tokens
			head_importance /= tot_tokens
			# Layerwise importance normalization
			if not args.dont_normalize_importance_by_layer:
						lowerCamelCase__	:	int				=2
						lowerCamelCase__	:	List[str]				=torch.pow(torch.pow(__lowerCamelCase							,	__lowerCamelCase			).sum(-1			)							,	1 / exponent			)
						head_importance /= norm_by_layer.unsqueeze(-1			) + 1e-2_0

			if not args.dont_normalize_global_importance:
						lowerCamelCase__	:	int				=(head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())

			# Print matrices
			if compute_entropy:
						logger.info('''Attention entropies'''			)
						print_ad_tensor(__lowerCamelCase			)
			if compute_importance:
						logger.info('''Head importance scores'''			)
						print_ad_tensor(__lowerCamelCase			)
			logger.info('''Head ranked by importance scores'''			)
			lowerCamelCase__	:	Optional[int]				=torch.zeros(head_importance.numel()							,	dtype=torch.long							,	device=args.device			)
			lowerCamelCase__	:	Dict				=torch.arange(
			    head_importance.numel()							,	device=args.device			)
			lowerCamelCase__	:	Any				=head_ranks.view_as(__lowerCamelCase			)
			print_ad_tensor(__lowerCamelCase			)
			return attn_entropy, head_importance, total_loss






def   snake_case__     (	__lowerCamelCase       :					List[str]							,	__lowerCamelCase       :					Tuple							,	__lowerCamelCase       :					int			):
			"""simple docstring"""
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Dict				=compute_heads_importance(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase			)
			lowerCamelCase__	:	int				=1 / loss  # instead of downsteam score use the LM loss
			logger.info('''Pruning: original score: %f, threshold: %f'''							,	__lowerCamelCase							,	original_score * args.masking_threshold			)

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

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

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

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

						# Compute metric and head importance again
						lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Optional[Any]				=compute_heads_importance(
						    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase							,	head_mask=__lowerCamelCase			)
						lowerCamelCase__	:	Any				=1 / loss
						logger.info(
						    '''Masking: current score: %f, remaining heads %d (%.1f percents)'''							,	__lowerCamelCase							,	new_head_mask.sum()							,	new_head_mask.sum() / new_head_mask.numel() * 100							,	)

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

			return head_mask






def   snake_case__     (	__lowerCamelCase       :					Union[str, Any]							,	__lowerCamelCase       :					List[Any]							,	__lowerCamelCase       :					str							,	__lowerCamelCase       :					Optional[Any]			):
			"""simple docstring"""
			lowerCamelCase__	:	str				=datetime.now()
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	List[str]				=compute_heads_importance(
			    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase							,	compute_importance=__lowerCamelCase							,	head_mask=__lowerCamelCase			)
			lowerCamelCase__	:	Tuple				=1 / loss
			lowerCamelCase__	:	Optional[Any]				=datetime.now() - before_time

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

			for k, v in heads_to_prune.items():
						if isinstance(__lowerCamelCase							,	__lowerCamelCase			):
									lowerCamelCase__	:	Optional[int]				=[
									    v,
									]

			assert sum(len(__lowerCamelCase			) for h in heads_to_prune.values()			) == (1 - head_mask.long()).sum().item()
			model.prune_heads(__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=sum(p.numel() for p in model.parameters()			)

			lowerCamelCase__	:	Any				=datetime.now()
			lowerCamelCase__			,     lowerCamelCase__			,     lowerCamelCase__	:	Dict				=compute_heads_importance(
			    __lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	compute_entropy=__lowerCamelCase							,	compute_importance=__lowerCamelCase							,	head_mask=__lowerCamelCase							,	actually_pruned=__lowerCamelCase							,	)

			lowerCamelCase__	:	str				=1 / loss
			lowerCamelCase__	:	Union[str, Any]				=datetime.now() - before_time

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






def   snake_case__     (	):
			"""simple docstring"""
			lowerCamelCase__	:	Optional[int]				=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--data_dir'''							,	default=__lowerCamelCase							,	type=__lowerCamelCase							,	required=__lowerCamelCase							,	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=__lowerCamelCase							,	type=__lowerCamelCase							,	required=__lowerCamelCase							,	help='''Path to pretrained model or model identifier from huggingface.co/models'''							,	)
			parser.add_argument(
			    '''--output_dir'''							,	default=__lowerCamelCase							,	type=__lowerCamelCase							,	required=__lowerCamelCase							,	help='''The output directory where the model predictions and checkpoints will be written.'''							,	)

			# Other parameters
			parser.add_argument(
			    '''--config_name'''							,	default=''''''							,	type=__lowerCamelCase							,	help='''Pretrained config name or path if not the same as model_name_or_path'''							,	)
			parser.add_argument(
			    '''--tokenizer_name'''							,	default=''''''							,	type=__lowerCamelCase							,	help='''Pretrained tokenizer name or path if not the same as model_name_or_path'''							,	)
			parser.add_argument(
			    '''--cache_dir'''							,	default=__lowerCamelCase							,	type=__lowerCamelCase							,	help='''Where do you want to store the pre-trained models downloaded from s3'''							,	)
			parser.add_argument(
			    '''--data_subset'''							,	type=__lowerCamelCase							,	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=__lowerCamelCase							,	help='''masking threshold in term of metrics (stop masking when metric < threshold * original metric value).'''							,	)
			parser.add_argument(
			    '''--masking_amount'''							,	default=0.1							,	type=__lowerCamelCase							,	help='''Amount to heads to masking at each masking step.'''			)
			parser.add_argument('''--metric_name'''							,	default='''acc'''							,	type=__lowerCamelCase							,	help='''Metric to use for head masking.'''			)

			parser.add_argument(
			    '''--max_seq_length'''							,	default=128							,	type=__lowerCamelCase							,	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=__lowerCamelCase							,	help='''Batch size.'''			)

			parser.add_argument('''--seed'''							,	type=__lowerCamelCase							,	default=42			)
			parser.add_argument('''--local_rank'''							,	type=__lowerCamelCase							,	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=__lowerCamelCase							,	default=''''''							,	help='''Can be used for distant debugging.'''			)
			parser.add_argument('''--server_port'''							,	type=__lowerCamelCase							,	default=''''''							,	help='''Can be used for distant debugging.'''			)
			lowerCamelCase__	:	List[Any]				=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=__lowerCamelCase			)
						ptvsd.wait_for_attach()

			# Setup devices and distributed training
			if args.local_rank == -1 or args.no_cuda:
						lowerCamelCase__	:	Dict				=torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''			)
						lowerCamelCase__	:	Dict				=0 if args.no_cuda else torch.cuda.device_count()
			else:
						torch.cuda.set_device(args.local_rank			)
						lowerCamelCase__	:	str				=torch.device('''cuda'''							,	args.local_rank			)
						lowerCamelCase__	:	Any				=1
						torch.distributed.init_process_group(backend='''nccl'''			)  # Initializes the distributed backend

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

			lowerCamelCase__	:	Union[str, Any]				=GPTaLMHeadModel.from_pretrained(args.model_name_or_path			)

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

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

			# Prepare dataset
			lowerCamelCase__	:	Union[str, Any]				=np.concatenate(
			    [
			        np.loadtxt(args.data_dir							,	dtype=np.intaa			),
			    ]			)
			lowerCamelCase__	:	Any				=(torch.from_numpy(__lowerCamelCase			),)
			lowerCamelCase__	:	List[Any]				=TensorDataset(*__lowerCamelCase			)
			lowerCamelCase__	:	List[str]				=RandomSampler(__lowerCamelCase			)
			lowerCamelCase__	:	Dict				=DataLoader(__lowerCamelCase							,	sampler=__lowerCamelCase							,	batch_size=args.batch_size			)

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

			# 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:
						lowerCamelCase__	:	Optional[int]				=mask_heads(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)
						prune_heads(__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase							,	__lowerCamelCase			)


if __name__ == "__main__":
		main()




