

import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase     :   Any						     =  get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
				from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCAmelCase     :   List[Any]						     =  25_00_04
lowerCAmelCase     :   List[Any]						     =  25_00_20
@require_sentencepiece
@require_tokenizers
class  _A		(  snake_case__   ,     unittest.TestCase):
		SCREAMING_SNAKE_CASE    :      Optional[int]       =  MBartaaTokenizer
		SCREAMING_SNAKE_CASE    :      Dict       =  MBartaaTokenizerFast
		SCREAMING_SNAKE_CASE    :      Optional[Any]       =  True
		SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  True

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								super().setUp()

								# We have a SentencePiece fixture for testing
								SCREAMING_SNAKE_CASE_				:  Dict           =     MBartaaTokenizer(lowercase_ ,					src_lang='en_XX' ,					tgt_lang='ro_RO' ,					keep_accents=lowercase_					)
								tokenizer.save_pretrained(self.tmpdirname					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  List[str]           =     "<s>"
								SCREAMING_SNAKE_CASE_				:  Any           =     0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_					) ,					lowercase_					)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_					) ,					lowercase_					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  int           =     list(self.get_tokenizer().get_vocab().keys()					)

								self.assertEqual(vocab_keys[0] ,					'<s>'					)
								self.assertEqual(vocab_keys[1] ,					'<pad>'					)
								self.assertEqual(vocab_keys[-1] ,					'<mask>'					)
								self.assertEqual(len(lowercase_					) ,					1054					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								self.assertEqual(self.get_tokenizer().vocab_size ,					1054					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Any           =     MBartaaTokenizer(lowercase_ ,					src_lang='en_XX' ,					tgt_lang='ro_RO' ,					keep_accents=lowercase_					)

								SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.tokenize('This is a test'					)
								self.assertListEqual(lowercase_ ,					['▁This', '▁is', '▁a', '▁t', 'est']					)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(lowercase_					) ,					[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,					)

								SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.tokenize('I was born in 92000, and this is falsé.'					)
								self.assertListEqual(
								    lowercase_ ,					[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '9', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', 'é', '.'] ,					)
								SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer.convert_tokens_to_ids(lowercase_					)
								self.assertListEqual(
								    lowercase_ ,					[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ] ,					)

								SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer.convert_ids_to_tokens(lowercase_					)
								self.assertListEqual(
								    lowercase_ ,					[SPIECE_UNDERLINE + 'I', SPIECE_UNDERLINE + 'was', SPIECE_UNDERLINE + 'b', 'or', 'n', SPIECE_UNDERLINE + 'in', SPIECE_UNDERLINE + '', '<unk>', '2', '0', '0', '0', ',', SPIECE_UNDERLINE + 'and', SPIECE_UNDERLINE + 'this', SPIECE_UNDERLINE + 'is', SPIECE_UNDERLINE + 'f', 'al', 's', '<unk>', '.'] ,					)

		@slow
		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  int           =     {"input_ids": [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=lowercase_ ,					model_name='facebook/mbart-large-50' ,					revision='d3913889c59cd5c9e456b269c376325eabad57e2' ,					)



		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								if not self.test_slow_tokenizer:
														# as we don't have a slow version, we can't compare the outputs between slow and fast versions
														return

								SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
														with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
																				SCREAMING_SNAKE_CASE_				:  Dict           =     self.rust_tokenizer_class.from_pretrained(lowercase_ ,					**lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer_class.from_pretrained(lowercase_ ,					**lowercase_					)

																				SCREAMING_SNAKE_CASE_				:  List[Any]           =     tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tokenizer_r.save_pretrained(lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer_p.save_pretrained(lowercase_					)

																				# Checks it save with the same files + the tokenizer.json file for the fast one
																				self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files					)					)
																				SCREAMING_SNAKE_CASE_				:  Any           =     tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f					)
																				self.assertSequenceEqual(lowercase_ ,					lowercase_					)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer_r.from_pretrained(lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  str           =     tokenizer_p.from_pretrained(lowercase_					)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(lowercase_ ,					lowercase_					)					)
																										# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
																										# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

																				shutil.rmtree(lowercase_					)

																				# Save tokenizer rust, legacy_format=True
																				SCREAMING_SNAKE_CASE_				:  List[Any]           =     tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer_r.save_pretrained(lowercase_ ,					legacy_format=lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  List[Any]           =     tokenizer_p.save_pretrained(lowercase_					)

																				# Checks it save with the same files
																				self.assertSequenceEqual(lowercase_ ,					lowercase_					)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer_r.from_pretrained(lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tokenizer_p.from_pretrained(lowercase_					)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(lowercase_ ,					lowercase_					)					)

																				shutil.rmtree(lowercase_					)

																				# Save tokenizer rust, legacy_format=False
																				SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     tempfile.mkdtemp()

																				SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer_r.save_pretrained(lowercase_ ,					legacy_format=lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer_p.save_pretrained(lowercase_					)

																				# Checks it saved the tokenizer.json file
																				self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files					)					)

																				# Checks everything loads correctly in the same way
																				SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer_r.from_pretrained(lowercase_					)
																				SCREAMING_SNAKE_CASE_				:  List[str]           =     tokenizer_p.from_pretrained(lowercase_					)

																				# Check special tokens are set accordingly on Rust and Python
																				for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(lowercase_ ,					lowercase_					)					)

																				shutil.rmtree(lowercase_					)

@require_torch
@require_sentencepiece
@require_tokenizers
class  _A		(  unittest.TestCase):
		SCREAMING_SNAKE_CASE    :      List[str]       =  '''facebook/mbart-large-50-one-to-many-mmt'''
		SCREAMING_SNAKE_CASE    :      Tuple       =  [
		    ''' UN Chief Says There Is No Military Solution in Syria''',
		    ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
		]
		SCREAMING_SNAKE_CASE    :      Any       =  [
		    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
		    '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
		    ''' pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor'''
		    ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
		]
		SCREAMING_SNAKE_CASE    :      str       =  [EN_CODE, 8_274, 127_873, 25_916, 7, 8_622, 2_071, 438, 67_485, 53, 187_895, 23, 51_712, 2]

		@classmethod
		def  UpperCAmelCase    (				cls					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  MBartaaTokenizer           =     MBartaaTokenizer.from_pretrained(
								    cls.checkpoint_name ,					src_lang='en_XX' ,					tgt_lang='ro_RO'					)
								SCREAMING_SNAKE_CASE_				:  Dict           =     1
								return cls

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR'] ,					25_0001					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN'] ,					25_0004					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO'] ,					25_0020					)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN'] ,					25_0038					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  int           =     self.tokenizer.batch_encode_plus(self.src_text					).input_ids[0]
								self.assertListEqual(self.expected_src_tokens ,					lowercase_					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								self.assertIn(lowercase_ ,					self.tokenizer.all_special_ids					)
								SCREAMING_SNAKE_CASE_				:  List[str]           =     [RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
								SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.tokenizer.decode(lowercase_ ,					skip_special_tokens=lowercase_					)
								SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer.decode(generated_ids[1:] ,					skip_special_tokens=lowercase_					)
								self.assertEqual(lowercase_ ,					lowercase_					)
								self.assertNotIn(self.tokenizer.eos_token ,					lowercase_					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     ["this is gunna be a long sentence " * 20]
								assert isinstance(src_text[0] ,					lowercase_					)
								SCREAMING_SNAKE_CASE_				:  str           =     10
								SCREAMING_SNAKE_CASE_				:  Any           =     self.tokenizer(lowercase_ ,					max_length=lowercase_ ,					truncation=lowercase_					).input_ids[0]
								self.assertEqual(ids[0] ,					lowercase_					)
								self.assertEqual(ids[-1] ,					2					)
								self.assertEqual(len(lowercase_					) ,					lowercase_					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']					) ,					[25_0053, 25_0001]					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Optional[int]           =     tempfile.mkdtemp()
								SCREAMING_SNAKE_CASE_				:  List[str]           =     self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowercase_					)
								SCREAMING_SNAKE_CASE_				:  int           =     MBartaaTokenizer.from_pretrained(lowercase_					)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,					lowercase_					)

		@require_torch
		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.tokenizer(self.src_text ,					text_target=self.tgt_text ,					padding=lowercase_ ,					return_tensors='pt'					)
								SCREAMING_SNAKE_CASE_				:  Optional[int]           =     shift_tokens_right(batch['labels'] ,					self.tokenizer.pad_token_id					)

								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == RO_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]

		@require_torch
		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer(
								    self.src_text ,					text_target=self.tgt_text ,					padding=lowercase_ ,					truncation=lowercase_ ,					max_length=len(self.expected_src_tokens					) ,					return_tensors='pt' ,					)
								SCREAMING_SNAKE_CASE_				:  Tuple           =     shift_tokens_right(batch['labels'] ,					self.tokenizer.pad_token_id					)

								self.assertIsInstance(lowercase_ ,					lowercase_					)

								self.assertEqual((2, 14) ,					batch.input_ids.shape					)
								self.assertEqual((2, 14) ,					batch.attention_mask.shape					)
								SCREAMING_SNAKE_CASE_				:  Optional[int]           =     batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens ,					lowercase_					)
								self.assertEqual(2 ,					batch.decoder_input_ids[0, 0]					)  # decoder_start_token_id
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens ,					[EN_CODE]					)
								self.assertEqual(self.tokenizer.suffix_tokens ,					[self.tokenizer.eos_token_id]					)

		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.tokenizer(self.src_text ,					padding=lowercase_ ,					truncation=lowercase_ ,					max_length=3 ,					return_tensors='pt'					)
								SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.tokenizer(
								    text_target=self.tgt_text ,					padding=lowercase_ ,					truncation=lowercase_ ,					max_length=10 ,					return_tensors='pt'					)
								SCREAMING_SNAKE_CASE_				:  str           =     targets["input_ids"]
								SCREAMING_SNAKE_CASE_				:  List[Any]           =     shift_tokens_right(lowercase_ ,					self.tokenizer.pad_token_id					)

								self.assertEqual(batch.input_ids.shape[1] ,					3					)
								self.assertEqual(batch.decoder_input_ids.shape[1] ,					10					)



		@require_torch
		def  UpperCAmelCase    (				self					):





								"""simple docstring"""







								SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     self.tokenizer._build_translation_inputs(
								    'A test' ,					return_tensors='pt' ,					src_lang='en_XX' ,					tgt_lang='ar_AR'					)

								self.assertEqual(
								    nested_simplify(lowercase_					) ,					{
								        # en_XX, A, test, EOS
								        'input_ids': [[25_0004, 62, 3034, 2]],
								        'attention_mask': [[1, 1, 1, 1]],
								        # ar_AR
								        'forced_bos_token_id': 25_0001,
								    } ,					)









import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel

def    A_  (							a=3_2					,   a=1_0					,   a=1_0_0					,   a=1_0_2_6					,   a=True					,   a="data/tokenized_stories_train_wikitext103.jbl"					,   a="igf_context_pairs.jbl"					,   ):

      """simple docstring"""



      set_seed(3  )
      # generate train_data and objective_set
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     generate_datasets(
          a					,   a					,   number=a					,   min_len=1_0_2_6					,   trim=a  )
      # keeps model same across runs
      set_seed(4  )
      # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
      # can we train on GPU?
      SCREAMING_SNAKE_CASE_				:  str           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )

      # load pretrained model
      SCREAMING_SNAKE_CASE_				:  List[str]           =     load_gpta('gpt2'  ).to(a  )
      print('computing perplexity on objective set'  )
      SCREAMING_SNAKE_CASE_				:  List[Any]           =     compute_perplexity(a					,   a					,   a  ).item()
      print('perplexity on objective set:'					,   a  )

      # collect igf pairs and save to file demo.jbl
      collect_objective_set(a					,   a					,   a					,   a					,   a					,   a					,   a					,   a  )

      # clean up, delete model and data we don't need anymore
      del model, train_data, objective_set
      torch.cuda.empty_cache()

def    A_  (							a					,   a=1_5					,   a=1_2_8					,   a=1_0_0					,   a="igf_model.pt"					,   ):

      """simple docstring"""



      set_seed(4_2  )

      # Load pre-trained model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )

      # Initialize secondary learner to use embedding weights of model
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     SecondaryLearner(a  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Any           =     train_secondary_learner(
          a					,   a					,   max_epochs=a					,   batch_size=a					,   eval_freq=1_0_0					,   igf_model_path=a					,   )

      del model, secondary_learner_train_data
      torch.cuda.empty_cache()

      return secondary_learner

def    A_  (							a					,   a					,   a					,   a=3_2					,   a=1_0_0_0					,   a=1_6					,   a=1.0					,   a=recopy_gpta					,   a=None					,   a=1_0					,   a="gpt2_finetuned.pt"					,   ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Dict           =     torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'  )
      SCREAMING_SNAKE_CASE_				:  List[str]           =     RandomSampler(a  )
      SCREAMING_SNAKE_CASE_				:  Dict           =     DataLoader(a					,   sampler=a  )

      SCREAMING_SNAKE_CASE_				:  int           =     max_steps // (len(a  )) + 1
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     0
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.zeros((1, context_len)					,   dtype=torch.long					,   device=a  )
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     recopy_model(a					,   a					,   a  )

      model.train()
      if secondary_learner is not None:
            secondary_learner.to(a  )
            secondary_learner.eval()
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  List[str]           =     0

      SCREAMING_SNAKE_CASE_				:  List[Any]           =     []
      SCREAMING_SNAKE_CASE_				:  Dict           =     []

      # Compute the performance of the transformer model at the beginning
      SCREAMING_SNAKE_CASE_				:  str           =     compute_perplexity(a					,   a					,   a  )
      test_perps.append(a  )
      print('Test perplexity, step'					,   a					,   ':'					,   a  )
      for epoch in range(int(a  )  ):
            for step, example in enumerate(a  ):
                  torch.cuda.empty_cache()
                  SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     random.randint(0					,   example.size(2  ) - context_len - 1  )
                  SCREAMING_SNAKE_CASE_				:  Any           =     example[0, 0, start : start + context_len]
                  lm_optimizer.zero_grad()
                  SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     model(a					,   labels=a  )
                  SCREAMING_SNAKE_CASE_				:  int           =     True

                  if secondary_learner is not None:
                        SCREAMING_SNAKE_CASE_				:  str           =     secondary_learner.forward(
                            torch.tensor(a					,   dtype=torch.long					,   device=a  ).unsqueeze(0  )  )[0].item()
                        observed_qs.append(float(a  )  )

                        # Here we implement the simple non-constant threshold for the predicted IG(X) value
                        # We will decay the selectivity of our secondary learner filter from
                        # 1 standard deviation above average to 1 below average after 10 batches.

                        if global_step == 1_0:
                              SCREAMING_SNAKE_CASE_				:  Any           =     -1
                        if predicted_q < threshold:
                              SCREAMING_SNAKE_CASE_				:  Tuple           =     False

            # If we passed the filter, add the context to the batch!
                  if do_backprop:
                        contexts.append(np.array(context.cpu()  )  )
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     outputs[0]
                        lm_loss.backward()
                        examples += 1

                  del outputs

                  # Once the batch is filled with enough contexts, backprop on the batch.
                  if examples == batch_size:
                        torch.cuda.empty_cache()
                        SCREAMING_SNAKE_CASE_				:  Tuple           =     0
                        # Do LM backprop
                        torch.nn.utils.clip_grad_norm_(model.parameters()					,   3.0  )
                        lm_optimizer.step()
                        lm_scheduler.step()  # Update learning rate schedule
                        global_step += 1
                        # Compute the performance of the transformer model at this batch
                        if global_step % eval_interval == 0:
                              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     compute_perplexity(a					,   a					,   a  )
                              test_perps.append(a  )

                              print('Test perplexity, step'					,   a					,   ':'					,   a  )
            # Break out of the loop after 60 batches
                  if max_steps > 0 and global_step > 6_0:
                        break
            if max_steps > 0 and global_step > 6_0:
                  break

    # save finetuned transformer model
      torch.save(model.state_dict()					,   a  )
      torch.cuda.empty_cache()
      # Do some cleaning up so we can reinitialize for the next run of this function
      del lm_optimizer
      del lm_scheduler
      return model

def    A_  (							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'  )

      # Required parameters
      parser.add_argument(
          '--data_dir'					,   default=a					,   type=a					,   required=a					,   help='The input data dir. Should contain data files for WikiText.'					,   )
      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(
          '--data_file'					,   type=a					,   default=a					,   help=(
              'A jbl file containing tokenized data which can be split as objective dataset, '
              'train_dataset and test_dataset.'
          )					,   )

      parser.add_argument(
          '--igf_data_file'					,   type=a					,   default=a					,   help='A jbl file containing the context and information gain pairs to train secondary learner.'					,   )

      parser.add_argument(
          '--output_dir'					,   default=a					,   type=a					,   required=a					,   help='The output directory where the final fine-tuned model is stored.'					,   )

      parser.add_argument(
          '--tokenizer_name'					,   default=a					,   type=a					,   help='Pretrained tokenizer name or path if not the same as model_name'					,   )
      parser.add_argument('--seed'					,   type=a					,   default=a					,   help='A seed for reproducible training.'  )

      parser.add_argument(
          '--context_len'					,   default=3_2					,   type=a					,   help=(
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )					,   )

      parser.add_argument(
          '--size_objective_set'					,   default=1_0_0					,   type=a					,   help='number of articles that are long enough to be used as our objective set'					,   )
      parser.add_argument(
          '--eval_freq'					,   default=1_0_0					,   type=a					,   help='secondary model evaluation is triggered at eval_freq'  )

      parser.add_argument('--max_steps'					,   default=1_0_0_0					,   type=a					,   help='To calculate training epochs'  )

      parser.add_argument(
          '--secondary_learner_batch_size'					,   default=1_2_8					,   type=a					,   help='batch size of training data for secondary learner'					,   )

      parser.add_argument(
          '--batch_size'					,   default=1_6					,   type=a					,   help='batch size of training data of language model(gpt2) '  )

      parser.add_argument(
          '--eval_interval'					,   default=1_0					,   type=a					,   help=(
              'decay the selectivity of our secondary learner filter from'
              '1 standard deviation above average to 1 below average after 10 batches'
          )					,   )

      parser.add_argument(
          '--number'					,   default=1_0_0					,   type=a					,   help='The number of examples split to be used as objective_set/test_data'  )

      parser.add_argument(
          '--min_len'					,   default=1_0_2_6					,   type=a					,   help='The minimum length of the article to be used as objective set'  )

      parser.add_argument(
          '--secondary_learner_max_epochs'					,   default=1_5					,   type=a					,   help='number of epochs to train secondary learner'  )

      parser.add_argument('--trim'					,   default=a					,   type=a					,   help='truncate the example if it exceeds context length'  )

      parser.add_argument(
          '--threshold'					,   default=1.0					,   type=a					,   help=(
              'The threshold value used by secondary learner to filter the train_data and allow only'
              ' informative data as input to the model'
          )					,   )

      parser.add_argument('--finetuned_model_name'					,   default='gpt2_finetuned.pt'					,   type=a					,   help='finetuned_model_name'  )

      parser.add_argument(
          '--recopy_model'					,   default=a					,   type=a					,   help='Reset the model to the original pretrained GPT-2 weights after each iteration'					,   )

      # function calls
      # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
      generate_n_pairs(
          context_len=3_2					,   max_steps=1_0					,   size_objective_set=1_0_0					,   min_len=1_0_2_6					,   trim=a					,   data_file='data/tokenized_stories_train_wikitext103.jbl'					,   igf_data_file='igf_context_pairs.jbl'					,   )

      # Load train data for secondary learner
      SCREAMING_SNAKE_CASE_				:  Tuple           =     joblib.load('data/IGF_values.jbl'  )

      # Train secondary learner
      SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     training_secondary_learner(
          a					,   secondary_learner_max_epochs=1_5					,   secondary_learner_batch_size=1_2_8					,   eval_freq=1_0_0					,   igf_model_path='igf_model.pt'					,   )

      # load pretrained gpt2 model
      SCREAMING_SNAKE_CASE_				:  int           =     GPTaLMHeadModel.from_pretrained('gpt2'  )
      set_seed(4_2  )

      # Generate train and test data to train and evaluate gpt2 model
      SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  List[str]           =     generate_datasets(
          context_len=3_2					,   file='data/tokenized_stories_train_wikitext103.jbl'					,   number=1_0_0					,   min_len=1_0_2_6					,   trim=a  )

      # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
      finetune(
          a					,   a					,   a					,   context_len=3_2					,   max_steps=1_0_0_0					,   batch_size=1_6					,   threshold=1.0					,   recopy_model=a					,   secondary_learner=a					,   eval_interval=1_0					,   finetuned_model_name='gpt2_finetuned.pt'					,   )


if __name__ == "__main__":
    main()






