






import unittest

from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


a__       =     get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_sentencepiece
@require_tokenizers
class    _lowerCAmelCase			(		lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[Any]      =  XLNetTokenizer
				_lowercase				:   Any      =  XLNetTokenizerFast
				_lowercase				:   List[Any]      =  True
				_lowercase				:   Tuple      =  True



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											super().setUp()

											# We have a SentencePiece fixture for testing
											snake_case__	  =       XLNetTokenizer(UpperCamelCase__			,		keep_accents=UpperCamelCase__)
											tokenizer.sanitize_special_tokens()
											tokenizer.save_pretrained(self.tmpdirname)



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       """<s>"""
											snake_case__	  =       1

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



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       list(self.get_tokenizer().get_vocab().keys())

											self.assertEqual(vocab_keys[0]			,		"""<unk>""")
											self.assertEqual(vocab_keys[1]			,		"""<s>""")
											self.assertEqual(vocab_keys[-1]			,		"""<eod>""")
											self.assertEqual(len(UpperCamelCase__)			,		1_0_0_6)



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											self.assertEqual(self.get_tokenizer().vocab_size			,		1_0_0_0)



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__	  =       XLNetTokenizer(UpperCamelCase__			,		keep_accents=UpperCamelCase__)

											snake_case__	  =       tokenizer.tokenize("""This is a test""")
											self.assertListEqual(UpperCamelCase__			,		["""▁This""", """▁is""", """▁a""", """▁t""", """est"""])

											self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase__)			,		[2_8_5, 4_6, 1_0, 1_7_0, 3_8_2])

											snake_case__	  =       tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        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""",
											        """.""",
											    ]			,		)
											snake_case__	  =       tokenizer.convert_tokens_to_ids(UpperCamelCase__)
											self.assertListEqual(UpperCamelCase__			,		[8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4])

											snake_case__	  =       tokenizer.convert_ids_to_tokens(UpperCamelCase__)
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        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>""",
											        """.""",
											    ]			,		)



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       XLNetTokenizer(UpperCamelCase__			,		do_lower_case=UpperCamelCase__)
											snake_case__	  =       tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        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""",
											        """se""",
											        """.""",
											    ]			,		)
											self.assertListEqual(tokenizer.tokenize("""H\u00E9llo""")			,		["""▁he""", """ll""", """o"""])



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       XLNetTokenizer(UpperCamelCase__			,		do_lower_case=UpperCamelCase__)
											snake_case__	  =       tokenizer.tokenize("""I was born in 92000, and this is falsé.""")
											self.assertListEqual(
											    UpperCamelCase__			,		[
											        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""",
											        """se""",
											        """.""",
											    ]			,		)



				@slow
				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       XLNetTokenizer.from_pretrained("""xlnet-base-cased""")

											snake_case__	  =       tokenizer.encode("""sequence builders"""			,		add_special_tokens=UpperCamelCase__)
											snake_case__	  =       tokenizer.encode("""multi-sequence build"""			,		add_special_tokens=UpperCamelCase__)

											snake_case__	  =       tokenizer.build_inputs_with_special_tokens(UpperCamelCase__)
											snake_case__	  =       tokenizer.build_inputs_with_special_tokens(UpperCamelCase__			,		UpperCamelCase__)

											assert encoded_sentence == text + [4, 3]
											assert encoded_pair == text + [4] + text_a + [4, 3]



				@slow
				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__	  =       {"""input_ids""": [[1_7, 2_1_4_4_2, 2_7_0, 1_7, 1_0, 1_4_6_4_5, 3_1_8, 3_4, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 7_7_5_2, 2_2_0_1_8, 2_3, 2_1, 1_7, 4_5_4_6, 3_1_4_5, 7_8_7, 1_3, 3_3_5_2, 1_4_4_3_1, 1_3, 5_5_0_0, 1_1, 1_1_7_6, 5_8_0, 1_3, 1_6_8_1_9, 4_7_9_7, 2_3, 1_7, 1_0, 1_7_1_3_5, 6_5_8, 1_9, 4_5_7, 7_9_3_2, 1_3, 1_8_4, 1_9, 3_1_5_4, 1_7_1_3_5, 6_4_6_8, 1_9, 1_4_0_4, 1_2_2_6_9, 1_9, 4_2_2_9, 5_3_5_6, 1_6_2_6_4, 4_6, 1_9, 1_7, 2_0_5_4_5, 1_0_3_9_5, 9, 9, 9, 1_1, 2_8, 6_4_2_1, 9_5_3_1, 2_0_7_2_9, 1_7, 1_0, 3_5_3, 1_7_0_2_2, 1_1, 2_1, 6_4_2_1, 9_5_3_1, 1_6_9_4_9, 1_7, 1_0, 1_1_5_0_9, 7_5_3, 1_1, 3_3, 9_5, 2_4_2_1, 7_3_8_5, 9_5_6, 1_4_4_3_1, 2_6_2_6, 2_5, 8_4_2, 7_3_8_5, 4_8_3_6, 2_1, 1_4_2_9, 2_2_7_2, 9_8_5_5, 3_1_2_0, 1_6_1, 2_4_7_3_8, 1_9, 1_3_2_0_3, 6_5_8, 2_1_8, 7_8_7, 2_1, 4_3_0, 1_8_4_8_2, 8_4_7, 2_6_3_7, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2_2, 2_2_1_7_8, 2_7, 1_0_6_4, 2_2, 9_5_6, 1_3, 1_1_1_0_1, 1_4_2_9, 5_8_5_4, 2_4_3_1_3, 1_8_9_5_3, 4_0, 4_2_2, 2_4_3_6_6, 6_8, 1_7_5_8, 3_7, 1_0_4_8_3, 1_4_2_5_7, 3_1, 2_0_7, 2_6_3, 2_1, 2_0_3, 3_7_7_3, 2_5, 7_1, 9_7_3_5, 9, 4, 3], [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 3_2, 2_0_4_9, 3_4_4_2, 1_7, 1_3_8_9_4, 3_3_8_0, 2_3, 9_5, 1_8, 1_7_6_3_4, 2_2_8_8, 9, 4, 3]], """token_type_ids""": [[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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 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, 2], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2]], """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], [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, 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, 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]]}  # noqa: E501
											# fmt: on

											self.tokenizer_integration_test_util(
											    expected_encoding=UpperCamelCase__			,		model_name="""xlnet-base-cased"""			,		revision="""c841166438c31ec7ca9a106dee7bb312b73ae511"""			,		)








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 _UpperCAmelCase   (	a						:	int=32  ,					a						:	int=10  ,					a						:	Tuple=100  ,					a						:	str=1026  ,					a						:	Union[str, Any]=True  ,					a						:	Optional[int]="data/tokenized_stories_train_wikitext103.jbl"  ,					a						:	Tuple="igf_context_pairs.jbl"  ,					):
							set_seed(3 )
							# generate train_data and objective_set
							snake_case__  , snake_case__	  =       generate_datasets(
							    a  ,					a  ,					number=a  ,					min_len=1026  ,					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?
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )

							# load pretrained model
							snake_case__	  =       load_gpta("""gpt2""" ).to(a )
							print("""computing perplexity on objective set""" )
							snake_case__	  =       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 _UpperCAmelCase   (	a						:	int  ,					a						:	Dict=15  ,					a						:	Optional[int]=128  ,					a						:	str=100  ,					a						:	Optional[int]="igf_model.pt"  ,					):
							set_seed(42 )

							# Load pre-trained model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )

							# Initialize secondary learner to use embedding weights of model
							snake_case__	  =       SecondaryLearner(a )

							# Train secondary learner
							snake_case__	  =       train_secondary_learner(
							    a  ,					a  ,					max_epochs=a  ,					batch_size=a  ,					eval_freq=100  ,					igf_model_path=a  ,					)

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner

def _UpperCAmelCase   (	a						:	Tuple  ,					a						:	List[Any]  ,					a						:	str  ,					a						:	List[Any]=32  ,					a						:	Any=1000  ,					a						:	str=16  ,					a						:	Tuple=1.0  ,					a						:	str=recopy_gpta  ,					a						:	Any=None  ,					a						:	Optional[int]=10  ,					a						:	Tuple="gpt2_finetuned.pt"  ,					):
							snake_case__	  =       torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
							snake_case__	  =       RandomSampler(a )
							snake_case__	  =       DataLoader(a  ,					sampler=a )

							snake_case__	  =       max_steps // (len(a )) + 1
							snake_case__	  =       0
							snake_case__	  =       torch.zeros((1, context_len)  ,					dtype=torch.long  ,					device=a )
							snake_case__  , snake_case__  , snake_case__	  =       recopy_model(a  ,					a  ,					a )

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(a )
														secondary_learner.eval()
							snake_case__	  =       []
							snake_case__	  =       0

							snake_case__	  =       []
							snake_case__	  =       []

							# Compute the performance of the transformer model at the beginning
							snake_case__	  =       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()
																					snake_case__	  =       random.randint(0  ,					example.size(2 ) - context_len - 1 )
																					snake_case__	  =       example[0, 0, start : start + context_len]
																					lm_optimizer.zero_grad()
																					snake_case__	  =       model(a  ,					labels=a )
																					snake_case__	  =       True

																					if secondary_learner is not None:
																												snake_case__	  =       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 == 10:
																																			snake_case__	  =       -1
																												if predicted_q < threshold:
																																			snake_case__	  =       False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												snake_case__	  =       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()
																												snake_case__	  =       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:
																																			snake_case__	  =       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 > 60:
																												break
														if max_steps > 0 and global_step > 60:
																					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 _UpperCAmelCase   (	):
							snake_case__	  =       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=32  ,					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=100  ,					type=a  ,					help="""number of articles that are long enough to be used as our objective set"""  ,					)
							parser.add_argument(
							    """--eval_freq"""  ,					default=100  ,					type=a  ,					help="""secondary model evaluation is triggered at eval_freq""" )

							parser.add_argument("""--max_steps"""  ,					default=1000  ,					type=a  ,					help="""To calculate training epochs""" )

							parser.add_argument(
							    """--secondary_learner_batch_size"""  ,					default=128  ,					type=a  ,					help="""batch size of training data for secondary learner"""  ,					)

							parser.add_argument(
							    """--batch_size"""  ,					default=16  ,					type=a  ,					help="""batch size of training data of language model(gpt2) """ )

							parser.add_argument(
							    """--eval_interval"""  ,					default=10  ,					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=100  ,					type=a  ,					help="""The number of examples split to be used as objective_set/test_data""" )

							parser.add_argument(
							    """--min_len"""  ,					default=1026  ,					type=a  ,					help="""The minimum length of the article to be used as objective set""" )

							parser.add_argument(
							    """--secondary_learner_max_epochs"""  ,					default=15  ,					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=32  ,					max_steps=10  ,					size_objective_set=100  ,					min_len=1026  ,					trim=a  ,					data_file="""data/tokenized_stories_train_wikitext103.jbl"""  ,					igf_data_file="""igf_context_pairs.jbl"""  ,					)

							# Load train data for secondary learner
							snake_case__	  =       joblib.load("""data/IGF_values.jbl""" )

							# Train secondary learner
							snake_case__	  =       training_secondary_learner(
							    a  ,					secondary_learner_max_epochs=15  ,					secondary_learner_batch_size=128  ,					eval_freq=100  ,					igf_model_path="""igf_model.pt"""  ,					)

							# load pretrained gpt2 model
							snake_case__	  =       GPTaLMHeadModel.from_pretrained("""gpt2""" )
							set_seed(42 )

							# Generate train and test data to train and evaluate gpt2 model
							snake_case__  , snake_case__	  =       generate_datasets(
							    context_len=32  ,					file="""data/tokenized_stories_train_wikitext103.jbl"""  ,					number=100  ,					min_len=1026  ,					trim=a )

							# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
							finetune(
							    a  ,					a  ,					a  ,					context_len=32  ,					max_steps=1000  ,					batch_size=16  ,					threshold=1.0  ,					recopy_model=a  ,					secondary_learner=a  ,					eval_interval=10  ,					finetuned_model_name="""gpt2_finetuned.pt"""  ,					)


if __name__ == "__main__":
	main()
