




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(       UpperCamelCase__							:			str=32				,       UpperCamelCase__							:			Optional[int]=10				,       UpperCamelCase__							:			Tuple=100				,       UpperCamelCase__							:			Optional[int]=1026				,       UpperCamelCase__							:			int=True				,       UpperCamelCase__							:			Union[str, Any]="data/tokenized_stories_train_wikitext103.jbl"				,       UpperCamelCase__							:			Tuple="igf_context_pairs.jbl"				,       )    -> Union[str, Any]:
							'''simple docstring'''


							set_seed(3				)
							# generate train_data and objective_set
							__lowercase     ,     __lowercase      =    generate_datasets(
							    UpperCamelCase__				,       UpperCamelCase__				,       number=UpperCamelCase__				,       min_len=1026				,       trim=UpperCamelCase__				)
							# 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?
							__lowercase      =    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''				)

							# load pretrained model
							__lowercase      =    load_gpta('''gpt2'''				).to(UpperCamelCase__				)
							print('''computing perplexity on objective set'''				)
							__lowercase      =    compute_perplexity(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				).item()
							print('''perplexity on objective set:'''				,       UpperCamelCase__				)

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

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







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Union[str, Any]=15				,       UpperCamelCase__							:			List[str]=128				,       UpperCamelCase__							:			List[Any]=100				,       UpperCamelCase__							:			str="igf_model.pt"				,       )    -> str:
							'''simple docstring'''


							set_seed(42				)

							# Load pre-trained model
							__lowercase      =    GPTaLMHeadModel.from_pretrained('''gpt2'''				)

							# Initialize secondary learner to use embedding weights of model
							__lowercase      =    SecondaryLearner(UpperCamelCase__				)

							# Train secondary learner
							__lowercase      =    train_secondary_learner(
							    UpperCamelCase__				,       UpperCamelCase__				,       max_epochs=UpperCamelCase__				,       batch_size=UpperCamelCase__				,       eval_freq=100				,       igf_model_path=UpperCamelCase__				,       )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]				,       UpperCamelCase__							:			Optional[Any]=32				,       UpperCamelCase__							:			Dict=1000				,       UpperCamelCase__							:			List[Any]=16				,       UpperCamelCase__							:			Optional[Any]=1.0				,       UpperCamelCase__							:			str=recopy_gpta				,       UpperCamelCase__							:			Optional[int]=None				,       UpperCamelCase__							:			Dict=10				,       UpperCamelCase__							:			Any="gpt2_finetuned.pt"				,       )    -> List[str]:
							'''simple docstring'''


							__lowercase      =    torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''				)
							__lowercase      =    RandomSampler(UpperCamelCase__				)
							__lowercase      =    DataLoader(UpperCamelCase__				,       sampler=UpperCamelCase__				)

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

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

							__lowercase      =    []
							__lowercase      =    []

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

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

																												# 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:
																																			__lowercase      =    -1
																												if predicted_q < threshold:
																																			__lowercase      =    False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()				)				)
																												__lowercase      =    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()
																												__lowercase      =    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:
																																			__lowercase      =    compute_perplexity(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				)
																																			test_perps.append(UpperCamelCase__				)

																																			print('''Test perplexity, step'''				,       UpperCamelCase__				,       ''':'''				,       UpperCamelCase__				)
            # 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()				,       UpperCamelCase__				)
							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(       )    -> Dict:
							'''simple docstring'''


							__lowercase      =    argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''				)

							# Required parameters
							parser.add_argument(
							    '''--data_dir'''				,       default=UpperCamelCase__				,       type=UpperCamelCase__				,       required=UpperCamelCase__				,       help='''The input data dir. Should contain data files for WikiText.'''				,       )
							parser.add_argument(
							    '''--model_name_or_path'''				,       default=UpperCamelCase__				,       type=UpperCamelCase__				,       required=UpperCamelCase__				,       help='''Path to pretrained model or model identifier from huggingface.co/models'''				,       )
							parser.add_argument(
							    '''--data_file'''				,       type=UpperCamelCase__				,       default=UpperCamelCase__				,       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=UpperCamelCase__				,       default=UpperCamelCase__				,       help='''A jbl file containing the context and information gain pairs to train secondary learner.'''				,       )

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

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

							parser.add_argument(
							    '''--context_len'''				,       default=32				,       type=UpperCamelCase__				,       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=UpperCamelCase__				,       help='''number of articles that are long enough to be used as our objective set'''				,       )
							parser.add_argument(
							    '''--eval_freq'''				,       default=100				,       type=UpperCamelCase__				,       help='''secondary model evaluation is triggered at eval_freq'''				)

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

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

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

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

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

							parser.add_argument(
							    '''--secondary_learner_max_epochs'''				,       default=15				,       type=UpperCamelCase__				,       help='''number of epochs to train secondary learner'''				)

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

							parser.add_argument(
							    '''--threshold'''				,       default=1.0				,       type=UpperCamelCase__				,       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=UpperCamelCase__				,       help='''finetuned_model_name'''				)

							parser.add_argument(
							    '''--recopy_model'''				,       default=UpperCamelCase__				,       type=UpperCamelCase__				,       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=UpperCamelCase__				,       data_file='''data/tokenized_stories_train_wikitext103.jbl'''				,       igf_data_file='''igf_context_pairs.jbl'''				,       )

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

							# Train secondary learner
							__lowercase      =    training_secondary_learner(
							    UpperCamelCase__				,       secondary_learner_max_epochs=15				,       secondary_learner_batch_size=128				,       eval_freq=100				,       igf_model_path='''igf_model.pt'''				,       )

							# load pretrained gpt2 model
							__lowercase      =    GPTaLMHeadModel.from_pretrained('''gpt2'''				)
							set_seed(42				)

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

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


if __name__ == "__main__":
			main()












import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				     = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")







@require_sentencepiece
@require_tokenizers
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		GPTSwaTokenizer
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		True
						UpperCamelCase_	:       Optional[Any]       	=		False






						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													super().setUp()

													# We have a SentencePiece fixture for testing
													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				,							eos_token='''<unk>'''				,							bos_token='''<unk>'''				,							pad_token='''<unk>'''				)

													tokenizer.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Dict				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''This is a test'''
													__lowercase      =    '''This is a test'''
													return input_text, output_text






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''<s>'''
													__lowercase      =    1

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


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

													self.assertEqual(vocab_keys[0]				,							'''<unk>'''				)
													self.assertEqual(vocab_keys[1]				,							'''<s>'''				)
													self.assertEqual(vocab_keys[-1]				,							'''j'''				)
													self.assertEqual(len(lowerCamelCase__				)				,							2_000				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


													self.assertEqual(self.get_tokenizer().vocab_size				,							2_000				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)

													__lowercase      =    tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCamelCase__				,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)				,							[465, 287, 265, 631, 842]				)

													__lowercase      =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				,							)
													# fmt: on

													__lowercase      =    tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
													self.assertListEqual(
													    lowerCamelCase__				,							[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]				,							)

													__lowercase      =    tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				)
													# fmt: on






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)
													__lowercase      =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
													__lowercase      =    [
													    [465, 287, 265, 631, 842],
													    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
													]

													# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
													for text, expected_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertListEqual(tokenizer.encode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)

													# Test that decode_fast returns the input text
													for text, token_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertEqual(tokenizer.decode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    [
													    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
													    '''Hey there, how are you doing this fine day?''',
													    '''This is a text with a trailing spaces followed by a dot     .''',
													    '''Häj sväjs lillebrör! =)''',
													    '''Det är inget fel på Mr. Cool''',
													]

													# fmt: off
													__lowercase      =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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], [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]]}
													# fmt: on
													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCamelCase__				,							model_name='''AI-Sweden/gpt-sw3-126m'''				,							sequences=lowerCamelCase__				,							)






