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	(    _lowercase=32		,  _lowercase=10		,  _lowercase=100		,  _lowercase=1_026		,  _lowercase=True		,  _lowercase="data/tokenized_stories_train_wikitext103.jbl"		,  _lowercase="igf_context_pairs.jbl"		,  ):
						set_seed(3						)
						# generate train_data and objective_set
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							generate_datasets(
						    _lowercase		,  _lowercase		,  number=_lowercase		,  min_len=1_026		,  trim=_lowercase						)
						# 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							:    Any							=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)

						# load pretrained model
						SCREAMING_SNAKE_CASE							:    str							=							load_gpta('''gpt2'''						).to(_lowercase						)
						print('''computing perplexity on objective set'''						)
						SCREAMING_SNAKE_CASE							:    Any							=							compute_perplexity(_lowercase		,  _lowercase		,  _lowercase						).item()
						print('''perplexity on objective set:'''		,  _lowercase						)

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

						# clean up, delete model and data we don't need anymore
						del model, train_data, objective_set
						torch.cuda.empty_cache()
def       A	(    _lowercase		,  _lowercase=15		,  _lowercase=128		,  _lowercase=100		,  _lowercase="igf_model.pt"		,  ):
						set_seed(42						)

						# Load pre-trained model
						SCREAMING_SNAKE_CASE							:    List[Any]							=							GPTaLMHeadModel.from_pretrained('''gpt2'''						)

						# Initialize secondary learner to use embedding weights of model
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							SecondaryLearner(_lowercase						)

						# Train secondary learner
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							train_secondary_learner(
						    _lowercase		,  _lowercase		,  max_epochs=_lowercase		,  batch_size=_lowercase		,  eval_freq=100		,  igf_model_path=_lowercase		,  )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner
def       A	(    _lowercase		,  _lowercase		,  _lowercase		,  _lowercase=32		,  _lowercase=1_000		,  _lowercase=16		,  _lowercase=1.0		,  _lowercase=recopy_gpta		,  _lowercase=None		,  _lowercase=10		,  _lowercase="gpt2_finetuned.pt"		,  ):
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''						)
						SCREAMING_SNAKE_CASE							:    Dict							=							RandomSampler(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[Any]							=							DataLoader(_lowercase		,  sampler=_lowercase						)

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

						model.train()
						if secondary_learner is not None:
												secondary_learner.to(_lowercase						)
												secondary_learner.eval()
						SCREAMING_SNAKE_CASE							:    Tuple							=							[]
						SCREAMING_SNAKE_CASE							:    Any							=							0

						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[]
						SCREAMING_SNAKE_CASE							:    Any							=							[]

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

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

																								# 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:
																														SCREAMING_SNAKE_CASE							:    Tuple							=							-1
																								if predicted_q < threshold:
																														SCREAMING_SNAKE_CASE							:    Optional[int]							=							False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()						)						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							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							:    Optional[int]							=							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							:    int							=							compute_perplexity(_lowercase		,  _lowercase		,  _lowercase						)
																														test_perps.append(_lowercase						)

																														print('''Test perplexity, step'''		,  _lowercase		,  ''':'''		,  _lowercase						)
            # 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()		,  _lowercase						)
						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	(    ):
						SCREAMING_SNAKE_CASE							:    List[Any]							=							argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''						)

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

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

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

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

						parser.add_argument('''--max_steps'''		,  default=1_000		,  type=_lowercase		,  help='''To calculate training epochs'''						)

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

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

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

						parser.add_argument(
						    '''--min_len'''		,  default=1_026		,  type=_lowercase		,  help='''The minimum length of the article to be used as objective set'''						)

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

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

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

						parser.add_argument(
						    '''--recopy_model'''		,  default=_lowercase		,  type=_lowercase		,  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=1_026		,  trim=_lowercase		,  data_file='''data/tokenized_stories_train_wikitext103.jbl'''		,  igf_data_file='''igf_context_pairs.jbl'''		,  )

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

						# Train secondary learner
						SCREAMING_SNAKE_CASE							:    int							=							training_secondary_learner(
						    _lowercase		,  secondary_learner_max_epochs=15		,  secondary_learner_batch_size=128		,  eval_freq=100		,  igf_model_path='''igf_model.pt'''		,  )

						# load pretrained gpt2 model
						SCREAMING_SNAKE_CASE							:    str							=							GPTaLMHeadModel.from_pretrained('''gpt2'''						)
						set_seed(42						)

						# Generate train and test data to train and evaluate gpt2 model
						SCREAMING_SNAKE_CASE				,  SCREAMING_SNAKE_CASE							:    str							=							generate_datasets(
						    context_len=32		,  file='''data/tokenized_stories_train_wikitext103.jbl'''		,  number=100		,  min_len=1_026		,  trim=_lowercase						)

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    _lowercase		,  _lowercase		,  _lowercase		,  context_len=32		,  max_steps=1_000		,  batch_size=16		,  threshold=1.0		,  recopy_model=_lowercase		,  secondary_learner=_lowercase		,  eval_interval=10		,  finetuned_model_name='''gpt2_finetuned.pt'''		,  )


if __name__ == "__main__":
					main()






import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
					import tensorflow as tf

if is_keras_nlp_available():
					from transformers.models.gpta import TFGPTaTokenizer


__UpperCamelCase		:       Optional[Any]									=     ['gpt2']
__UpperCamelCase		:       str									=     'gpt2'

if is_tf_available():





					class 					lowercase__						(   tf.Module):


											def __init__(       self	:				Optional[Any]    ,		UpperCamelCase__	:				Union[str, Any]						):





																	'''simple docstring'''





																	super().__init__()
																	SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer
																	SCREAMING_SNAKE_CASE							:    int							=							AutoConfig.from_pretrained(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Dict							=							TFGPTaLMHeadModel.from_config(UpperCamelCase__						)



											@tf.function(input_signature=(tf.TensorSpec((None,)    ,		tf.string    ,		name='''text'''						),)						)
											def        __A       (       self	:				str    ,		UpperCamelCase__	:				int						):





																	'''simple docstring'''





																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.tokenizer(UpperCamelCase__						)
																	SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tokenized['''input_ids'''].to_tensor()

																	SCREAMING_SNAKE_CASE							:    Any							=							tf.cast(input_ids_dense > 0    ,		tf.intaa						)
																	# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

																	SCREAMING_SNAKE_CASE							:    List[Any]							=							self.model(input_ids=UpperCamelCase__    ,		attention_mask=UpperCamelCase__						)['''logits''']

																	return outputs





@require_tf
@require_keras_nlp
class 					lowercase__						(   unittest.TestCase):


						def        __A       (       self	:				int						):





												'''simple docstring'''





												super().setUp()

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[GPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in (TOKENIZER_CHECKPOINTS)]
												SCREAMING_SNAKE_CASE							:    List[str]							=							[TFGPTaTokenizer.from_pretrained(UpperCamelCase__						) for checkpoint in TOKENIZER_CHECKPOINTS]
												assert len(self.tokenizers						) == len(self.tf_tokenizers						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							[
												    '''This is a straightforward English test sentence.''',
												    '''This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.''',
												    '''Now we\'re going to add some Chinese: 一 二 三 一二三''',
												    '''And some much more rare Chinese: 齉 堃 齉堃''',
												    '''Je vais aussi écrire en français pour tester les accents''',
												    '''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
												]
												SCREAMING_SNAKE_CASE							:    Dict							=							list(zip(self.test_sentences    ,		self.test_sentences[::-1]						)						)


						def        __A       (       self	:				str						):





												'''simple docstring'''





												for tokenizer, tf_tokenizer in zip(self.tokenizers    ,		self.tf_tokenizers						):
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tokenizer([test_inputs]    ,		return_tensors='''tf'''						)
																								SCREAMING_SNAKE_CASE							:    Any							=							tf_tokenizer([test_inputs]						)

																								for key in python_outputs.keys():
																														# convert them to numpy to avoid messing with ragged tensors
																														SCREAMING_SNAKE_CASE							:    int							=							python_outputs[key].numpy()
																														SCREAMING_SNAKE_CASE							:    int							=							tf_outputs[key].numpy()

																														self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape						)						)
																														self.assertTrue(tf.reduce_all(tf.cast(UpperCamelCase__    ,		tf.intaa						) == tf_outputs_values						)						)


						@slow
						def        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.function(UpperCamelCase__						)
																		for test_inputs in self.test_sentences:
																								SCREAMING_SNAKE_CASE							:    Optional[int]							=							tf.constant(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[str]							=							compiled_tokenizer(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    List[Any]							=							tf_tokenizer(UpperCamelCase__						)

																								for key in eager_outputs.keys():
																														self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]						)						)


						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    str							=							ModelToSave(tokenizer=UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							model.serving(UpperCamelCase__						)  # Build model with some sample inputs
																		with TemporaryDirectory() as tempdir:
																								SCREAMING_SNAKE_CASE							:    List[str]							=							Path(UpperCamelCase__						) / '''saved.model'''
																								tf.saved_model.save(UpperCamelCase__    ,		UpperCamelCase__    ,		signatures={'''serving_default''': model.serving}						)
																								SCREAMING_SNAKE_CASE							:    str							=							tf.saved_model.load(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    int							=							loaded_model.signatures['''serving_default'''](UpperCamelCase__						)['''output_0''']
																		# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																		self.assertTrue(tf.reduce_all(out == loaded_output						)						)


						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																		SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__						)  # Build model with some sample inputs

																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							tf_tokenizer.get_config()
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							TFGPTaTokenizer.from_config(UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    str							=							model_from_config(UpperCamelCase__						)

																		for key in from_config_output.keys():
																								self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]						)						)



						@slow
						def        __A       (       self	:				Optional[int]						):





												'''simple docstring'''





												for tf_tokenizer in self.tf_tokenizers:
																		# for the test to run
																		SCREAMING_SNAKE_CASE							:    Tuple							=							12_3123

																		for max_length in [3, 5, 1024]:
																								SCREAMING_SNAKE_CASE							:    Dict							=							tf.convert_to_tensor([self.test_sentences[0]]						)
																								SCREAMING_SNAKE_CASE							:    Tuple							=							tf_tokenizer(UpperCamelCase__    ,		max_length=UpperCamelCase__						)

																								SCREAMING_SNAKE_CASE							:    Tuple							=							out['''input_ids'''].numpy().shape[1]

																								assert out_length == max_length





