
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"					, )     ->						str:
		'''simple docstring'''

		set_seed(3				)
		# generate train_data and objective_set
		UpperCamelCase			,						UpperCamelCase						=  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?
		UpperCamelCase						=  torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)

		# load pretrained model
		UpperCamelCase						=  load_gpta('gpt2'				).to(lowercase				)
		print('computing perplexity on objective set'				)
		UpperCamelCase						=  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"					, )     ->						Optional[Any]:
		'''simple docstring'''

		set_seed(42				)

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

		# Initialize secondary learner to use embedding weights of model
		UpperCamelCase						=  SecondaryLearner(lowercase				)

		# Train secondary learner
		UpperCamelCase						=  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"					, )     ->						List[str]:
		'''simple docstring'''

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

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

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

		UpperCamelCase						=  []
		UpperCamelCase						=  []

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

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

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()				)				)
								UpperCamelCase						=  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()
								UpperCamelCase						=  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:
										UpperCamelCase						=  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 (      )     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  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
		UpperCamelCase						=  joblib.load('data/IGF_values.jbl'				)

		# Train secondary learner
		UpperCamelCase						=  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
		UpperCamelCase						=  GPTaLMHeadModel.from_pretrained('gpt2'				)
		set_seed(42				)

		# Generate train and test data to train and evaluate gpt2 model
		UpperCamelCase			,						UpperCamelCase						=  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()


from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

_UpperCAmelCase      :				Tuple          = _symbol_database.Default()


_UpperCAmelCase      :				List[Any]          = _descriptor_pool.Default().AddSerializedFile(
    b"\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"
)

_UpperCAmelCase      :				int          = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, "sentencepiece_model_pb2", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					_UpperCAmelCase      :				int          = None
					_UpperCAmelCase      :				List[str]          = b"H\003"
					# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
					_UpperCAmelCase      :				Optional[Any]          = 45
					_UpperCAmelCase      :				Any          = 1_581
					_UpperCAmelCase      :				Tuple          = 1_517
					_UpperCAmelCase      :				List[str]          = 1_570
					_UpperCAmelCase      :				int          = 1_584
					_UpperCAmelCase      :				List[Any]          = 1_793
					_UpperCAmelCase      :				Optional[int]          = 1_795
					_UpperCAmelCase      :				Any          = 1_916
					_UpperCAmelCase      :				Tuple          = 1_864
					_UpperCAmelCase      :				List[Any]          = 1_905
					_UpperCAmelCase      :				Union[str, Any]          = 1_919
					_UpperCAmelCase      :				str          = 2_429
					_UpperCAmelCase      :				Any          = 2_208
					_UpperCAmelCase      :				Dict          = 2_418
					_UpperCAmelCase      :				Optional[Any]          = 2_323
					_UpperCAmelCase      :				Tuple          = 2_407
# @@protoc_insertion_point(module_scope)
