





"""simple docstring"""


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							(SCREAMING_SNAKE_CASE=32							,     SCREAMING_SNAKE_CASE=10							,     SCREAMING_SNAKE_CASE=100							,     SCREAMING_SNAKE_CASE=1026							,     SCREAMING_SNAKE_CASE=True							,     SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"							,     SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"							,     ):
						set_seed(3     )
						# generate train_data and objective_set
						UpperCamelCase   ,						UpperCamelCase				:		Optional[int]				   =			generate_datasets(
						    SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     number=SCREAMING_SNAKE_CASE							,     min_len=1026							,     trim=SCREAMING_SNAKE_CASE     )
						# 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				:		Dict				   =			torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""     )

						# load pretrained model
						UpperCamelCase				:		Dict				   =			load_gpta("""gpt2"""     ).to(SCREAMING_SNAKE_CASE     )
						print("""computing perplexity on objective set"""     )
						UpperCamelCase				:		Dict				   =			compute_perplexity(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ).item()
						print("""perplexity on objective set:"""							,     SCREAMING_SNAKE_CASE     )

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

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

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE=15							,     SCREAMING_SNAKE_CASE=128							,     SCREAMING_SNAKE_CASE=100							,     SCREAMING_SNAKE_CASE="igf_model.pt"							,     ):
						set_seed(42     )

						# Load pre-trained model
						UpperCamelCase				:		int				   =			GPTaLMHeadModel.from_pretrained("""gpt2"""     )

						# Initialize secondary learner to use embedding weights of model
						UpperCamelCase				:		Tuple				   =			SecondaryLearner(SCREAMING_SNAKE_CASE     )

						# Train secondary learner
						UpperCamelCase				:		Dict				   =			train_secondary_learner(
						    SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     max_epochs=SCREAMING_SNAKE_CASE							,     batch_size=SCREAMING_SNAKE_CASE							,     eval_freq=100							,     igf_model_path=SCREAMING_SNAKE_CASE							,     )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner

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

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

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

						UpperCamelCase				:		Dict				   =			[]
						UpperCamelCase				:		Any				   =			[]

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

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

																								# 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				:		str				   =			-1
																								if predicted_q < threshold:
																														UpperCamelCase				:		List[Any]				   =			False

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

																														print("""Test perplexity, step"""							,     SCREAMING_SNAKE_CASE							,     """:"""							,     SCREAMING_SNAKE_CASE     )
            # 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()							,     SCREAMING_SNAKE_CASE     )
						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							():
						UpperCamelCase				:		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=SCREAMING_SNAKE_CASE							,     type=SCREAMING_SNAKE_CASE							,     required=SCREAMING_SNAKE_CASE							,     help="""The input data dir. Should contain data files for WikiText."""							,     )
						parser.add_argument(
						    """--model_name_or_path"""							,     default=SCREAMING_SNAKE_CASE							,     type=SCREAMING_SNAKE_CASE							,     required=SCREAMING_SNAKE_CASE							,     help="""Path to pretrained model or model identifier from huggingface.co/models"""							,     )
						parser.add_argument(
						    """--data_file"""							,     type=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     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=SCREAMING_SNAKE_CASE							,     default=SCREAMING_SNAKE_CASE							,     help="""A jbl file containing the context and information gain pairs to train secondary learner."""							,     )

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

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

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

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

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

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

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

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

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

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

						parser.add_argument(
						    """--threshold"""							,     default=1.0							,     type=SCREAMING_SNAKE_CASE							,     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=SCREAMING_SNAKE_CASE							,     help="""finetuned_model_name"""     )

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

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

						# Train secondary learner
						UpperCamelCase				:		List[str]				   =			training_secondary_learner(
						    SCREAMING_SNAKE_CASE							,     secondary_learner_max_epochs=15							,     secondary_learner_batch_size=128							,     eval_freq=100							,     igf_model_path="""igf_model.pt"""							,     )

						# load pretrained gpt2 model
						UpperCamelCase				:		int				   =			GPTaLMHeadModel.from_pretrained("""gpt2"""     )
						set_seed(42     )

						# Generate train and test data to train and evaluate gpt2 model
						UpperCamelCase   ,						UpperCamelCase				:		List[str]				   =			generate_datasets(
						    context_len=32							,     file="""data/tokenized_stories_train_wikitext103.jbl"""							,     number=100							,     min_len=1026							,     trim=SCREAMING_SNAKE_CASE     )

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


if __name__ == "__main__":
		main()









import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class    SCREAMING_SNAKE_CASE_				:



	"""simple docstring"""


	def __init__(					self		:						Tuple					,     lowerCAmelCase		:						Tuple	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Any             =      data
						__UpperCamelCase					:	List[str]             =      [0x6_7_4_5_2_3_0_1, 0xe_f_c_d_a_b_8_9, 0x9_8_b_a_d_c_f_e, 0x1_0_3_2_5_4_7_6, 0xc_3_d_2_e_1_f_0]






	@staticmethod
	def 					lowerCamelCase__     (					lowerCAmelCase		:						str					,     lowerCAmelCase		:						Union[str, Any]	)	->       str:







						"""simple docstring"""







						return ((n << b) | (n >> (32 - b))) & 0xf_f_f_f_f_f_f_f






	def 					lowerCamelCase__     (					self		:						Optional[int]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	int             =      b"""\x80""" + b"""\x00""" * (63 - (len(self.data	) + 8) % 64)
						__UpperCamelCase					:	int             =      self.data + padding + struct.pack(""">Q"""					,     8 * len(self.data	)	)
						return padded_data






	def 					lowerCamelCase__     (					self		:						str	)	->       str:







						"""simple docstring"""







						return [
						    self.padded_data[i : i + 64] for i in range(0					,     len(self.padded_data	)					,     64	)
						]






	def 					lowerCamelCase__     (					self		:						Union[str, Any]					,     lowerCAmelCase		:						Tuple	)	->       List[Any]:







						"""simple docstring"""







						__UpperCamelCase					:	Tuple             =      list(struct.unpack(""">16L"""					,     lowerCAmelCase	)	) + [0] * 64
						for i in range(16					,     80	):
											__UpperCamelCase					:	Dict             =      self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16])					,     1	)
						return w






	def 					lowerCamelCase__     (					self		:						Union[str, Any]	)	->       Dict:







						"""simple docstring"""







						__UpperCamelCase					:	Dict             =      self.padding()
						__UpperCamelCase					:	int             =      self.split_blocks()
						for block in self.blocks:
											__UpperCamelCase					:	Union[str, Any]             =      self.expand_block(lowerCAmelCase	)
											__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	str             =      self.h
											for i in range(0					,     80	):
																if 0 <= i < 20:
																					__UpperCamelCase					:	List[Any]             =      (b & c) | ((~b) & d)
																					__UpperCamelCase					:	str             =      0x5_a_8_2_7_9_9_9
																elif 20 <= i < 40:
																					__UpperCamelCase					:	Tuple             =      b ^ c ^ d
																					__UpperCamelCase					:	List[Any]             =      0x6_e_d_9_e_b_a_1
																elif 40 <= i < 60:
																					__UpperCamelCase					:	Tuple             =      (b & c) | (b & d) | (c & d)
																					__UpperCamelCase					:	List[str]             =      0x8_f_1_b_b_c_d_c
																elif 60 <= i < 80:
																					__UpperCamelCase					:	Optional[int]             =      b ^ c ^ d
																					__UpperCamelCase					:	Tuple             =      0xc_a_6_2_c_1_d_6
																__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase      ,			__UpperCamelCase					:	Tuple             =      (
																    self.rotate(lowerCAmelCase					,     5	) + f + e + k + expanded_block[i] & 0xf_f_f_f_f_f_f_f,
																    a,
																    self.rotate(lowerCAmelCase					,     30	),
																    c,
																    d,
																)
											__UpperCamelCase					:	List[Any]             =      (
											    self.h[0] + a & 0xf_f_f_f_f_f_f_f,
											    self.h[1] + b & 0xf_f_f_f_f_f_f_f,
											    self.h[2] + c & 0xf_f_f_f_f_f_f_f,
											    self.h[3] + d & 0xf_f_f_f_f_f_f_f,
											    self.h[4] + e & 0xf_f_f_f_f_f_f_f,
											)
						return ("{:08x}" * 5).format(*self.h	)






def 				A__ ()     ->						int:
					__UpperCamelCase					:	Optional[int]             =      b"""Test String"""
					assert SHAaHash(snake_case				).final_hash() == hashlib.shaa(snake_case				).hexdigest()  # noqa: S324





def 				A__ ()     ->						Tuple:
					__UpperCamelCase					:	Optional[Any]             =      argparse.ArgumentParser(description="""Process some strings or files"""				)
					parser.add_argument(
					    """--string"""		,	dest="""input_string"""		,	default="""Hello World!! Welcome to Cryptography"""		,	help="""Hash the string"""		,	)
					parser.add_argument("""--file"""		,	dest="""input_file"""		,	help="""Hash contents of a file"""				)
					__UpperCamelCase					:	List[Any]             =      parser.parse_args()
					__UpperCamelCase					:	List[str]             =      args.input_string
					# In any case hash input should be a bytestring
					if args.input_file:
										with open(args.input_file		,	"""rb"""				) as f:
															__UpperCamelCase					:	Tuple             =      f.read()
					else:
										__UpperCamelCase					:	Any             =      bytes(snake_case		,	"""utf-8"""				)
					print(SHAaHash(snake_case				).final_hash()				)


if __name__ == "__main__":
	main()
	import doctest

	doctest.testmod()






