



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 snake_case__	(      __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"      ,  )		->				Dict:
		set_seed(3			)
		# generate train_data and objective_set
		UpperCAmelCase_       ,    UpperCAmelCase_						       =					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?
		UpperCAmelCase_						       =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"			)

		# load pretrained model
		UpperCAmelCase_						       =					load_gpta("gpt2"			).to(UpperCAmelCase_			)
		print("computing perplexity on objective set"			)
		UpperCAmelCase_						       =					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 snake_case__	(      __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE=15      ,  __SCREAMING_SNAKE_CASE=128      ,  __SCREAMING_SNAKE_CASE=100      ,  __SCREAMING_SNAKE_CASE="igf_model.pt"      ,  )		->				Any:
		set_seed(42			)

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

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

		# Train secondary learner
		UpperCAmelCase_						       =					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 snake_case__	(      __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"      ,  )		->				List[str]:
		UpperCAmelCase_						       =					torch.device("cuda:0" if torch.cuda.is_available() else "cpu"			)
		UpperCAmelCase_						       =					RandomSampler(UpperCAmelCase_			)
		UpperCAmelCase_						       =					DataLoader(UpperCAmelCase_      ,  sampler=UpperCAmelCase_			)

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

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

		UpperCAmelCase_						       =					[]
		UpperCAmelCase_						       =					[]

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

						if secondary_learner is not None:
								UpperCAmelCase_						       =					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:
										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(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 snake_case__	(      )		->				List[Any]:
		UpperCAmelCase_						       =					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
		UpperCAmelCase_						       =					joblib.load("data/IGF_values.jbl"			)

		# Train secondary learner
		UpperCAmelCase_						       =					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
		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=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()








'''simple docstring'''




from collections import Counter
from timeit import timeit




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""					,				)   ->bool:
	return sum(c % 2 for c in Counter(input_str.replace(' '					,				''							).lower()							).values()							) < 2




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->bool:
	if len(UpperCAmelCase_							) == 0:
		return True
	snake_case__	       =   input_str.replace(' '					,				''							).lower()
	# character_freq_dict: Stores the frequency of every character in the input string
	snake_case__	       =   {}

	for character in lower_case_input_str:
		snake_case__	       =   character_freq_dict.get(UpperCAmelCase_					,				0							) + 1
	snake_case__	       =   0

	for character_count in character_freq_dict.values():
		if character_count % 2:
			odd_char += 1
	if odd_char > 1:
		return False
	return True




def 						__lowerCamelCase			(	UpperCAmelCase_ = ""							)   ->None:
	print('\nFor string = '					,				UpperCAmelCase_					,				':'							)
	print(
	    '> can_string_be_rearranged_as_palindrome_counter()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome_counter(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome_counter(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)
	print(
	    '> can_string_be_rearranged_as_palindrome()'					,				'\tans ='					,				can_string_be_rearranged_as_palindrome(UpperCAmelCase_							)					,				'\ttime ='					,				timeit(
	        'z.can_string_be_rearranged_as_palindrome(z.check_str)'					,				setup='import __main__ as z'					,				)					,				'seconds'					,				)


if __name__ == "__main__":
			a__					:			str          =     input(
			    '''Enter string to determine if it can be rearranged as a palindrome or not: '''
			).strip()
			benchmark(check_str)
			a__					:			List[Any]          =     can_string_be_rearranged_as_palindrome_counter(check_str)
			print(f"""{check_str} can {'' if status else 'not '}be rearranged as a palindrome""")



