


'''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 			_lowercase       (				UpperCamelCase__       :					Optional[Any]=32,				UpperCamelCase__       :					List[Any]=10,				UpperCamelCase__       :					str=100,				UpperCamelCase__       :					Any=1026,				UpperCamelCase__       :					int=True,				UpperCamelCase__       :					str="data/tokenized_stories_train_wikitext103.jbl",				UpperCamelCase__       :					str="igf_context_pairs.jbl",				):
	set_seed(3			)
	# generate train_data and objective_set
	__A     ,__A  :		int       =			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?
	__A  :		str       =			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)

	# load pretrained model
	__A  :		Optional[int]       =			load_gpta('gpt2'			).to(UpperCamelCase__			)
	print('computing perplexity on objective set'			)
	__A  :		Union[str, Any]       =			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 			_lowercase       (				UpperCamelCase__       :					Union[str, Any],				UpperCamelCase__       :					Optional[int]=15,				UpperCamelCase__       :					Union[str, Any]=128,				UpperCamelCase__       :					List[Any]=100,				UpperCamelCase__       :					Optional[int]="igf_model.pt",				):
	set_seed(42			)

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

	# Initialize secondary learner to use embedding weights of model
	__A  :		Optional[int]       =			SecondaryLearner(UpperCamelCase__			)

	# Train secondary learner
	__A  :		Any       =			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 			_lowercase       (				UpperCamelCase__       :					Tuple,				UpperCamelCase__       :					int,				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Any=32,				UpperCamelCase__       :					Any=1000,				UpperCamelCase__       :					Union[str, Any]=16,				UpperCamelCase__       :					Dict=1.0,				UpperCamelCase__       :					List[str]=recopy_gpta,				UpperCamelCase__       :					List[str]=None,				UpperCamelCase__       :					List[str]=10,				UpperCamelCase__       :					List[str]="gpt2_finetuned.pt",				):
	__A  :		List[str]       =			torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'			)
	__A  :		Dict       =			RandomSampler(UpperCamelCase__			)
	__A  :		int       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__			)

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

	model.train()
	if secondary_learner is not None:
		secondary_learner.to(UpperCamelCase__			)
		secondary_learner.eval()
	__A  :		str       =			[]
	__A  :		Union[str, Any]       =			0

	__A  :		List[Any]       =			[]
	__A  :		str       =			[]

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

			if secondary_learner is not None:
				__A  :		List[str]       =			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:
					__A  :		Optional[int]       =			-1
				if predicted_q < threshold:
					__A  :		Tuple       =			False

            # If we passed the filter, add the context to the batch!
			if do_backprop:
				contexts.append(np.array(context.cpu()			)			)
				__A  :		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()
				__A  :		List[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:
					__A  :		Optional[Any]       =			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 			_lowercase       (				):
	__A  :		str       =			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
	__A  :		Optional[int]       =			joblib.load('data/IGF_values.jbl'			)

	# Train secondary learner
	__A  :		Optional[int]       =			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
	__A  :		List[Any]       =			GPTaLMHeadModel.from_pretrained('gpt2'			)
	set_seed(42			)

	# Generate train and test data to train and evaluate gpt2 model
	__A     ,__A  :		Optional[Any]       =			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'''
import unittest

from transformers import DonutProcessor


UpperCAmelCase_ :     str								=      'naver-clova-ix/donut-base'

class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			DonutProcessor.from_pretrained(__lowercase						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			{
      'name': 'John Doe',
      'age': '99',
      'city': 'Atlanta',
      'state': 'GA',
      'zip': '30301',
      'phone': '123-4567',
      'nicknames': [{'nickname': 'Johnny'}, {'nickname': 'JD'}],
  }

  __A  :		Optional[Any]       =			(
      '<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>'
      '<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>'
      '<s_nicknames><s_nickname>Johnny</s_nickname>'
      '<sep/><s_nickname>JD</s_nickname></s_nicknames>'
  )
  __A  :		List[Any]       =			self.processor.tokenajson(__lowercase						)

  self.assertDictEqual(__lowercase      ,      __lowercase						)






