





"""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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       str=32       ,			_lowercase		:       Any=10       ,			_lowercase		:       Tuple=100       ,			_lowercase		:       int=1026       ,			_lowercase		:       str=True       ,			_lowercase		:       List[str]="data/tokenized_stories_train_wikitext103.jbl"       ,			_lowercase		:       Dict="igf_context_pairs.jbl"       ,			)					->Dict:






	'''simple docstring'''






	set_seed(3   )
	# generate train_data and objective_set
	a,			a :						str      =							generate_datasets(
	    _lowercase       ,			_lowercase       ,			number=_lowercase       ,			min_len=1026       ,			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?
	a :						List[Any]      =							torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )

	# load pretrained model
	a :						Union[str, Any]      =							load_gpta("gpt2"   ).to(_lowercase   )
	print("computing perplexity on objective set"   )
	a :						Optional[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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       Union[str, Any]       ,			_lowercase		:       Any=15       ,			_lowercase		:       int=128       ,			_lowercase		:       Optional[Any]=100       ,			_lowercase		:       Tuple="igf_model.pt"       ,			)					->int:






	'''simple docstring'''






	set_seed(42   )

	# Load pre-trained model
	a :						Dict      =							GPTaLMHeadModel.from_pretrained("gpt2"   )

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

	# Train secondary learner
	a :						Optional[int]      =							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  _SCREAMING_SNAKE_CASE						(      _lowercase		:       List[Any]       ,			_lowercase		:       str       ,			_lowercase		:       Optional[Any]       ,			_lowercase		:       Union[str, Any]=32       ,			_lowercase		:       Tuple=1000       ,			_lowercase		:       List[Any]=16       ,			_lowercase		:       List[str]=1.0       ,			_lowercase		:       Any=recopy_gpta       ,			_lowercase		:       Tuple=None       ,			_lowercase		:       Union[str, Any]=10       ,			_lowercase		:       List[str]="gpt2_finetuned.pt"       ,			)					->str:






	'''simple docstring'''






	a :						Any      =							torch.device("cuda:0" if torch.cuda.is_available() else "cpu"   )
	a :						List[Any]      =							RandomSampler(_lowercase   )
	a :						List[Any]      =							DataLoader(_lowercase       ,			sampler=_lowercase   )

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

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

	a :						List[Any]      =							[]
	a :						List[Any]      =							[]

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

			if secondary_learner is not None:
				a :						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:
					a :						List[Any]      =							-1
				if predicted_q < threshold:
					a :						Optional[int]      =							False

            # If we passed the filter, add the context to the batch!
			if do_backprop:
				contexts.append(np.array(context.cpu()   )   )
				a :						int      =							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 :						Union[str, 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 :						Any      =							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  _SCREAMING_SNAKE_CASE						(      )					->Optional[Any]:






	'''simple docstring'''






	a :						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=1000       ,			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=1026       ,			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=1026       ,			trim=_lowercase       ,			data_file="data/tokenized_stories_train_wikitext103.jbl"       ,			igf_data_file="igf_context_pairs.jbl"       ,			)

	# Load train data for secondary learner
	a :						Optional[Any]      =							joblib.load("data/IGF_values.jbl"   )

	# Train secondary learner
	a :						Optional[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
	a :						List[Any]      =							GPTaLMHeadModel.from_pretrained("gpt2"   )
	set_seed(42   )

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

	# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
	finetune(
	    _lowercase       ,			_lowercase       ,			_lowercase       ,			context_len=32       ,			max_steps=1000       ,			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()









"""simple docstring"""




from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
a		:					int	      =     numpy.array([0, 0])
a		:					Optional[Any]	      =     numpy.array([0.5, 0.866_0254])
a		:					Tuple	      =     numpy.array([1, 0])
a		:					List[str]	      =     [VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]       ,			_lowercase		:       int   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						List[str]      =							initial_vectors
	for _ in range(_lowercase   ):
		a :						Optional[Any]      =							iteration_step(_lowercase   )
	return vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->list[numpy.ndarray]:






	'''simple docstring'''






	a :						Union[str, Any]      =							[]
	for i, start_vector in enumerate(vectors[:-1]   ):
		a :						str      =							vectors[i + 1]
		new_vectors.append(_lowercase   )
		a :						Optional[int]      =							end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3   )
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3       ,			60   )   )
		new_vectors.append(start_vector + difference_vector * 2 / 3   )
	new_vectors.append(vectors[-1]   )
	return new_vectors






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       numpy.ndarray       ,			_lowercase		:       float   )					->numpy.ndarray:






	'''simple docstring'''






	a :						int      =							numpy.radians(_lowercase   )
	a,			a :						Optional[int]      =							numpy.cos(_lowercase   ), numpy.sin(_lowercase   )
	a :						Dict      =							numpy.array(((c, -s), (s, c))   )
	return numpy.dot(_lowercase       ,			_lowercase   )






def  _SCREAMING_SNAKE_CASE						(      _lowercase		:       list[numpy.ndarray]   )					->None:






	'''simple docstring'''






	a :						Dict      =							plt.gca()
	axes.set_aspect("equal"   )

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	a,			a :						Any      =							zip(*_lowercase   )
	plt.plot(_lowercase       ,			_lowercase   )
	plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		a		:					Optional[int]	      =     iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)


