





"""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       a__					(					_SCREAMING_SNAKE_CASE=32  ,	_SCREAMING_SNAKE_CASE=10  ,	_SCREAMING_SNAKE_CASE=100  ,	_SCREAMING_SNAKE_CASE=1_026  ,	_SCREAMING_SNAKE_CASE=True  ,	_SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"  ,	_SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"  ,	):




		"""simple docstring"""


		set_seed(3    )
		# generate train_data and objective_set
		UpperCamelCase   ,				UpperCamelCase   =	generate_datasets(
		    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	number=_SCREAMING_SNAKE_CASE  ,	min_len=1_026  ,	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   =	torch.device("cuda:0" if torch.cuda.is_available() else "cpu"    )

		# load pretrained model
		UpperCamelCase   =	load_gpta("gpt2"    ).to(_SCREAMING_SNAKE_CASE    )
		print("computing perplexity on objective set"    )
		UpperCamelCase   =	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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=15  ,	_SCREAMING_SNAKE_CASE=128  ,	_SCREAMING_SNAKE_CASE=100  ,	_SCREAMING_SNAKE_CASE="igf_model.pt"  ,	):




		"""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(_SCREAMING_SNAKE_CASE    )

		# Train secondary learner
		UpperCamelCase   =	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       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=32  ,	_SCREAMING_SNAKE_CASE=1_000  ,	_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"  ,	):




		"""simple docstring"""


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

		UpperCamelCase   =	max_steps // (len(_SCREAMING_SNAKE_CASE    )) + 1
		UpperCamelCase   =	0
		UpperCamelCase   =	torch.zeros((1, context_len)  ,	dtype=torch.long  ,	device=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   =	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   =	[]
		UpperCamelCase   =	0

		UpperCamelCase   =	[]
		UpperCamelCase   =	[]

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

						if secondary_learner is not None:
								UpperCamelCase   =	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   =	-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(_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       a__					(					):




		"""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=_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=1_000  ,	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=1_026  ,	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=1_026  ,	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   =	joblib.load("data/IGF_values.jbl"    )

		# Train secondary learner
		UpperCamelCase   =	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   =	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=_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=1_000  ,	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()













"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__     			=			logging.get_logger(__name__)

lowerCAmelCase__     			=			{
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"swinv2"

						UpperCAmelCase_ =			{
						    "num_attention_heads": "num_heads",
						    "num_hidden_layers": "num_layers",
						}


						def __init__(self   ,							__a=2_24   ,							__a=4   ,							__a=3   ,							__a=96   ,							__a=[2, 2, 6, 2]   ,							__a=[3, 6, 12, 24]   ,							__a=7   ,							__a=4.0   ,							__a=True   ,							__a=0.0   ,							__a=0.0   ,							__a=0.1   ,							__a="gelu"   ,							__a=False   ,							__a=0.02   ,							__a=1e-5   ,							__a=32   ,							**__a   ,							) ->   Optional[Any]:
								super().__init__(**__a		)

								UpperCamelCase   =	image_size
								UpperCamelCase   =	patch_size
								UpperCamelCase   =	num_channels
								UpperCamelCase   =	embed_dim
								UpperCamelCase   =	depths
								UpperCamelCase   =	len(__a		)
								UpperCamelCase   =	num_heads
								UpperCamelCase   =	window_size
								UpperCamelCase   =	mlp_ratio
								UpperCamelCase   =	qkv_bias
								UpperCamelCase   =	hidden_dropout_prob
								UpperCamelCase   =	attention_probs_dropout_prob
								UpperCamelCase   =	drop_path_rate
								UpperCamelCase   =	hidden_act
								UpperCamelCase   =	use_absolute_embeddings
								UpperCamelCase   =	layer_norm_eps
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	encoder_stride
								# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCamelCase   =	int(embed_dim * 2 ** (len(__a		) - 1)		)
								UpperCamelCase   =	(0, 0, 0, 0)






