

"""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       (     snake_case__=32		,				snake_case__=10		,				snake_case__=1_00		,				snake_case__=10_26		,				snake_case__=True		,				snake_case__="data/tokenized_stories_train_wikitext103.jbl"		,				snake_case__="igf_context_pairs.jbl"		,				):




			'''simple docstring'''


			set_seed(3    )
			# generate train_data and objective_set
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      generate_datasets(
			    snake_case__		,				snake_case__		,				number=snake_case__		,				min_len=10_26		,				trim=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?
			SCREAMING_SNAKE_CASE__								=      torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""    )

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

			# collect igf pairs and save to file demo.jbl
			collect_objective_set(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				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       (     snake_case__		,				snake_case__=15		,				snake_case__=1_28		,				snake_case__=1_00		,				snake_case__="igf_model.pt"		,				):




			'''simple docstring'''


			set_seed(42    )

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

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

			# Train secondary learner
			SCREAMING_SNAKE_CASE__								=      train_secondary_learner(
			    snake_case__		,				snake_case__		,				max_epochs=snake_case__		,				batch_size=snake_case__		,				eval_freq=1_00		,				igf_model_path=snake_case__		,				)

			del model, secondary_learner_train_data
			torch.cuda.empty_cache()

			return secondary_learner

def 	A       (     snake_case__		,				snake_case__		,				snake_case__		,				snake_case__=32		,				snake_case__=10_00		,				snake_case__=16		,				snake_case__=1.0		,				snake_case__=recopy_gpta		,				snake_case__=None		,				snake_case__=10		,				snake_case__="gpt2_finetuned.pt"		,				):




			'''simple docstring'''


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

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

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

			SCREAMING_SNAKE_CASE__								=      []
			SCREAMING_SNAKE_CASE__								=      []

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

									if secondary_learner is not None:
												SCREAMING_SNAKE_CASE__								=      secondary_learner.forward(
												    torch.tensor(snake_case__		,				dtype=torch.long		,				device=snake_case__    ).unsqueeze(0    )    )[0].item()
												observed_qs.append(float(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:
															SCREAMING_SNAKE_CASE__								=      -1
												if predicted_q < threshold:
															SCREAMING_SNAKE_CASE__								=      False

            # If we passed the filter, add the context to the batch!
									if do_backprop:
												contexts.append(np.array(context.cpu()    )    )
												SCREAMING_SNAKE_CASE__								=      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()
												SCREAMING_SNAKE_CASE__								=      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:
															SCREAMING_SNAKE_CASE__								=      compute_perplexity(snake_case__		,				snake_case__		,				snake_case__    )
															test_perps.append(snake_case__    )

															print("""Test perplexity, step"""		,				snake_case__		,				""":"""		,				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()		,				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'''


			SCREAMING_SNAKE_CASE__								=      argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task"""    )

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

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

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

			parser.add_argument(
			    """--context_len"""		,				default=32		,				type=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=1_00		,				type=snake_case__		,				help="""number of articles that are long enough to be used as our objective set"""		,				)
			parser.add_argument(
			    """--eval_freq"""		,				default=1_00		,				type=snake_case__		,				help="""secondary model evaluation is triggered at eval_freq"""    )

			parser.add_argument("""--max_steps"""		,				default=10_00		,				type=snake_case__		,				help="""To calculate training epochs"""    )

			parser.add_argument(
			    """--secondary_learner_batch_size"""		,				default=1_28		,				type=snake_case__		,				help="""batch size of training data for secondary learner"""		,				)

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

			parser.add_argument(
			    """--eval_interval"""		,				default=10		,				type=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=1_00		,				type=snake_case__		,				help="""The number of examples split to be used as objective_set/test_data"""    )

			parser.add_argument(
			    """--min_len"""		,				default=10_26		,				type=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=snake_case__		,				help="""number of epochs to train secondary learner"""    )

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

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

			parser.add_argument(
			    """--recopy_model"""		,				default=snake_case__		,				type=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=1_00		,				min_len=10_26		,				trim=snake_case__		,				data_file="""data/tokenized_stories_train_wikitext103.jbl"""		,				igf_data_file="""igf_context_pairs.jbl"""		,				)

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

			# Train secondary learner
			SCREAMING_SNAKE_CASE__								=      training_secondary_learner(
			    snake_case__		,				secondary_learner_max_epochs=15		,				secondary_learner_batch_size=1_28		,				eval_freq=1_00		,				igf_model_path="""igf_model.pt"""		,				)

			# load pretrained gpt2 model
			SCREAMING_SNAKE_CASE__								=      GPTaLMHeadModel.from_pretrained("""gpt2"""    )
			set_seed(42    )

			# Generate train and test data to train and evaluate gpt2 model
			SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      generate_datasets(
			    context_len=32		,				file="""data/tokenized_stories_train_wikitext103.jbl"""		,				number=1_00		,				min_len=10_26		,				trim=snake_case__    )

			# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
			finetune(
			    snake_case__		,				snake_case__		,				snake_case__		,				context_len=32		,				max_steps=10_00		,				batch_size=16		,				threshold=1.0		,				recopy_model=snake_case__		,				secondary_learner=snake_case__		,				eval_interval=10		,				finetuned_model_name="""gpt2_finetuned.pt"""		,				)


if __name__ == "__main__":
						main()





"""simple docstring"""




import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()





class    lowerCamelCase						(unittest.TestCase			):

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Any:
						torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      UNetaDModel(
						    block_out_channels=(3_2, 6_4)    ,				layers_per_block=2    ,				sample_size=3_2    ,				in_channels=3    ,				out_channels=3    ,				down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")    ,				up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")    ,				)
						return model





			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      self.dummy_uncond_unet
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""    ,				return_dict=__UpperCAmelCase       )[0]

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						SCREAMING_SNAKE_CASE__								=      image_from_tuple[0, -3:, -3:, -1]

						assert image.shape == (1, 3_2, 3_2, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2





@slow
@require_torch
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      """google/ncsnpp-celebahq-256"""
						SCREAMING_SNAKE_CASE__								=      UNetaDModel.from_pretrained(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      KarrasVeScheduler()

						SCREAMING_SNAKE_CASE__								=      KarrasVePipeline(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )
						pipe.to(__UpperCAmelCase       )
						pipe.set_progress_bar_config(disable=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe(num_inference_steps=2_0    ,				generator=__UpperCAmelCase    ,				output_type="""numpy"""       ).images

						SCREAMING_SNAKE_CASE__								=      image[0, -3:, -3:, -1]
						assert image.shape == (1, 2_5_6, 2_5_6, 3)
						SCREAMING_SNAKE_CASE__								=      np.array([0.578, 0.5_811, 0.5_924, 0.5_809, 0.587, 0.5_886, 0.5_861, 0.5_802, 0.586]       )

						assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2


