





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__							(       UpperCamelCase__:							Optional[Any]=32					,   UpperCamelCase__:							List[Any]=10					,   UpperCamelCase__:							str=100					,   UpperCamelCase__:							Any=1_026					,   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
		SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      generate_datasets(
		    UpperCamelCase__					,   UpperCamelCase__					,   number=UpperCamelCase__					,   min_len=1_026					,   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?
		SCREAMING_SNAKE_CASE__			=      torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""    )

		# load pretrained model
		SCREAMING_SNAKE_CASE__			=      load_gpta("""gpt2"""    ).to(UpperCamelCase__    )
		print("""computing perplexity on objective set"""    )
		SCREAMING_SNAKE_CASE__			=      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 				SCREAMING_SNAKE_CASE__							(       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
		SCREAMING_SNAKE_CASE__			=      GPTaLMHeadModel.from_pretrained("""gpt2"""    )

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

		# Train secondary learner
		SCREAMING_SNAKE_CASE__			=      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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							int					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any=32					,   UpperCamelCase__:							Any=1_000					,   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"					,   ):
		SCREAMING_SNAKE_CASE__			=      torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""    )
		SCREAMING_SNAKE_CASE__			=      RandomSampler(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      DataLoader(UpperCamelCase__					,   sampler=UpperCamelCase__    )

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

		model.train()
		if secondary_learner is not None:
				secondary_learner.to(UpperCamelCase__    )
				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(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()
						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(UpperCamelCase__					,   labels=UpperCamelCase__    )
						SCREAMING_SNAKE_CASE__			=      True

						if secondary_learner is not None:
								SCREAMING_SNAKE_CASE__			=      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:
										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(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 				SCREAMING_SNAKE_CASE__							(       ):
		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=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=1_000					,   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=1_026					,   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=1_026					,   trim=UpperCamelCase__					,   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(
		    UpperCamelCase__					,   secondary_learner_max_epochs=15					,   secondary_learner_batch_size=128					,   eval_freq=100					,   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=100					,   min_len=1_026					,   trim=UpperCamelCase__    )

		# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
		finetune(
		    UpperCamelCase__					,   UpperCamelCase__					,   UpperCamelCase__					,   context_len=32					,   max_steps=1_000					,   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()






import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCamelCase   				=			logging.get_logger(__name__)  # pylint: disable=invalid-name






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		warnings.warn(
		    """The preprocess method is deprecated and will be removed in a future version. Please"""
		    """ use VaeImageProcessor.preprocess instead"""					,   UpperCamelCase__					,   )
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		if isinstance(image[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      image[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

				SCREAMING_SNAKE_CASE__			=      [np.array(i.resize((w, h)					,   resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      np.array(UpperCamelCase__    ).astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      image.transpose(0					,   3					,   1					,   2    )
				SCREAMING_SNAKE_CASE__			=      2.0 * image - 1.0
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(image[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return image






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[List, PIL.Image.Image, torch.Tensor]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return mask
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [mask]

		if isinstance(mask[0]					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      mask[0].size
				SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
				SCREAMING_SNAKE_CASE__			=      [np.array(m.convert("""L"""    ).resize((w, h)					,   resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
				SCREAMING_SNAKE_CASE__			=      np.concatenate(UpperCamelCase__					,   axis=0    )
				SCREAMING_SNAKE_CASE__			=      mask.astype(np.floataa    ) / 2_5_5.0
				SCREAMING_SNAKE_CASE__			=      0
				SCREAMING_SNAKE_CASE__			=      1
				SCREAMING_SNAKE_CASE__			=      torch.from_numpy(UpperCamelCase__    )
		elif isinstance(mask[0]					,   torch.Tensor    ):
				SCREAMING_SNAKE_CASE__			=      torch.cat(UpperCamelCase__					,   dim=0    )
		return mask




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					42
							lowerCamelCase_           =					42


							def __init__(							self    :Any							,				__A    :List[Any]							,				__A    :Optional[Any]		)   ->   Union[str, Any]:



									"""simple docstring"""



									super().__init__()
									self.register_modules(unet=__A							,				scheduler=__A		)


							@torch.no_grad()
							def __call__(							self    :str							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :Union[torch.Tensor, PIL.Image.Image]							,				__A    :int = 250							,				__A    :float = 0.0							,				__A    :int = 10							,				__A    :int = 10							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      image

									SCREAMING_SNAKE_CASE__			=      _preprocess_image(__A		)
									SCREAMING_SNAKE_CASE__			=      original_image.to(device=self.device							,				dtype=self.unet.dtype		)
									SCREAMING_SNAKE_CASE__			=      _preprocess_mask(__A		)
									SCREAMING_SNAKE_CASE__			=      mask_image.to(device=self.device							,				dtype=self.unet.dtype		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape[0]

									# sample gaussian noise to begin the loop
									if isinstance(__A							,				__A		) and len(__A		) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__A		)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

									SCREAMING_SNAKE_CASE__			=      original_image.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=self.device							,				dtype=self.unet.dtype		)

									# set step values
									self.scheduler.set_timesteps(__A							,				__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      eta

									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[0] + 1
									SCREAMING_SNAKE_CASE__			=      generator[0] if isinstance(__A							,				__A		) else generator
									for i, t in enumerate(self.progress_bar(self.scheduler.timesteps		)		):
											if t < t_last:
													# predict the noise residual
													SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample
													# compute previous image: x_t -> x_t-1
													SCREAMING_SNAKE_CASE__			=      self.scheduler.step(__A							,				__A							,				__A							,				__A							,				__A							,				__A		).prev_sample

											else:
													# compute the reverse: x_t-1 -> x_t
													SCREAMING_SNAKE_CASE__			=      self.scheduler.undo_step(__A							,				__A							,				__A		)
											SCREAMING_SNAKE_CASE__			=      t

									SCREAMING_SNAKE_CASE__			=      (image / 2 + 0.5).clamp(0							,				1		)
									SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=__A		)