
'''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 	_snake_case						(      A_       :    Union[str, Any]=32     ,		A_       :    str=10     ,		A_       :    List[Any]=100     ,		A_       :    str=1026     ,		A_       :    int=True     ,		A_       :    str="data/tokenized_stories_train_wikitext103.jbl"     ,		A_       :    int="igf_context_pairs.jbl"     ,		):
		"""simple docstring"""







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

		# load pretrained model
		a_      :	List[str]      =							load_gpta("""gpt2"""   ).to(__SCREAMING_SNAKE_CASE   )
		print("""computing perplexity on objective set"""   )
		a_      :	Any      =							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 	_snake_case						(      A_       :    str     ,		A_       :    List[Any]=15     ,		A_       :    Dict=128     ,		A_       :    Optional[int]=100     ,		A_       :    int="igf_model.pt"     ,		):
		"""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(__SCREAMING_SNAKE_CASE   )

		# Train secondary learner
		a_      :	Dict      =							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 	_snake_case						(      A_       :    str     ,		A_       :    int     ,		A_       :    int     ,		A_       :    Union[str, Any]=32     ,		A_       :    Optional[Any]=1000     ,		A_       :    Optional[Any]=16     ,		A_       :    Optional[Any]=1.0     ,		A_       :    List[Any]=recopy_gpta     ,		A_       :    Any=None     ,		A_       :    List[Any]=10     ,		A_       :    Dict="gpt2_finetuned.pt"     ,		):
		"""simple docstring"""







		a_      :	Dict      =							torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""   )
		a_      :	Union[str, Any]      =							RandomSampler(__SCREAMING_SNAKE_CASE   )
		a_      :	str      =							DataLoader(__SCREAMING_SNAKE_CASE     ,		sampler=__SCREAMING_SNAKE_CASE   )

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

		a_      :	Optional[int]      =							[]
		a_      :	List[str]      =							[]

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

						if secondary_learner is not None:
								a_      :	Union[str, Any]      =							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:
										a_      :	Any      =							-1
								if predicted_q < threshold:
										a_      :	Optional[Any]      =							False

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu()   )   )
								a_      :	str      =							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_      :	List[str]      =							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 	_snake_case						(      ):
		"""simple docstring"""







		a_      :	List[str]      =							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=1000     ,		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=1026     ,		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=1026     ,		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
		a_      :	Optional[Any]      =							joblib.load("""data/IGF_values.jbl"""   )

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

		# Generate train and test data to train and evaluate gpt2 model
		a_      , a_      :	Union[str, Any]      =							generate_datasets(
		    context_len=32     ,		file="""data/tokenized_stories_train_wikitext103.jbl"""     ,		number=100     ,		min_len=1026     ,		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=1000     ,		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 dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available


@dataclass
class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42


if is_transformers_available() and is_torch_available():
    from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline


