
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(lowerCAmelCase=3_2						,  lowerCAmelCase=1_0						,  lowerCAmelCase=1_0_0						,  lowerCAmelCase=1_0_2_6						,  lowerCAmelCase=True						,  lowerCAmelCase="data/tokenized_stories_train_wikitext103.jbl"						,  lowerCAmelCase="igf_context_pairs.jbl"						,  )		->				Tuple:

		"""simple docstring"""






		set_seed(3 )
		# generate train_data and objective_set
		_UpperCamelCase    ,	_UpperCamelCase						    =	generate_datasets(
		    lowerCAmelCase						,  lowerCAmelCase						,  number=lowerCAmelCase						,  min_len=1_0_2_6						,  trim=lowerCAmelCase )
		# 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(lowerCAmelCase )
		print("""computing perplexity on objective set""" )
		_UpperCamelCase						    =	compute_perplexity(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase ).item()
		print("""perplexity on objective set:"""						,  lowerCAmelCase )

		# collect igf pairs and save to file demo.jbl
		collect_objective_set(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )

		# clean up, delete model and data we don't need anymore
		del model, train_data, objective_set
		torch.cuda.empty_cache()


def 							__A(lowerCAmelCase						,  lowerCAmelCase=1_5						,  lowerCAmelCase=1_2_8						,  lowerCAmelCase=1_0_0						,  lowerCAmelCase="igf_model.pt"						,  )		->				Tuple:

		"""simple docstring"""






		set_seed(4_2 )

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

		# Initialize secondary learner to use embedding weights of model
		_UpperCamelCase						    =	SecondaryLearner(lowerCAmelCase )

		# Train secondary learner
		_UpperCamelCase						    =	train_secondary_learner(
		    lowerCAmelCase						,  lowerCAmelCase						,  max_epochs=lowerCAmelCase						,  batch_size=lowerCAmelCase						,  eval_freq=1_0_0						,  igf_model_path=lowerCAmelCase						,  )

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=3_2						,  lowerCAmelCase=1_0_0_0						,  lowerCAmelCase=1_6						,  lowerCAmelCase=1.0						,  lowerCAmelCase=recopy_gpta						,  lowerCAmelCase=None						,  lowerCAmelCase=1_0						,  lowerCAmelCase="gpt2_finetuned.pt"						,  )		->				Tuple:

		"""simple docstring"""






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

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

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

		_UpperCamelCase						    =	[]
		_UpperCamelCase						    =	[]

		# Compute the performance of the transformer model at the beginning
		_UpperCamelCase						    =	compute_perplexity(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
		test_perps.append(lowerCAmelCase )
		print("""Test perplexity, step"""						,  lowerCAmelCase						,  """:"""						,  lowerCAmelCase )
		for epoch in range(int(lowerCAmelCase ) ):
				for step, example in enumerate(lowerCAmelCase ):
						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(lowerCAmelCase						,  labels=lowerCAmelCase )
						_UpperCamelCase						    =	True

						if secondary_learner is not None:
								_UpperCamelCase						    =	secondary_learner.forward(
								    torch.tensor(lowerCAmelCase						,  dtype=torch.long						,  device=lowerCAmelCase ).unsqueeze(0 ) )[0].item()
								observed_qs.append(float(lowerCAmelCase ) )

								# 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 == 1_0:
										_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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )
										test_perps.append(lowerCAmelCase )

										print("""Test perplexity, step"""						,  lowerCAmelCase						,  """:"""						,  lowerCAmelCase )
            # Break out of the loop after 60 batches
						if max_steps > 0 and global_step > 6_0:
								break
				if max_steps > 0 and global_step > 6_0:
						break

    # save finetuned transformer model
		torch.save(model.state_dict()						,  lowerCAmelCase )
		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()		->				Any:

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

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

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

		parser.add_argument(
		    """--context_len"""						,  default=3_2						,  type=lowerCAmelCase						,  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_0_0						,  type=lowerCAmelCase						,  help="""number of articles that are long enough to be used as our objective set"""						,  )
		parser.add_argument(
		    """--eval_freq"""						,  default=1_0_0						,  type=lowerCAmelCase						,  help="""secondary model evaluation is triggered at eval_freq""" )

		parser.add_argument("""--max_steps"""						,  default=1_0_0_0						,  type=lowerCAmelCase						,  help="""To calculate training epochs""" )

		parser.add_argument(
		    """--secondary_learner_batch_size"""						,  default=1_2_8						,  type=lowerCAmelCase						,  help="""batch size of training data for secondary learner"""						,  )

		parser.add_argument(
		    """--batch_size"""						,  default=1_6						,  type=lowerCAmelCase						,  help="""batch size of training data of language model(gpt2) """ )

		parser.add_argument(
		    """--eval_interval"""						,  default=1_0						,  type=lowerCAmelCase						,  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_0_0						,  type=lowerCAmelCase						,  help="""The number of examples split to be used as objective_set/test_data""" )

		parser.add_argument(
		    """--min_len"""						,  default=1_0_2_6						,  type=lowerCAmelCase						,  help="""The minimum length of the article to be used as objective set""" )

		parser.add_argument(
		    """--secondary_learner_max_epochs"""						,  default=1_5						,  type=lowerCAmelCase						,  help="""number of epochs to train secondary learner""" )

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

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

		parser.add_argument(
		    """--recopy_model"""						,  default=lowerCAmelCase						,  type=lowerCAmelCase						,  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=3_2						,  max_steps=1_0						,  size_objective_set=1_0_0						,  min_len=1_0_2_6						,  trim=lowerCAmelCase						,  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(
		    lowerCAmelCase						,  secondary_learner_max_epochs=1_5						,  secondary_learner_batch_size=1_2_8						,  eval_freq=1_0_0						,  igf_model_path="""igf_model.pt"""						,  )

		# load pretrained gpt2 model
		_UpperCamelCase						    =	GPTaLMHeadModel.from_pretrained("""gpt2""" )
		set_seed(4_2 )

		# Generate train and test data to train and evaluate gpt2 model
		_UpperCamelCase    ,	_UpperCamelCase						    =	generate_datasets(
		    context_len=3_2						,  file="""data/tokenized_stories_train_wikitext103.jbl"""						,  number=1_0_0						,  min_len=1_0_2_6						,  trim=lowerCAmelCase )

		# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
		finetune(
		    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  context_len=3_2						,  max_steps=1_0_0_0						,  batch_size=1_6						,  threshold=1.0						,  recopy_model=lowerCAmelCase						,  secondary_learner=lowerCAmelCase						,  eval_interval=1_0						,  finetuned_model_name="""gpt2_finetuned.pt"""						,  )


if __name__ == "__main__":
		main()





from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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







class        lowerCAmelCase__      (   __lowercase	):

						def __init__(			self       ,							a       ,							a				)					->      Any:


								'''simple docstring'''

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







						@torch.no_grad()
						def __call__(			self       ,							a = 1       ,							a = 1_00       ,							a = None       ,							a = None       ,							a = True       ,							)					->      Union[AudioPipelineOutput, Tuple]:


								'''simple docstring'''

								if audio_length_in_s is None:
										_UpperCamelCase						    =	self.unet.config.sample_size / self.unet.config.sample_rate

								_UpperCamelCase						    =	audio_length_in_s * self.unet.config.sample_rate

								_UpperCamelCase						    =	2 ** len(self.unet.up_blocks				)
								if sample_size < 3 * down_scale_factor:
										raise ValueError(
										    F'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
										    F' {3 * down_scale_factor / self.unet.config.sample_rate}.'				)

								_UpperCamelCase						    =	int(a				)
								if sample_size % down_scale_factor != 0:
										_UpperCamelCase						    =	(
										    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
										) * down_scale_factor
										logger.info(
										    F'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
										    F' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
										    """ process."""				)
								_UpperCamelCase						    =	int(a				)

								_UpperCamelCase						    =	next(iter(self.unet.parameters()				)				).dtype
								_UpperCamelCase						    =	(batch_size, self.unet.config.in_channels, sample_size)
								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.'				)

								_UpperCamelCase						    =	randn_tensor(a       ,							generator=a       ,							device=self.device       ,							dtype=a				)

								# set step values
								self.scheduler.set_timesteps(a       ,							device=audio.device				)
								_UpperCamelCase						    =	self.scheduler.timesteps.to(a				)

								for t in self.progress_bar(self.scheduler.timesteps				):
										# 1. predict noise model_output
										_UpperCamelCase						    =	self.unet(a       ,							a				).sample

										# 2. compute previous image: x_t -> t_t-1
										_UpperCamelCase						    =	self.scheduler.step(a       ,							a       ,							a				).prev_sample

								_UpperCamelCase						    =	audio.clamp(-1       ,							1				).float().cpu().numpy()

								_UpperCamelCase						    =	audio[:, :, :original_sample_size]

								if not return_dict:
										return (audio,)

								return AudioPipelineOutput(audios=a				)



