




"""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 							lowercase					(snake_case__   : Any=32      ,       snake_case__   : int=10      ,       snake_case__   : Optional[int]=100      ,       snake_case__   : Optional[int]=1_026      ,       snake_case__   : Union[str, Any]=True      ,       snake_case__   : Any="data/tokenized_stories_train_wikitext103.jbl"      ,       snake_case__   : Union[str, Any]="igf_context_pairs.jbl"      ,       )     ->  Union[str, Any]:


				'''simple docstring'''






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

				# load pretrained model
				lowerCAmelCase  =				load_gpta("""gpt2""" ).to(snake_case__ )
				print("""computing perplexity on objective set""" )
				lowerCAmelCase  =				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 							lowercase					(snake_case__   : List[Any]      ,       snake_case__   : List[str]=15      ,       snake_case__   : str=128      ,       snake_case__   : int=100      ,       snake_case__   : Optional[int]="igf_model.pt"      ,       )     ->  Dict:


				'''simple docstring'''






				set_seed(42 )

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

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

				# Train secondary learner
				lowerCAmelCase  =				train_secondary_learner(
				    snake_case__      ,       snake_case__      ,       max_epochs=snake_case__      ,       batch_size=snake_case__      ,       eval_freq=100      ,       igf_model_path=snake_case__      ,       )

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner





def 							lowercase					(snake_case__   : int      ,       snake_case__   : List[str]      ,       snake_case__   : Any      ,       snake_case__   : List[str]=32      ,       snake_case__   : str=1_000      ,       snake_case__   : str=16      ,       snake_case__   : Dict=1.0      ,       snake_case__   : List[str]=recopy_gpta      ,       snake_case__   : Optional[int]=None      ,       snake_case__   : int=10      ,       snake_case__   : Dict="gpt2_finetuned.pt"      ,       )     ->  Union[str, Any]:


				'''simple docstring'''






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

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

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

				lowerCAmelCase  =				[]
				lowerCAmelCase  =				[]

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

												if secondary_learner is not None:
																lowerCAmelCase  =				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:
																				lowerCAmelCase  =				-1
																if predicted_q < threshold:
																				lowerCAmelCase  =				False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu() ) )
																lowerCAmelCase  =				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()
																lowerCAmelCase  =				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:
																				lowerCAmelCase  =				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 							lowercase					()     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				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=100      ,       type=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=snake_case__      ,       help="""secondary model evaluation is triggered at eval_freq""" )

				parser.add_argument("""--max_steps"""      ,       default=1_000      ,       type=snake_case__      ,       help="""To calculate training epochs""" )

				parser.add_argument(
				    """--secondary_learner_batch_size"""      ,       default=128      ,       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=100      ,       type=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=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=100      ,       min_len=1_026      ,       trim=snake_case__      ,       data_file="""data/tokenized_stories_train_wikitext103.jbl"""      ,       igf_data_file="""igf_context_pairs.jbl"""      ,       )

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

				# Train secondary learner
				lowerCAmelCase  =				training_secondary_learner(
				    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
				lowerCAmelCase  =				GPTaLMHeadModel.from_pretrained("""gpt2""" )
				set_seed(42 )

				# Generate train and test data to train and evaluate gpt2 model
				lowerCAmelCase ,						lowerCAmelCase  =				generate_datasets(
				    context_len=32      ,       file="""data/tokenized_stories_train_wikitext103.jbl"""      ,       number=100      ,       min_len=1_026      ,       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=1_000      ,       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"""







from typing import List, Optional

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


a      =							logging.get_logger(__name__)

a      =							{
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						'autoformer'
						_a    						=						{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}


						def __init__(							self				:			Dict						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			Optional[int] = None						,						lowerCAmelCase				:			str = "student_t"						,						lowerCAmelCase				:			str = "nll"						,						lowerCAmelCase				:			int = 1						,						lowerCAmelCase				:			List[int] = [1, 2, 3, 4, 5, 6, 7]						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			int = 0						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			Optional[List[int]] = None						,						lowerCAmelCase				:			int = 64						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 2						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			int = 32						,						lowerCAmelCase				:			str = "gelu"						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			float = 0.1						,						lowerCAmelCase				:			int = 100						,						lowerCAmelCase				:			float = 0.02						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			Tuple=True						,						lowerCAmelCase				:			int = 10						,						lowerCAmelCase				:			int = 25						,						lowerCAmelCase				:			int = 3						,						**lowerCAmelCase				:			Tuple						,						):
										# time series specific configuration
										lowerCAmelCase  =				prediction_length
										lowerCAmelCase  =				context_length if context_length is not None else prediction_length
										lowerCAmelCase  =				distribution_output
										lowerCAmelCase  =				loss
										lowerCAmelCase  =				input_size
										lowerCAmelCase  =				num_time_features
										lowerCAmelCase  =				lags_sequence
										lowerCAmelCase  =				scaling
										lowerCAmelCase  =				num_dynamic_real_features
										lowerCAmelCase  =				num_static_real_features
										lowerCAmelCase  =				num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(lowerCAmelCase					) != num_static_categorical_features:
																		raise ValueError(
																		    """The cardinality should be a list of the same length as `num_static_categorical_features`"""					)
														lowerCAmelCase  =				cardinality
										else:
														lowerCAmelCase  =				[0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(lowerCAmelCase					) != num_static_categorical_features:
																		raise ValueError(
																		    """The embedding dimension should be a list of the same length as `num_static_categorical_features`"""					)
														lowerCAmelCase  =				embedding_dimension
										else:
														lowerCAmelCase  =				[min(50						,						(cat + 1) // 2					) for cat in self.cardinality]
										lowerCAmelCase  =				num_parallel_samples

										# Transformer architecture configuration
										lowerCAmelCase  =				input_size * len(self.lags_sequence					) + self._number_of_features
										lowerCAmelCase  =				d_model
										lowerCAmelCase  =				encoder_attention_heads
										lowerCAmelCase  =				decoder_attention_heads
										lowerCAmelCase  =				encoder_ffn_dim
										lowerCAmelCase  =				decoder_ffn_dim
										lowerCAmelCase  =				encoder_layers
										lowerCAmelCase  =				decoder_layers

										lowerCAmelCase  =				dropout
										lowerCAmelCase  =				attention_dropout
										lowerCAmelCase  =				activation_dropout
										lowerCAmelCase  =				encoder_layerdrop
										lowerCAmelCase  =				decoder_layerdrop

										lowerCAmelCase  =				activation_function
										lowerCAmelCase  =				init_std

										lowerCAmelCase  =				use_cache

										# Autoformer
										lowerCAmelCase  =				label_length
										lowerCAmelCase  =				moving_average
										lowerCAmelCase  =				autocorrelation_factor

										super().__init__(is_encoder_decoder=lowerCAmelCase						,						**lowerCAmelCase					)




						@property
						def   __lowercase (							self				:			Tuple					):
										return (
										    sum(self.embedding_dimension					)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)





