






'''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__      (					__lowerCAmelCase		:      Tuple=32 ,     __lowerCAmelCase		:      int=10 ,     __lowerCAmelCase		:      Optional[Any]=100 ,     __lowerCAmelCase		:      Union[str, Any]=1026 ,     __lowerCAmelCase		:      List[str]=True ,     __lowerCAmelCase		:      List[Any]="data/tokenized_stories_train_wikitext103.jbl" ,     __lowerCAmelCase		:      Optional[int]="igf_context_pairs.jbl" ,     ):
						set_seed(3    )
						# generate train_data and objective_set
						lowerCamelCase__							,      lowerCamelCase__								=	generate_datasets(
						    __lowerCAmelCase ,     __lowerCAmelCase ,     number=__lowerCAmelCase ,     min_len=1026 ,     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?
						lowerCamelCase__								=	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""    )

						# load pretrained model
						lowerCamelCase__								=	load_gpta("""gpt2"""    ).to(__lowerCAmelCase    )
						print("""computing perplexity on objective set"""    )
						lowerCamelCase__								=	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		:      Dict ,     __lowerCAmelCase		:      Any=15 ,     __lowerCAmelCase		:      List[str]=128 ,     __lowerCAmelCase		:      Any=100 ,     __lowerCAmelCase		:      Optional[int]="igf_model.pt" ,     ):
						set_seed(42    )

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

						# Initialize secondary learner to use embedding weights of model
						lowerCamelCase__								=	SecondaryLearner(__lowerCAmelCase    )

						# Train secondary learner
						lowerCamelCase__								=	train_secondary_learner(
						    __lowerCAmelCase ,     __lowerCAmelCase ,     max_epochs=__lowerCAmelCase ,     batch_size=__lowerCAmelCase ,     eval_freq=100 ,     igf_model_path=__lowerCAmelCase ,     )

						del model, secondary_learner_train_data
						torch.cuda.empty_cache()

						return secondary_learner
def     A__      (					__lowerCAmelCase		:      List[Any] ,     __lowerCAmelCase		:      Optional[Any] ,     __lowerCAmelCase		:      List[str] ,     __lowerCAmelCase		:      str=32 ,     __lowerCAmelCase		:      Tuple=1000 ,     __lowerCAmelCase		:      str=16 ,     __lowerCAmelCase		:      Optional[int]=1.0 ,     __lowerCAmelCase		:      List[str]=recopy_gpta ,     __lowerCAmelCase		:      Optional[int]=None ,     __lowerCAmelCase		:      str=10 ,     __lowerCAmelCase		:      int="gpt2_finetuned.pt" ,     ):
						lowerCamelCase__								=	torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu"""    )
						lowerCamelCase__								=	RandomSampler(__lowerCAmelCase    )
						lowerCamelCase__								=	DataLoader(__lowerCAmelCase ,     sampler=__lowerCAmelCase    )

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

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

						lowerCamelCase__								=	[]
						lowerCamelCase__								=	[]

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

																		if secondary_learner is not None:
																								lowerCamelCase__								=	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 == 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(__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 > 60:
																								break
												if max_steps > 0 and global_step > 60:
																		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__      (					):
						lowerCamelCase__								=	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=32 ,     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=100 ,     type=__lowerCAmelCase ,     help="""number of articles that are long enough to be used as our objective set""" ,     )
						parser.add_argument(
						    """--eval_freq""" ,     default=100 ,     type=__lowerCAmelCase ,     help="""secondary model evaluation is triggered at eval_freq"""    )

						parser.add_argument("""--max_steps""" ,     default=1000 ,     type=__lowerCAmelCase ,     help="""To calculate training epochs"""    )

						parser.add_argument(
						    """--secondary_learner_batch_size""" ,     default=128 ,     type=__lowerCAmelCase ,     help="""batch size of training data for secondary learner""" ,     )

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

						parser.add_argument(
						    """--eval_interval""" ,     default=10 ,     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=100 ,     type=__lowerCAmelCase ,     help="""The number of examples split to be used as objective_set/test_data"""    )

						parser.add_argument(
						    """--min_len""" ,     default=1026 ,     type=__lowerCAmelCase ,     help="""The minimum length of the article to be used as objective set"""    )

						parser.add_argument(
						    """--secondary_learner_max_epochs""" ,     default=15 ,     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=32 ,     max_steps=10 ,     size_objective_set=100 ,     min_len=1026 ,     trim=__lowerCAmelCase ,     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(
						    __lowerCAmelCase ,     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=1026 ,     trim=__lowerCAmelCase    )

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    __lowerCAmelCase ,     __lowerCAmelCase ,     __lowerCAmelCase ,     context_len=32 ,     max_steps=1000 ,     batch_size=16 ,     threshold=1.0 ,     recopy_model=__lowerCAmelCase ,     secondary_learner=__lowerCAmelCase ,     eval_interval=10 ,     finetuned_model_name="""gpt2_finetuned.pt""" ,     )


if __name__ == "__main__":
							main()





'''simple docstring'''



from dataclasses import asdict, dataclass
from typing import Optional

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


A__						:   List[str]				=    logging.get_logger(__name__)

# TODO Update this
A__						:   Tuple				=    {
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'esm'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10_26     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_     ,	mask_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						vocab_size
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      str														=						num_hidden_layers
         __lowerCamelCase  :      List[str]														=						num_attention_heads
         __lowerCamelCase  :      Any														=						intermediate_size
         __lowerCamelCase  :      Optional[Any]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      Optional[int]														=						max_position_embeddings
         __lowerCamelCase  :      str														=						initializer_range
         __lowerCamelCase  :      Optional[int]														=						layer_norm_eps
         __lowerCamelCase  :      List[str]														=						position_embedding_type
         __lowerCamelCase  :      int														=						use_cache
         __lowerCamelCase  :      Optional[Any]														=						emb_layer_norm_before
         __lowerCamelCase  :      Optional[Any]														=						token_dropout
         __lowerCamelCase  :      str														=						is_folding_model
         if is_folding_model:
              if esmfold_config is None:
                   logger.info('No esmfold_config supplied for folding model, using default values.'      )
                   __lowerCamelCase  :      Dict														=						EsmFoldConfig()
              elif isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ):
                   __lowerCamelCase  :      Optional[int]														=						EsmFoldConfig(**SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      List[Any]														=						esmfold_config
              if vocab_list is None:
                   logger.warning('No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'      )
                   __lowerCamelCase  :      List[str]														=						get_default_vocab_list()
              else:
                   __lowerCamelCase  :      Optional[Any]														=						vocab_list
         else:
              __lowerCamelCase  :      Dict														=						None
              __lowerCamelCase  :      Optional[Any]														=						None
         if self.esmfold_config is not None and getattr(self.esmfold_config     ,	'use_esm_attn_map'     ,	SCREAMING_SNAKE_CASE_      ):
              raise ValueError('The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						super().to_dict()
         if isinstance(self.esmfold_config     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      int														=						self.esmfold_config.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     None
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   float					=     0

    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False

    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   "TrunkConfig"					=     None







    def  lowercase_							(				self      )							->	Any:
         if self.trunk is None:
              __lowerCamelCase  :      List[str]														=						TrunkConfig()
         elif isinstance(self.trunk     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Any														=						TrunkConfig(**self.trunk      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						asdict(self      )
         __lowerCamelCase  :      str														=						self.trunk.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     4_8
    lowerCamelCase    :   int					=     1_0_2_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   Optional[int]					=     1_2_8
    lowerCamelCase    :   "StructureModuleConfig"					=     None







    def  lowercase_							(				self      )							->	Optional[int]:
         if self.structure_module is None:
              __lowerCamelCase  :      Dict														=						StructureModuleConfig()
         elif isinstance(self.structure_module     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[Any]														=						StructureModuleConfig(**self.structure_module      )

         if self.max_recycles <= 0:
              raise ValueError(f'`max_recycles` should be positive, got {self.max_recycles}.'      )
         if self.sequence_state_dim % self.sequence_state_dim != 0:
              raise ValueError(
                  '`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'
                  f' {self.sequence_state_dim} and {self.sequence_state_dim}.'      )
         if self.pairwise_state_dim % self.pairwise_state_dim != 0:
              raise ValueError(
                  '`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'
                  f' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'      )

         __lowerCamelCase  :      Tuple														=						self.sequence_state_dim // self.sequence_head_width
         __lowerCamelCase  :      str														=						self.pairwise_state_dim // self.pairwise_head_width

         if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
              raise ValueError(
                  '`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'
                  f' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'      )
         if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
              raise ValueError(
                  '`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'
                  f' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'      )
         if self.pairwise_state_dim % 2 != 0:
              raise ValueError(f'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'      )

         if self.dropout >= 0.4:
              raise ValueError(f'`dropout` should not be greater than 0.4, got {self.dropout}.'      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						asdict(self      )
         __lowerCamelCase  :      int														=						self.structure_module.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     3_8_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_6
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_2
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   float					=     0.1
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     2
    lowerCamelCase    :   int					=     7
    lowerCamelCase    :   int					=     1_0
    lowerCamelCase    :   float					=     1e-8
    lowerCamelCase    :   float					=     1e5







    def  lowercase_							(				self      )							->	Any:
         return asdict(self      )







def      UpperCAmelCase__   (      )	->						Optional[Any]:
     return (
         "<cls>",
         "<pad>",
         "<eos>",
         "<unk>",
         "L",
         "A",
         "G",
         "V",
         "S",
         "E",
         "R",
         "T",
         "I",
         "D",
         "P",
         "K",
         "Q",
         "N",
         "F",
         "Y",
         "M",
         "H",
         "W",
         "C",
         "X",
         "B",
         "U",
         "Z",
         "O",
         ".",
         "-",
         "<null_1>",
         "<mask>",
     )






