
"""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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE=32							,    SCREAMING_SNAKE_CASE=10							,    SCREAMING_SNAKE_CASE=1_00							,    SCREAMING_SNAKE_CASE=10_26							,    SCREAMING_SNAKE_CASE=True							,    SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"							,    SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"							,    )  ->     Optional[Any]:







							"""simple docstring"""


							set_seed(3 )
							# generate train_data and objective_set
							__snake_case			,   __snake_case     			=       generate_datasets(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    number=SCREAMING_SNAKE_CASE							,    min_len=10_26							,    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?
							__snake_case     			=       torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

							# load pretrained model
							__snake_case     			=       load_gpta("gpt2" ).to(SCREAMING_SNAKE_CASE )
							print("computing perplexity on objective set" )
							__snake_case     			=       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   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=15							,    SCREAMING_SNAKE_CASE=1_28							,    SCREAMING_SNAKE_CASE=1_00							,    SCREAMING_SNAKE_CASE="igf_model.pt"							,    )  ->     int:







							"""simple docstring"""


							set_seed(42 )

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

							# Initialize secondary learner to use embedding weights of model
							__snake_case     			=       SecondaryLearner(SCREAMING_SNAKE_CASE )

							# Train secondary learner
							__snake_case     			=       train_secondary_learner(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    max_epochs=SCREAMING_SNAKE_CASE							,    batch_size=SCREAMING_SNAKE_CASE							,    eval_freq=1_00							,    igf_model_path=SCREAMING_SNAKE_CASE							,    )

							del model, secondary_learner_train_data
							torch.cuda.empty_cache()

							return secondary_learner
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=32							,    SCREAMING_SNAKE_CASE=10_00							,    SCREAMING_SNAKE_CASE=16							,    SCREAMING_SNAKE_CASE=1.0							,    SCREAMING_SNAKE_CASE=recopy_gpta							,    SCREAMING_SNAKE_CASE=None							,    SCREAMING_SNAKE_CASE=10							,    SCREAMING_SNAKE_CASE="gpt2_finetuned.pt"							,    )  ->     Union[str, Any]:







							"""simple docstring"""


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

							__snake_case     			=       max_steps // (len(SCREAMING_SNAKE_CASE )) + 1
							__snake_case     			=       0
							__snake_case     			=       torch.zeros((1, context_len)							,    dtype=torch.long							,    device=SCREAMING_SNAKE_CASE )
							__snake_case			,   __snake_case			,   __snake_case     			=       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()
							__snake_case     			=       []
							__snake_case     			=       0

							__snake_case     			=       []
							__snake_case     			=       []

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

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

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu() ) )
																												__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()
																												__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:
																																			__snake_case     			=       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   __UpperCamelCase   (					)  ->     Any:







							"""simple docstring"""


							__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=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=1_00							,    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=1_00							,    type=SCREAMING_SNAKE_CASE							,    help="secondary model evaluation is triggered at eval_freq" )

							parser.add_argument("--max_steps"							,    default=10_00							,    type=SCREAMING_SNAKE_CASE							,    help="To calculate training epochs" )

							parser.add_argument(
							    "--secondary_learner_batch_size"							,    default=1_28							,    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=1_00							,    type=SCREAMING_SNAKE_CASE							,    help="The number of examples split to be used as objective_set/test_data" )

							parser.add_argument(
							    "--min_len"							,    default=10_26							,    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=1_00							,    min_len=10_26							,    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
							__snake_case     			=       joblib.load("data/IGF_values.jbl" )

							# Train secondary learner
							__snake_case     			=       training_secondary_learner(
							    SCREAMING_SNAKE_CASE							,    secondary_learner_max_epochs=15							,    secondary_learner_batch_size=1_28							,    eval_freq=1_00							,    igf_model_path="igf_model.pt"							,    )

							# load pretrained gpt2 model
							__snake_case     			=       GPTaLMHeadModel.from_pretrained("gpt2" )
							set_seed(42 )

							# Generate train and test data to train and evaluate gpt2 model
							__snake_case			,   __snake_case     			=       generate_datasets(
							    context_len=32							,    file="data/tokenized_stories_train_wikitext103.jbl"							,    number=1_00							,    min_len=10_26							,    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=10_00							,    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"""







import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     List[Any]:







							"""simple docstring"""


							__snake_case     			=       TapasConfig.from_json_file(SCREAMING_SNAKE_CASE )
							# set absolute/relative position embeddings parameter
							__snake_case     			=       reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WTQ":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       True
														# hparam_utils.py hparams
														__snake_case     			=       0.664_694
														__snake_case     			=       0.207_951
														__snake_case     			=       0.121_194
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       False
														__snake_case     			=       0.0_352_513

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__snake_case     			=       4
														__snake_case     			=       False
														# hparam_utils.py hparams
														__snake_case     			=       36.4_519
														__snake_case     			=       0.903_421
														__snake_case     			=       222.088
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       True
														__snake_case     			=       0.763_141

														__snake_case     			=       TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
							elif task == "TABFACT":
														__snake_case     			=       TapasForSequenceClassification(config=SCREAMING_SNAKE_CASE )
							elif task == "MLM":
														__snake_case     			=       TapasForMaskedLM(config=SCREAMING_SNAKE_CASE )
							elif task == "INTERMEDIATE_PRETRAINING":
														__snake_case     			=       TapasModel(config=SCREAMING_SNAKE_CASE )
							else:
														raise ValueError(F'''Task {task} not supported.''' )

							print(F'''Building PyTorch model from configuration: {config}''' )
							# Load weights from tf checkpoint
							load_tf_weights_in_tapas(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# Save pytorch-model (weights and configuration)
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							model.save_pretrained(SCREAMING_SNAKE_CASE )

							# Save tokenizer files
							print(F'''Save tokenizer files to {pytorch_dump_path}''' )
							__snake_case     			=       TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"							,    model_max_length=5_12 )
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE )

							print("Used relative position embeddings:"							,    model.config.reset_position_index_per_cell )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
		)
		parser.add_argument(
		    """--reset_position_index_per_cell""",
		    default=False,
		    action="""store_true""",
		    help="""Whether to use relative position embeddings or not. Defaults to True.""",
		)
		parser.add_argument(
		    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
		)
		parser.add_argument(
		    """--tapas_config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        """The config json file corresponding to the pre-trained TAPAS model. \n"""
		        """This specifies the model architecture."""
		    ),
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.task,
		    args.reset_position_index_per_cell,
		    args.tf_checkpoint_path,
		    args.tapas_config_file,
		    args.pytorch_dump_path,
		)
