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						(__lowercase=32 ,      __lowercase=10 ,      __lowercase=100 ,      __lowercase=1_026 ,      __lowercase=True ,      __lowercase="data/tokenized_stories_train_wikitext103.jbl" ,      __lowercase="igf_context_pairs.jbl" ,      )	->	List[str]:

						'''simple docstring'''



						set_seed(3						)
						# generate train_data and objective_set
						_snake_case    ,_snake_case						:					Optional[int]    =   generate_datasets(
						    _lowerCAmelCase ,      _lowerCAmelCase ,      number=_lowerCAmelCase ,      min_len=1_026 ,      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?
						_snake_case						:					Optional[Any]    =   torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)

						# load pretrained model
						_snake_case						:					Any    =   load_gpta("gpt2"						).to(_lowerCAmelCase						)
						print("computing perplexity on objective set"						)
						_snake_case						:					Tuple    =   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  snake_case						(__lowercase ,      __lowercase=15 ,      __lowercase=128 ,      __lowercase=100 ,      __lowercase="igf_model.pt" ,      )	->	List[str]:

						'''simple docstring'''



						set_seed(42						)

						# Load pre-trained model
						_snake_case						:					int    =   GPTaLMHeadModel.from_pretrained("gpt2"						)

						# Initialize secondary learner to use embedding weights of model
						_snake_case						:					List[str]    =   SecondaryLearner(_lowerCAmelCase						)

						# Train secondary learner
						_snake_case						:					str    =   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  snake_case						(__lowercase ,      __lowercase ,      __lowercase ,      __lowercase=32 ,      __lowercase=1_000 ,      __lowercase=16 ,      __lowercase=1.0 ,      __lowercase=recopy_gpta ,      __lowercase=None ,      __lowercase=10 ,      __lowercase="gpt2_finetuned.pt" ,      )	->	Optional[Any]:

						'''simple docstring'''



						_snake_case						:					Union[str, Any]    =   torch.device("cuda:0" if torch.cuda.is_available() else "cpu"						)
						_snake_case						:					Optional[int]    =   RandomSampler(_lowerCAmelCase						)
						_snake_case						:					Optional[int]    =   DataLoader(_lowerCAmelCase ,      sampler=_lowerCAmelCase						)

						_snake_case						:					int    =   max_steps // (len(_lowerCAmelCase						)) + 1
						_snake_case						:					Dict    =   0
						_snake_case						:					Dict    =   torch.zeros((1, context_len) ,      dtype=torch.long ,      device=_lowerCAmelCase						)
						_snake_case    ,_snake_case    ,_snake_case						:					Tuple    =   recopy_model(_lowerCAmelCase ,      _lowerCAmelCase ,      _lowerCAmelCase						)

						model.train()
						if secondary_learner is not None:
												secondary_learner.to(_lowerCAmelCase						)
												secondary_learner.eval()
						_snake_case						:					Optional[Any]    =   []
						_snake_case						:					str    =   0

						_snake_case						:					Dict    =   []
						_snake_case						:					Tuple    =   []

						# Compute the performance of the transformer model at the beginning
						_snake_case						:					List[str]    =   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()
																		_snake_case						:					int    =   random.randint(0 ,      example.size(2						) - context_len - 1						)
																		_snake_case						:					Tuple    =   example[0, 0, start : start + context_len]
																		lm_optimizer.zero_grad()
																		_snake_case						:					Dict    =   model(_lowerCAmelCase ,      labels=_lowerCAmelCase						)
																		_snake_case						:					str    =   True

																		if secondary_learner is not None:
																								_snake_case						:					List[str]    =   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:
																														_snake_case						:					List[str]    =   -1
																								if predicted_q < threshold:
																														_snake_case						:					Any    =   False

            # If we passed the filter, add the context to the batch!
																		if do_backprop:
																								contexts.append(np.array(context.cpu()						)						)
																								_snake_case						:					Dict    =   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						:					Optional[int]    =   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						:					Union[str, Any]    =   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  snake_case						()	->	int:

						'''simple docstring'''



						_snake_case						:					Optional[Any]    =   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=1_000 ,      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=1_026 ,      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=1_026 ,      trim=_lowerCAmelCase ,      data_file="data/tokenized_stories_train_wikitext103.jbl" ,      igf_data_file="igf_context_pairs.jbl" ,      )

						# Load train data for secondary learner
						_snake_case						:					List[str]    =   joblib.load("data/IGF_values.jbl"						)

						# Train secondary learner
						_snake_case						:					Optional[int]    =   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
						_snake_case						:					List[str]    =   GPTaLMHeadModel.from_pretrained("gpt2"						)
						set_seed(42						)

						# Generate train and test data to train and evaluate gpt2 model
						_snake_case    ,_snake_case						:					List[Any]    =   generate_datasets(
						    context_len=32 ,      file="data/tokenized_stories_train_wikitext103.jbl" ,      number=100 ,      min_len=1_026 ,      trim=_lowerCAmelCase						)

						# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
						finetune(
						    _lowerCAmelCase ,      _lowerCAmelCase ,      _lowerCAmelCase ,      context_len=32 ,      max_steps=1_000 ,      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()






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__SCREAMING_SNAKE_CASE						:   Any         =			{'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE						:   Optional[Any]         =			[
				    'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MraForMaskedLM',
				    'MraForMultipleChoice',
				    'MraForQuestionAnswering',
				    'MraForSequenceClassification',
				    'MraForTokenClassification',
				    'MraLayer',
				    'MraModel',
				    'MraPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_mra import (
								    MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MraForMaskedLM,
								    MraForMultipleChoice,
								    MraForQuestionAnswering,
								    MraForSequenceClassification,
								    MraForTokenClassification,
								    MraLayer,
								    MraModel,
								    MraPreTrainedModel,
								)
else:
				import sys

				__SCREAMING_SNAKE_CASE						:   str         =			_LazyModule(__name__, globals()['__file__'], _import_structure)