
'''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				:List[str]=32			,     __lowerCamelCase				:Optional[int]=10			,     __lowerCamelCase				:Union[str, Any]=100			,     __lowerCamelCase				:List[Any]=1026			,     __lowerCamelCase				:Any=True			,     __lowerCamelCase				:Optional[int]="data/tokenized_stories_train_wikitext103.jbl"			,     __lowerCamelCase				:Any="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				:str			,     __lowerCamelCase				:List[str]=15			,     __lowerCamelCase				:Tuple=128			,     __lowerCamelCase				:Any=100			,     __lowerCamelCase				:List[Any]="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				:Dict			,     __lowerCamelCase				:List[Any]			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:List[Any]=32			,     __lowerCamelCase				:Optional[int]=1000			,     __lowerCamelCase				:Dict=16			,     __lowerCamelCase				:Dict=1.0			,     __lowerCamelCase				:List[Any]=recopy_gpta			,     __lowerCamelCase				:List[Any]=None			,     __lowerCamelCase				:Dict=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'''



import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase    =		"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowercase    =		direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowercase    =		transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowercase    =		re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_lowercase    =		{
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}


def A				(__lowerCamelCase				:str		):
						_lowerCAmelCase  						=			None

						# source code of `config_class`
						_lowerCAmelCase  						=			inspect.getsource(__lowerCamelCase		)
						_lowerCAmelCase  						=			_re_checkpoint.findall(__lowerCamelCase		)

						# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
						# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
						for ckpt_name, ckpt_link in checkpoints:
												# allow the link to end with `/`
												if ckpt_link.endswith("""/"""		):
																		_lowerCAmelCase  						=			ckpt_link[:-1]

												# verify the checkpoint name corresponds to the checkpoint link
												_lowerCAmelCase  						=			f'https://huggingface.co/{ckpt_name}'
												if ckpt_link == ckpt_link_from_name:
																		_lowerCAmelCase  						=			ckpt_name
																		break

						return checkpoint


def A				():
						_lowerCAmelCase  						=			[]

						for config_class in list(CONFIG_MAPPING.values()		):
												# Skip deprecated models
												if "models.deprecated" in config_class.__module__:
																		continue
												_lowerCAmelCase  						=			get_checkpoint_from_config_class(__lowerCamelCase		)

												_lowerCAmelCase  						=			config_class.__name__
												if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
																		configs_without_checkpoint.append(__lowerCamelCase		)

						if len(__lowerCamelCase		) > 0:
												_lowerCAmelCase  						=			"""\n""".join(sorted(__lowerCamelCase		)		)
												raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()
