
import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowercase						:      str									= logging.getLogger()




def 			_SCREAMING_SNAKE_CASE      (	)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      argparse.ArgumentParser()
							parser.add_argument("-f")
							__UpperCamelCase			:     Tuple         =      parser.parse_args()
							return args.f




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def      _lowerCamelCase       (      self	:int		)	->				None:
								__UpperCamelCase			:     List[str]         =      logging.StreamHandler(sys.stdout		)
								logger.addHandler(a		)







	def      _lowerCamelCase       (      self	:Optional[Any]  ,     a	:str		)	->				str:
								__UpperCamelCase			:     Any         =      get_gpu_count()

								if n_gpu > 1:
															pass
															# XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
															# script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
															# distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
															# cmd = [sys.executable] + distributed_args + args
															# execute_subprocess_async(cmd, env=self.get_env())
															# XXX: test the results - need to save them first into .json file
								else:
															args.insert(0  ,     "run_glue_deebert.py"		)
															with patch.object(a  ,     "argv"  ,     a		):
																						__UpperCamelCase			:     List[str]         =      run_glue_deebert.main()
																						for value in result.values():
																													self.assertGreaterEqual(a  ,     0.666		)







	@slow
	@require_torch_non_multi_gpu
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     int         =      "\n            --model_type roberta\n            --model_name_or_path roberta-base\n            --task_name MRPC\n            --do_train\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --max_seq_length 128\n            --per_gpu_eval_batch_size=1\n            --per_gpu_train_batch_size=8\n            --learning_rate 2e-4\n            --num_train_epochs 3\n            --overwrite_output_dir\n            --seed 42\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --save_steps 0\n            --overwrite_cache\n            --eval_after_first_stage\n            ".split()
								self.run_and_check(a		)

								__UpperCamelCase			:     Any         =      "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --eval_each_highway\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
								self.run_and_check(a		)

								__UpperCamelCase			:     int         =      "\n            --model_type roberta\n            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --task_name MRPC\n            --do_eval\n            --do_lower_case\n            --data_dir ./tests/fixtures/tests_samples/MRPC/\n            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage\n            --plot_data_dir ./examples/deebert/results/\n            --max_seq_length 128\n            --early_exit_entropy 0.1\n            --eval_highway\n            --overwrite_cache\n            --per_gpu_eval_batch_size=1\n            ".split()
								self.run_and_check(a		)

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






							'''simple docstring'''





							set_seed(3)
							# generate train_data and objective_set
							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, Any]         =      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?
							__UpperCamelCase			:     Dict         =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

							# load pretrained model
							__UpperCamelCase			:     str         =      load_gpta("gpt2").to(_lowerCamelCase)
							print("computing perplexity on objective set")
							__UpperCamelCase			:     Union[str, Any]         =      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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[Any]		,						_lowerCamelCase				:    Any=15		,						_lowerCamelCase				:    Union[str, Any]=128		,						_lowerCamelCase				:    Any=100		,						_lowerCamelCase				:    List[Any]="igf_model.pt"		,						)      ->					Any:






							'''simple docstring'''





							set_seed(42)

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

							# Initialize secondary learner to use embedding weights of model
							__UpperCamelCase			:     Any         =      SecondaryLearner(_lowerCamelCase)

							# Train secondary learner
							__UpperCamelCase			:     Union[str, Any]         =      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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    List[str]		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Optional[Any]		,						_lowerCamelCase				:    int=32		,						_lowerCamelCase				:    Tuple=1_000		,						_lowerCamelCase				:    Dict=16		,						_lowerCamelCase				:    Union[str, Any]=1.0		,						_lowerCamelCase				:    Optional[Any]=recopy_gpta		,						_lowerCamelCase				:    List[Any]=None		,						_lowerCamelCase				:    Optional[Any]=10		,						_lowerCamelCase				:    Union[str, Any]="gpt2_finetuned.pt"		,						)      ->					Union[str, Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[Any]         =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
							__UpperCamelCase			:     List[Any]         =      RandomSampler(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      DataLoader(_lowerCamelCase		,						sampler=_lowerCamelCase)

							__UpperCamelCase			:     Tuple         =      max_steps // (len(_lowerCamelCase)) + 1
							__UpperCamelCase			:     List[Any]         =      0
							__UpperCamelCase			:     List[Any]         =      torch.zeros((1, context_len)		,						dtype=torch.long		,						device=_lowerCamelCase)
							__UpperCamelCase     ,		__UpperCamelCase     ,		__UpperCamelCase			:     List[Any]         =      recopy_model(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

							model.train()
							if secondary_learner is not None:
														secondary_learner.to(_lowerCamelCase)
														secondary_learner.eval()
							__UpperCamelCase			:     Union[str, Any]         =      []
							__UpperCamelCase			:     Any         =      0

							__UpperCamelCase			:     List[Any]         =      []
							__UpperCamelCase			:     Any         =      []

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

																					if secondary_learner is not None:
																												__UpperCamelCase			:     Optional[int]         =      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:
																																			__UpperCamelCase			:     List[str]         =      -1
																												if predicted_q < threshold:
																																			__UpperCamelCase			:     Optional[int]         =      False

            # If we passed the filter, add the context to the batch!
																					if do_backprop:
																												contexts.append(np.array(context.cpu()))
																												__UpperCamelCase			:     Optional[Any]         =      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()
																												__UpperCamelCase			:     str         =      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:
																																			__UpperCamelCase			:     List[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 			_SCREAMING_SNAKE_CASE      (	)      ->					Optional[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      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
							__UpperCamelCase			:     Any         =      joblib.load("data/IGF_values.jbl")

							# Train secondary learner
							__UpperCamelCase			:     Optional[Any]         =      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
							__UpperCamelCase			:     int         =      GPTaLMHeadModel.from_pretrained("gpt2")
							set_seed(42)

							# Generate train and test data to train and evaluate gpt2 model
							__UpperCamelCase     ,		__UpperCamelCase			:     Union[str, 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()