



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__  (			__SCREAMING_SNAKE_CASE=32			,     __SCREAMING_SNAKE_CASE=10			,     __SCREAMING_SNAKE_CASE=100			,     __SCREAMING_SNAKE_CASE=1026			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl"			,     __SCREAMING_SNAKE_CASE="igf_context_pairs.jbl"			,     )		->							Optional[int]:
		set_seed(3 )
		# generate train_data and objective_set
		UpperCAmelCase_					,						UpperCAmelCase_						=				generate_datasets(
		    __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     number=__SCREAMING_SNAKE_CASE			,     min_len=1026			,     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?
		UpperCAmelCase_						=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )

		# load pretrained model
		UpperCAmelCase_						=				load_gpta("gpt2" ).to(__SCREAMING_SNAKE_CASE )
		print("computing perplexity on objective set" )
		UpperCAmelCase_						=				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 snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=15			,     __SCREAMING_SNAKE_CASE=128			,     __SCREAMING_SNAKE_CASE=100			,     __SCREAMING_SNAKE_CASE="igf_model.pt"			,     )		->							Tuple:
		set_seed(42 )

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

		# Initialize secondary learner to use embedding weights of model
		UpperCAmelCase_						=				SecondaryLearner(__SCREAMING_SNAKE_CASE )

		# Train secondary learner
		UpperCAmelCase_						=				train_secondary_learner(
		    __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     max_epochs=__SCREAMING_SNAKE_CASE			,     batch_size=__SCREAMING_SNAKE_CASE			,     eval_freq=100			,     igf_model_path=__SCREAMING_SNAKE_CASE			,     )

		del model, secondary_learner_train_data
		torch.cuda.empty_cache()

		return secondary_learner







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=32			,     __SCREAMING_SNAKE_CASE=1000			,     __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"			,     )		->							Tuple:
		UpperCAmelCase_						=				torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
		UpperCAmelCase_						=				RandomSampler(__SCREAMING_SNAKE_CASE )
		UpperCAmelCase_						=				DataLoader(__SCREAMING_SNAKE_CASE			,     sampler=__SCREAMING_SNAKE_CASE )

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

		UpperCAmelCase_						=				[]
		UpperCAmelCase_						=				[]

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

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

            # If we passed the filter, add the context to the batch!
						if do_backprop:
								contexts.append(np.array(context.cpu() ) )
								UpperCAmelCase_						=				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_						=				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_						=				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 snake_case__  (			)		->							Dict:
		UpperCAmelCase_						=				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=100			,     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=100			,     type=__SCREAMING_SNAKE_CASE			,     help="secondary model evaluation is triggered at eval_freq" )

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

		parser.add_argument(
		    "--secondary_learner_batch_size"			,     default=128			,     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=100			,     type=__SCREAMING_SNAKE_CASE			,     help="The number of examples split to be used as objective_set/test_data" )

		parser.add_argument(
		    "--min_len"			,     default=1026			,     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=100			,     min_len=1026			,     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
		UpperCAmelCase_						=				joblib.load("data/IGF_values.jbl" )

		# Train secondary learner
		UpperCAmelCase_						=				training_secondary_learner(
		    __SCREAMING_SNAKE_CASE			,     secondary_learner_max_epochs=15			,     secondary_learner_batch_size=128			,     eval_freq=100			,     igf_model_path="igf_model.pt"			,     )

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

		# Generate train and test data to train and evaluate gpt2 model
		UpperCAmelCase_					,						UpperCAmelCase_						=				generate_datasets(
		    context_len=32			,     file="data/tokenized_stories_train_wikitext103.jbl"			,     number=100			,     min_len=1026			,     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=1000			,     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()






import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							np.array:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		UpperCAmelCase_						=				"f32le"
		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-i",
		    "pipe:0",
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]

		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdin=subprocess.PIPE			,     stdout=subprocess.PIPE ) as ffmpeg_process:
						UpperCAmelCase_						=				ffmpeg_process.communicate(__SCREAMING_SNAKE_CASE )
		except FileNotFoundError as error:
				raise ValueError("ffmpeg was not found but is required to load audio files from filename" ) from error
		UpperCAmelCase_						=				output_stream[0]
		UpperCAmelCase_						=				np.frombuffer(__SCREAMING_SNAKE_CASE			,     np.floataa )
		if audio.shape[0] == 0:
				raise ValueError("Malformed soundfile" )
		return audio







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							Dict:
		UpperCAmelCase_						=				f'''{sampling_rate}'''
		UpperCAmelCase_						=				"1"
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		UpperCAmelCase_						=				platform.system()
		if system == "Linux":
				UpperCAmelCase_						=				"alsa"
				UpperCAmelCase_						=				"default"
		elif system == "Darwin":
				UpperCAmelCase_						=				"avfoundation"
				UpperCAmelCase_						=				":0"
		elif system == "Windows":
				UpperCAmelCase_						=				"dshow"
				UpperCAmelCase_						=				"default"

		UpperCAmelCase_						=				[
		    "ffmpeg",
		    "-f",
		    format_,
		    "-i",
		    input_,
		    "-ac",
		    ac,
		    "-ar",
		    ar,
		    "-f",
		    format_for_conversion,
		    "-fflags",
		    "nobuffer",
		    "-hide_banner",
		    "-loglevel",
		    "quiet",
		    "pipe:1",
		]
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		UpperCAmelCase_						=				_ffmpeg_stream(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
		for item in iterator:
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = None			,     __SCREAMING_SNAKE_CASE = "f32le"			,     )		->							int:
		if stream_chunk_s is not None:
				UpperCAmelCase_						=				stream_chunk_s
		else:
				UpperCAmelCase_						=				chunk_length_s

		UpperCAmelCase_						=				ffmpeg_microphone(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     format_for_conversion=__SCREAMING_SNAKE_CASE )
		if format_for_conversion == "s16le":
				UpperCAmelCase_						=				np.intaa
				UpperCAmelCase_						=				2
		elif format_for_conversion == "f32le":
				UpperCAmelCase_						=				np.floataa
				UpperCAmelCase_						=				4
		else:
				raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`''' )

		if stride_length_s is None:
				UpperCAmelCase_						=				chunk_length_s / 6
		UpperCAmelCase_						=				int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
		if isinstance(__SCREAMING_SNAKE_CASE			,     (int, float) ):
				UpperCAmelCase_						=				[stride_length_s, stride_length_s]

		UpperCAmelCase_						=				int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
		UpperCAmelCase_						=				int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
		UpperCAmelCase_						=				datetime.datetime.now()
		UpperCAmelCase_						=				datetime.timedelta(seconds=__SCREAMING_SNAKE_CASE )
		for item in chunk_bytes_iter(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     stride=(stride_left, stride_right)			,     stream=__SCREAMING_SNAKE_CASE ):
				# Put everything back in numpy scale
				UpperCAmelCase_						=				np.frombuffer(item["raw"]			,     dtype=__SCREAMING_SNAKE_CASE )
				UpperCAmelCase_						=				(
				    item["stride"][0] // size_of_sample,
				    item["stride"][1] // size_of_sample,
				)
				UpperCAmelCase_						=				sampling_rate
				audio_time += delta
				if datetime.datetime.now() > audio_time + 10 * delta:
						# We're late !! SKIP
						continue
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE = False )		->							Dict:
		UpperCAmelCase_						=				B""
		UpperCAmelCase_					,						UpperCAmelCase_						=				stride
		if stride_left + stride_right >= chunk_len:
				raise ValueError(
				    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}''' )
		UpperCAmelCase_						=				0
		for raw in iterator:
				acc += raw
				if stream and len(__SCREAMING_SNAKE_CASE ) < chunk_len:
						UpperCAmelCase_						=				(_stride_left, 0)
						yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
				else:
						while len(__SCREAMING_SNAKE_CASE ) >= chunk_len:
								# We are flushing the accumulator
								UpperCAmelCase_						=				(_stride_left, stride_right)
								UpperCAmelCase_						=				{"raw": acc[:chunk_len], "stride": stride}
								if stream:
										UpperCAmelCase_						=				False
								yield item
								UpperCAmelCase_						=				stride_left
								UpperCAmelCase_						=				acc[chunk_len - stride_left - stride_right :]
    # Last chunk
		if len(__SCREAMING_SNAKE_CASE ) > stride_left:
				UpperCAmelCase_						=				{"raw": acc, "stride": (_stride_left, 0)}
				if stream:
						UpperCAmelCase_						=				False
				yield item







def snake_case__  (			__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )		->							Optional[Any]:
		UpperCAmelCase_						=				2**24  # 16Mo
		try:
				with subprocess.Popen(__SCREAMING_SNAKE_CASE			,     stdout=subprocess.PIPE			,     bufsize=__SCREAMING_SNAKE_CASE ) as ffmpeg_process:
						while True:
								UpperCAmelCase_						=				ffmpeg_process.stdout.read(__SCREAMING_SNAKE_CASE )
								if raw == b"":
										break
								yield raw
		except FileNotFoundError as error:
				raise ValueError("ffmpeg was not found but is required to stream audio files from filename" ) from error

