





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      (    a=32,     a=10,     a=100,     a=1026,     a=True,     a="data/tokenized_stories_train_wikitext103.jbl",     a="igf_context_pairs.jbl",     ) ->Optional[int]:
					set_seed(3)
					# generate train_data and objective_set
					lowerCamelCase__    ,		lowerCamelCase__       =       generate_datasets(
					    a,     a,     number=a,     min_len=1026,     trim=a)
					# 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(a)
					print("computing perplexity on objective set")
					lowerCamelCase__       =       compute_perplexity(a,     a,     a).item()
					print("perplexity on objective set:",     a)

					# collect igf pairs and save to file demo.jbl
					collect_objective_set(a,     a,     a,     a,     a,     a,     a,     a)

					# clean up, delete model and data we don't need anymore
					del model, train_data, objective_set
					torch.cuda.empty_cache()





def    __UpperCamelCase      (    a,     a=15,     a=128,     a=100,     a="igf_model.pt",     ) ->List[str]:
					set_seed(42)

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

					# Initialize secondary learner to use embedding weights of model
					lowerCamelCase__       =       SecondaryLearner(a)

					# Train secondary learner
					lowerCamelCase__       =       train_secondary_learner(
					    a,     a,     max_epochs=a,     batch_size=a,     eval_freq=100,     igf_model_path=a,     )

					del model, secondary_learner_train_data
					torch.cuda.empty_cache()

					return secondary_learner





def    __UpperCamelCase      (    a,     a,     a,     a=32,     a=1000,     a=16,     a=1.0,     a=recopy_gpta,     a=None,     a=10,     a="gpt2_finetuned.pt",     ) ->int:
					lowerCamelCase__       =       torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
					lowerCamelCase__       =       RandomSampler(a)
					lowerCamelCase__       =       DataLoader(a,     sampler=a)

					lowerCamelCase__       =       max_steps // (len(a)) + 1
					lowerCamelCase__       =       0
					lowerCamelCase__       =       torch.zeros((1, context_len),     dtype=torch.long,     device=a)
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       recopy_model(a,     a,     a)

					model.train()
					if secondary_learner is not None:
										secondary_learner.to(a)
										secondary_learner.eval()
					lowerCamelCase__       =       []
					lowerCamelCase__       =       0

					lowerCamelCase__       =       []
					lowerCamelCase__       =       []

					# Compute the performance of the transformer model at the beginning
					lowerCamelCase__       =       compute_perplexity(a,     a,     a)
					test_perps.append(a)
					print("Test perplexity, step",     a,     ":",     a)
					for epoch in range(int(a)):
										for step, example in enumerate(a):
															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(a,     labels=a)
															lowerCamelCase__       =       True

															if secondary_learner is not None:
																				lowerCamelCase__       =       secondary_learner.forward(
																				    torch.tensor(a,     dtype=torch.long,     device=a).unsqueeze(0))[0].item()
																				observed_qs.append(float(a))

																				# 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(a,     a,     a)
																									test_perps.append(a)

																									print("Test perplexity, step",     a,     ":",     a)
            # 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(),     a)
					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      (    ) ->List[str]:
					lowerCamelCase__       =       argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task")

					# Required parameters
					parser.add_argument(
					    "--data_dir",     default=a,     type=a,     required=a,     help="The input data dir. Should contain data files for WikiText.",     )
					parser.add_argument(
					    "--model_name_or_path",     default=a,     type=a,     required=a,     help="Path to pretrained model or model identifier from huggingface.co/models",     )
					parser.add_argument(
					    "--data_file",     type=a,     default=a,     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=a,     default=a,     help="A jbl file containing the context and information gain pairs to train secondary learner.",     )

					parser.add_argument(
					    "--output_dir",     default=a,     type=a,     required=a,     help="The output directory where the final fine-tuned model is stored.",     )

					parser.add_argument(
					    "--tokenizer_name",     default=a,     type=a,     help="Pretrained tokenizer name or path if not the same as model_name",     )
					parser.add_argument("--seed",     type=a,     default=a,     help="A seed for reproducible training.")

					parser.add_argument(
					    "--context_len",     default=32,     type=a,     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=a,     help="number of articles that are long enough to be used as our objective set",     )
					parser.add_argument(
					    "--eval_freq",     default=100,     type=a,     help="secondary model evaluation is triggered at eval_freq")

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

					parser.add_argument(
					    "--secondary_learner_batch_size",     default=128,     type=a,     help="batch size of training data for secondary learner",     )

					parser.add_argument(
					    "--batch_size",     default=16,     type=a,     help="batch size of training data of language model(gpt2) ")

					parser.add_argument(
					    "--eval_interval",     default=10,     type=a,     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=a,     help="The number of examples split to be used as objective_set/test_data")

					parser.add_argument(
					    "--min_len",     default=1026,     type=a,     help="The minimum length of the article to be used as objective set")

					parser.add_argument(
					    "--secondary_learner_max_epochs",     default=15,     type=a,     help="number of epochs to train secondary learner")

					parser.add_argument("--trim",     default=a,     type=a,     help="truncate the example if it exceeds context length")

					parser.add_argument(
					    "--threshold",     default=1.0,     type=a,     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=a,     help="finetuned_model_name")

					parser.add_argument(
					    "--recopy_model",     default=a,     type=a,     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=a,     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(
					    a,     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=a)

					# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
					finetune(
					    a,     a,     a,     context_len=32,     max_steps=1000,     batch_size=16,     threshold=1.0,     recopy_model=a,     secondary_learner=a,     eval_interval=10,     finetuned_model_name="gpt2_finetuned.pt",     )


if __name__ == "__main__":
						main()








from __future__ import annotations

import math
import random
from collections.abc import Collection
from typing import overload

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase = None	):
										if components is None:
															lowerCamelCase__       =       []
										lowerCamelCase__       =       list(_lowerCAmelCase	)





					def __len__( self	):
										return len(self.__components	)





					def __str__( self	):
										return "(" + ",".join(map(_lowerCAmelCase	,  self.__components	)	) + ")"





					def __add__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       len(self	)
										if size == len(_lowerCAmelCase	):
															lowerCamelCase__       =       [self.__components[i] + other.component(_lowerCAmelCase	) for i in range(_lowerCAmelCase	)]
															return Vector(_lowerCAmelCase	)
										else:
															raise Exception("must have the same size"	)





					def __sub__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       len(self	)
										if size == len(_lowerCAmelCase	):
															lowerCamelCase__       =       [self.__components[i] - other.component(_lowerCAmelCase	) for i in range(_lowerCAmelCase	)]
															return Vector(_lowerCAmelCase	)
										else:  # error case
															raise Exception("must have the same size"	)





					@overload
					def __mul__( self	,  _lowerCAmelCase	):
										...





					@overload
					def __mul__( self	,  _lowerCAmelCase	):
										...





					def __mul__( self	,  _lowerCAmelCase	):
										if isinstance(_lowerCAmelCase	,  (float, int)	):
															lowerCamelCase__       =       [c * other for c in self.__components]
															return Vector(_lowerCAmelCase	)
										elif isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) and len(self	) == len(_lowerCAmelCase	):
															lowerCamelCase__       =       len(self	)
															lowerCamelCase__       =       [self.__components[i] * other.component(_lowerCAmelCase	) for i in range(_lowerCAmelCase	)]
															return sum(_lowerCAmelCase	)
										else:  # error case
															raise Exception("invalid operand!"	)





					def 					__magic_name__ ( self	):
										return Vector(self.__components	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	):
										if isinstance(_lowerCAmelCase	,  _lowerCAmelCase	) and -len(self.__components	) <= i < len(self.__components	):
															return self.__components[i]
										else:
															raise Exception("index out of range"	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										assert -len(self.__components	) <= pos < len(self.__components	)
										lowerCamelCase__       =       value





					def 					__magic_name__ ( self	):
										if len(self.__components	) == 0:
															raise Exception("Vector is empty"	)
										lowerCamelCase__       =       [c**2 for c in self.__components]
										return math.sqrt(sum(_lowerCAmelCase	)	)






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = False	):
										lowerCamelCase__       =       self * other
										lowerCamelCase__       =       self.euclidean_length() * other.euclidean_length()
										if deg:
															return math.degrees(math.acos(num / den	)	)
										else:
															return math.acos(num / den	)





def    __UpperCamelCase      (    a) ->Vector:
					assert isinstance(a,     a)
					return Vector([0] * dimension)





def    __UpperCamelCase      (    a,     a) ->Vector:
					assert isinstance(a,     a) and (isinstance(a,     a))
					lowerCamelCase__       =       [0] * dimension
					lowerCamelCase__       =       1
					return Vector(a)





def    __UpperCamelCase      (    a,     a,     a) ->Vector:
					assert (
					    isinstance(a,     a)
					    and isinstance(a,     a)
					    and (isinstance(a,     (int, float)))
					)
					return x * scalar + y





def    __UpperCamelCase      (    a,     a,     a) ->Vector:
					random.seed(a)
					lowerCamelCase__       =       [random.randint(a,     a) for _ in range(a)]
					return Vector(a)

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       matrix
										lowerCamelCase__       =       w
										lowerCamelCase__       =       h





					def __str__( self	):
										lowerCamelCase__       =       ""
										for i in range(self.__height	):
															ans += "|"
															for j in range(self.__width	):
																				if j < self.__width - 1:
																									ans += str(self.__matrix[i][j]	) + ","
																				else:
																									ans += str(self.__matrix[i][j]	) + "|\n"
										return ans





					def __add__( self	,  _lowerCAmelCase	):
										if self.__width == other.width() and self.__height == other.height():
															lowerCamelCase__       =       []
															for i in range(self.__height	):
																				lowerCamelCase__       =       [
																				    self.__matrix[i][j] + other.component(_lowerCAmelCase	,  _lowerCAmelCase	)
																				    for j in range(self.__width	)
																				]
																				matrix.append(_lowerCAmelCase	)
															return Matrix(_lowerCAmelCase	,  self.__width	,  self.__height	)
										else:
															raise Exception("matrix must have the same dimension!"	)





					def __sub__( self	,  _lowerCAmelCase	):
										if self.__width == other.width() and self.__height == other.height():
															lowerCamelCase__       =       []
															for i in range(self.__height	):
																				lowerCamelCase__       =       [
																				    self.__matrix[i][j] - other.component(_lowerCAmelCase	,  _lowerCAmelCase	)
																				    for j in range(self.__width	)
																				]
																				matrix.append(_lowerCAmelCase	)
															return Matrix(_lowerCAmelCase	,  self.__width	,  self.__height	)
										else:
															raise Exception("matrices must have the same dimension!"	)





					@overload
					def __mul__( self	,  _lowerCAmelCase	):
										...





					@overload
					def __mul__( self	,  _lowerCAmelCase	):
										...





					def __mul__( self	,  _lowerCAmelCase	):
										if isinstance(_lowerCAmelCase	,  _lowerCAmelCase	):  # matrix-vector
															if len(_lowerCAmelCase	) == self.__width:
																				lowerCamelCase__       =       zero_vector(self.__height	)
																				for i in range(self.__height	):
																									lowerCamelCase__       =       [
																									    self.__matrix[i][j] * other.component(_lowerCAmelCase	)
																									    for j in range(self.__width	)
																									]
																									ans.change_component(_lowerCAmelCase	,  sum(_lowerCAmelCase	)	)
																				return ans
															else:
																				raise Exception(
																				    "vector must have the same size as the "
																				    "number of columns of the matrix!"	)
										elif isinstance(_lowerCAmelCase	,  (int, float)	):  # matrix-scalar
															lowerCamelCase__       =       [
															    [self.__matrix[i][j] * other for j in range(self.__width	)]
															    for i in range(self.__height	)
															]
															return Matrix(_lowerCAmelCase	,  self.__width	,  self.__height	)
										return None





					def 					__magic_name__ ( self	):
										return self.__height





					def 					__magic_name__ ( self	):
										return self.__width





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if 0 <= x < self.__height and 0 <= y < self.__width:
															return self.__matrix[x][y]
										else:
															raise Exception("change_component: indices out of bounds"	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if 0 <= x < self.__height and 0 <= y < self.__width:
															lowerCamelCase__       =       value
										else:
															raise Exception("change_component: indices out of bounds"	)





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if self.__height != self.__width:
															raise Exception("Matrix is not square"	)
										lowerCamelCase__       =       self.__matrix[:x] + self.__matrix[x + 1 :]
										for i in range(len(_lowerCAmelCase	)	):
															lowerCamelCase__       =       minor[i][:y] + minor[i][y + 1 :]
										return Matrix(_lowerCAmelCase	,  self.__width - 1	,  self.__height - 1	).determinant()





					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										if self.__height != self.__width:
															raise Exception("Matrix is not square"	)
										if 0 <= x < self.__height and 0 <= y < self.__width:
															return (-1) ** (x + y) * self.minor(_lowerCAmelCase	,  _lowerCAmelCase	)
										else:
															raise Exception("Indices out of bounds"	)






					def 					__magic_name__ ( self	):
										if self.__height != self.__width:
															raise Exception("Matrix is not square"	)
										if self.__height < 1:
															raise Exception("Matrix has no element"	)
										elif self.__height == 1:
															return self.__matrix[0][0]
										elif self.__height == 2:
															return (
															    self.__matrix[0][0] * self.__matrix[1][1]
															    - self.__matrix[0][1] * self.__matrix[1][0]
															)
										else:
															lowerCamelCase__       =       [
															    self.__matrix[0][y] * self.cofactor(0	,  _lowerCAmelCase	) for y in range(self.__width	)
															]
															return sum(_lowerCAmelCase	)





def    __UpperCamelCase      (    a) ->Matrix:
					lowerCamelCase__       =       [[0] * n for _ in range(a)]
					return Matrix(a,     a,     a)







def    __UpperCamelCase      (    a,     a,     a,     a) ->Matrix:
					random.seed(a)
					lowerCamelCase__       =       [
					    [random.randint(a,     a) for _ in range(a)] for _ in range(a)
					]
					return Matrix(a,     a,     a)

