
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 		lowercase_    (     A__=32				,	A__=10				,	A__=100				,	A__=1026				,	A__=True				,	A__="data/tokenized_stories_train_wikitext103.jbl"				,	A__="igf_context_pairs.jbl"				,	)							->							Optional[Any]:
    """simple docstring"""

    set_seed(3	)
    # generate train_data and objective_set
    snake_case				  =      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?
    snake_case				  =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)

    # load pretrained model
    snake_case				  =      load_gpta("gpt2"	).to(A__	)
    print("computing perplexity on objective set"	)
    snake_case				  =      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 		lowercase_    (     A__				,	A__=15				,	A__=128				,	A__=100				,	A__="igf_model.pt"				,	)							->							Dict:
    """simple docstring"""

    set_seed(42	)

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

    # Initialize secondary learner to use embedding weights of model
    snake_case				  =      SecondaryLearner(A__	)

    # Train secondary learner
    snake_case				  =      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 		lowercase_    (     A__				,	A__				,	A__				,	A__=32				,	A__=1000				,	A__=16				,	A__=1.0				,	A__=recopy_gpta				,	A__=None				,	A__=10				,	A__="gpt2_finetuned.pt"				,	)							->							Dict:
    """simple docstring"""

    snake_case				  =      torch.device("cuda:0" if torch.cuda.is_available() else "cpu"	)
    snake_case				  =      RandomSampler(A__	)
    snake_case				  =      DataLoader(A__				,	sampler=A__	)

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

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

    snake_case				  =      []
    snake_case				  =      []

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

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

                # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()	)	)
                snake_case				  =      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				  =      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				  =      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 		lowercase_    (     )							->							Dict:
    """simple docstring"""

    snake_case				  =      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
    snake_case				  =      joblib.load("data/IGF_values.jbl"	)

    # Train secondary learner
    snake_case				  =      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
    snake_case				  =      GPTaLMHeadModel.from_pretrained("gpt2"	)
    set_seed(42	)

    # Generate train and test data to train and evaluate gpt2 model
    snake_case				  =      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 collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "sail/poolformer_s12": "https://huggingface.co/sail/poolformer_s12/resolve/main/config.json",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Any     =	"poolformer"





     def __init__(self   :					Optional[int]       ,					_A   :					Optional[Any]=3       ,					_A   :					Optional[int]=1_6       ,					_A   :					Dict=1_6       ,					_A   :					Tuple=3       ,					_A   :					Tuple=4.0       ,					_A   :					int=[2, 2, 6, 2]       ,					_A   :					Dict=[6_4, 1_2_8, 3_2_0, 5_1_2]       ,					_A   :					int=[7, 3, 3, 3]       ,					_A   :					List[str]=[4, 2, 2, 2]       ,					_A   :					str=[2, 1, 1, 1]       ,					_A   :					List[Any]=4       ,					_A   :					Any=0.0       ,					_A   :					Optional[Any]="gelu"       ,					_A   :					Optional[Any]=True       ,					_A   :					List[str]=1E-5       ,					_A   :					List[str]=0.02       ,					**_A   :					str       ,					)		->		Tuple:
         snake_case				  =      num_channels
         snake_case				  =      patch_size
         snake_case				  =      stride
         snake_case				  =      padding
         snake_case				  =      pool_size
         snake_case				  =      hidden_sizes
         snake_case				  =      mlp_ratio
         snake_case				  =      depths
         snake_case				  =      patch_sizes
         snake_case				  =      strides
         snake_case				  =      num_encoder_blocks
         snake_case				  =      drop_path_rate
         snake_case				  =      hidden_act
         snake_case				  =      use_layer_scale
         snake_case				  =      layer_scale_init_value
         snake_case				  =      initializer_range
         super().__init__(**_A     )







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Tuple     =	version.parse("1.11"						)





     @property
     def 	UpperCAmelCase(self   :					int     )		->		Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]     )





     @property
     def 	UpperCAmelCase(self   :					int     )		->		float:
         return 2E-3

