

"""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 		lowerCamelCase__		(					_lowerCamelCase=32 ,   _lowerCamelCase=10 ,   _lowerCamelCase=100 ,   _lowerCamelCase=1026 ,   _lowerCamelCase=True ,   _lowerCamelCase="data/tokenized_stories_train_wikitext103.jbl" ,   _lowerCamelCase="igf_context_pairs.jbl" ,   ):

    '''simple docstring'''



    set_seed(3   )
    # generate train_data and objective_set
    _lowerCAmelCase       :   int  =    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       :   int  =    torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'   )

    # load pretrained model
    _lowerCAmelCase       :   Dict  =    load_gpta('gpt2'   ).to(_lowerCamelCase   )
    print('computing perplexity on objective set'   )
    _lowerCAmelCase       :   str  =    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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=15 ,   _lowerCamelCase=128 ,   _lowerCamelCase=100 ,   _lowerCamelCase="igf_model.pt" ,   ):

    '''simple docstring'''



    set_seed(42   )

    # Load pre-trained model
    _lowerCAmelCase       :   int  =    GPTaLMHeadModel.from_pretrained('gpt2'   )

    # Initialize secondary learner to use embedding weights of model
    _lowerCAmelCase       :   Optional[int]  =    SecondaryLearner(_lowerCamelCase   )

    # Train secondary learner
    _lowerCAmelCase       :   int  =    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 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase=32 ,   _lowerCamelCase=1000 ,   _lowerCamelCase=16 ,   _lowerCamelCase=1.0 ,   _lowerCamelCase=recopy_gpta ,   _lowerCamelCase=None ,   _lowerCamelCase=10 ,   _lowerCamelCase="gpt2_finetuned.pt" ,   ):

    '''simple docstring'''



    _lowerCAmelCase       :   str  =    torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'   )
    _lowerCAmelCase       :   Tuple  =    RandomSampler(_lowerCamelCase   )
    _lowerCAmelCase       :   Tuple  =    DataLoader(_lowerCamelCase ,   sampler=_lowerCamelCase   )

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

    model.train()
    if secondary_learner is not None:
        secondary_learner.to(_lowerCamelCase   )
        secondary_learner.eval()
    _lowerCAmelCase       :   Tuple  =    []
    _lowerCAmelCase       :   int  =    0

    _lowerCAmelCase       :   Optional[int]  =    []
    _lowerCAmelCase       :   str  =    []

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

            if secondary_learner is not None:
                _lowerCAmelCase       :   Dict  =    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       :   Union[str, Any]  =    -1
                if predicted_q < threshold:
                    _lowerCAmelCase       :   List[Any]  =    False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()   )   )
                _lowerCAmelCase       :   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()
                _lowerCAmelCase       :   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:
                    _lowerCAmelCase       :   Tuple  =    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 		lowerCamelCase__		(					):

    '''simple docstring'''



    _lowerCAmelCase       :   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=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       :   Optional[int]  =    joblib.load('data/IGF_values.jbl'   )

    # Train secondary learner
    _lowerCAmelCase       :   str  =    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       :   Any  =    GPTaLMHeadModel.from_pretrained('gpt2'   )
    set_seed(42   )

    # Generate train and test data to train and evaluate gpt2 model
    _lowerCAmelCase       :   Any  =    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 argparse
import json
import os
from pathlib import Path

import requests
import torch

from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase						=		logging.get_logger(__name__)


_lowerCAmelCase						=		"""https://openaipublic.azureedge.net/jukebox/models/"""
_lowerCAmelCase						=		{
    """jukebox-1b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """1b_lyrics/prior_level_2.pth.tar""",
    ],
    """jukebox-5b-lyrics""": [
        """5b/vqvae.pth.tar""",
        """5b/prior_level_0.pth.tar""",
        """5b/prior_level_1.pth.tar""",
        """5b_lyrics/prior_level_2.pth.tar""",
    ],
}

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    if key.endswith('.model.1.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.bias' ,   '.conv1d_1.bias'   )
    elif key.endswith('.model.1.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Optional[int]  =    key.replace('.model.1.weight' ,   '.conv1d_1.weight'   )
    elif key.endswith('.model.3.bias'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   int  =    key.replace('.model.3.bias' ,   '.conv1d_2.bias'   )
    elif key.endswith('.model.3.weight'   ) and len(key.split('.'   )   ) > 10:
        _lowerCAmelCase       :   Tuple  =    key.replace('.model.3.weight' ,   '.conv1d_2.weight'   )

    if "conditioner_blocks.0." in key:
        _lowerCAmelCase       :   Dict  =    key.replace('conditioner_blocks.0' ,   'conditioner_blocks'   )

    if "prime_prior" in key:
        _lowerCAmelCase       :   str  =    key.replace('prime_prior' ,   'encoder'   )

    if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
        _lowerCAmelCase       :   Optional[Any]  =    key.replace('.emb.' ,   '.'   )

    if key.endswith('k'   ):  # replace vqvae.X.k with vqvae.X.codebook
        return key.replace('.k' ,   '.codebook'   )
    if "y_emb." in key:
        return key.replace('y_emb.' ,   'metadata_embedding.'   )

    if "x_emb.emb." in key:
        _lowerCAmelCase       :   Any  =    key.replace('0.x_emb.emb' ,   'embed_tokens'   )

    if "prime_state_ln" in key:
        return key.replace('prime_state_ln' ,   'encoder.final_layer_norm'   )
    if ".ln" in key:
        return key.replace('.ln' ,   '.layer_norm'   )
    if "_ln" in key:
        return key.replace('_ln' ,   '_layer_norm'   )

    if "prime_state_proj" in key:
        return key.replace('prime_state_proj' ,   'encoder.proj_in'   )
    if "prime_x_out" in key:
        return key.replace('prime_x_out' ,   'encoder.lm_head'   )
    if "prior.x_out" in key:
        return key.replace('x_out' ,   'fc_proj_out'   )
    if "x_emb" in key:
        return key.replace('x_emb' ,   'embed_tokens'   )

    return key

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[int]  =    {}
    import re

    _lowerCAmelCase       :   Optional[Any]  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Optional[int]  =    re.compile(
        R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(R'encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Union[str, Any]  =    re.compile(
        R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   Tuple  =    re.compile(R'decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)'   )

    _lowerCAmelCase       :   Optional[int]  =    re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)'   )
    _lowerCAmelCase       :   Dict  =    re.compile(
        R'conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)'   )
    _lowerCAmelCase       :   List[str]  =    re.compile(R'conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)'   )

    for original_key, value in state_dict.items():
        # rename vqvae.encoder keys
        if re_encoder_block_conv_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   int  =    re_encoder_block_conv_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   int  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[2]   ) * 2 + int(groups[3]   )
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Optional[int]  =    re_encoder_block_conv_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_encoder_block_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_encoder_block_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    int(groups[2]   ) * 2 + int(groups[3]   )
            _lowerCAmelCase       :   Tuple  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   Union[str, Any]  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
            _lowerCAmelCase       :   Optional[int]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Optional[int]  =    prefix + resnet_block
            _lowerCAmelCase       :   Dict  =    re_encoder_block_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_encoder_block_proj_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    re_encoder_block_proj_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Union[str, Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
            _lowerCAmelCase       :   Any  =    re_encoder_block_proj_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # rename vqvae.decoder keys
        elif re_decoder_block_conv_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Optional[int]  =    re_decoder_block_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            _lowerCAmelCase       :   Dict  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_decoder_block_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_decoder_block_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Any  =    re_decoder_block_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Dict  =    regex_match.groups()
            _lowerCAmelCase       :   Dict  =    int(groups[2]   ) * 2 + int(groups[3]   ) - 2
            _lowerCAmelCase       :   Dict  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   int  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
            _lowerCAmelCase       :   Optional[int]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   List[Any]  =    prefix + resnet_block
            _lowerCAmelCase       :   str  =    re_decoder_block_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_decoder_block_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Any  =    re_decoder_block_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[Any]  =    regex_match.groups()
            _lowerCAmelCase       :   str  =    f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   str  =    re_decoder_block_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # rename prior cond.model to upsampler.upsample_block and resnet
        elif re_prior_cond_conv_out.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   List[Any]  =    re_prior_cond_conv_out.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Any  =    regex_match.groups()
            _lowerCAmelCase       :   Optional[int]  =    int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            _lowerCAmelCase       :   Any  =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
            _lowerCAmelCase       :   List[str]  =    re_prior_cond_conv_out.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_prior_cond_resnet.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_resnet.match(_lowerCamelCase   )
            _lowerCAmelCase       :   Tuple  =    regex_match.groups()
            _lowerCAmelCase       :   Any  =    int(groups[1]   ) * 2 + int(groups[2]   ) - 2
            _lowerCAmelCase       :   Tuple  =    {'1': 1, '3': 2}[groups[-2]]
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
            _lowerCAmelCase       :   List[str]  =    f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    prefix + resnet_block
            _lowerCAmelCase       :   List[str]  =    re_prior_cond_resnet.sub(_lowerCamelCase ,   _lowerCamelCase   )

        elif re_prior_cond_proj_in.fullmatch(_lowerCamelCase   ):
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.match(_lowerCamelCase   )
            _lowerCAmelCase       :   List[str]  =    regex_match.groups()
            _lowerCAmelCase       :   List[Any]  =    f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
            _lowerCAmelCase       :   Dict  =    re_prior_cond_proj_in.sub(_lowerCamelCase ,   _lowerCamelCase   )

        # keep original key
        else:
            _lowerCAmelCase       :   Optional[Any]  =    original_key

        _lowerCAmelCase       :   List[Any]  =    replace_key(_lowerCamelCase   )

        if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
            print(f"""failed converting {original_key} to {key}, does not match"""   )

        # handle missmatched shape
        elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
            _lowerCAmelCase       :   Dict  =    model_state_dict[f"""{key_prefix}.{key}"""]
            print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match"""   )
            _lowerCAmelCase       :   Optional[int]  =    original_key

        _lowerCAmelCase       :   Union[str, Any]  =    original_key
        _lowerCAmelCase       :   Optional[Any]  =    value

    return new_dict

@torch.no_grad()
def 		lowerCamelCase__		(					_lowerCamelCase=None ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    for file in MODEL_MAPPING[model_name]:
        if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}"""   ):
            _lowerCAmelCase       :   str  =    requests.get(f"""{PREFIX}{file}""" ,   allow_redirects=_lowerCamelCase   )
            os.makedirs(f"""{pytorch_dump_folder_path}/""" ,   exist_ok=_lowerCamelCase   )
            open(f"""{pytorch_dump_folder_path}/{file.split('/'   )[-1]}""" ,   'wb'   ).write(r.content   )

    _lowerCAmelCase       :   Union[str, Any]  =    MODEL_MAPPING[model_name.split('/'   )[-1]]

    _lowerCAmelCase       :   Optional[Any]  =    JukeboxConfig.from_pretrained(_lowerCamelCase   )
    _lowerCAmelCase       :   List[str]  =    JukeboxModel(_lowerCamelCase   )

    _lowerCAmelCase       :   int  =    []
    _lowerCAmelCase       :   Any  =    {}
    for i, dict_name in enumerate(_lowerCamelCase   ):
        _lowerCAmelCase       :   Union[str, Any]  =    torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split('/'   )[-1]}"""   )['model']

        _lowerCAmelCase       :   Optional[Any]  =    {}
        for k in old_dic.keys():
            if k.endswith('.b'   ):
                _lowerCAmelCase       :   int  =    old_dic[k]
            elif k.endswith('.w'   ):
                _lowerCAmelCase       :   Tuple  =    old_dic[k]
            elif "level_2" not in dict_name and "cond.model." in k:
                _lowerCAmelCase       :   str  =    old_dic[k]
            else:
                _lowerCAmelCase       :   Optional[Any]  =    old_dic[k]

        _lowerCAmelCase       :   List[str]  =    'vqvae' if i == 0 else f"""priors.{3 - i}"""
        _lowerCAmelCase       :   Tuple  =    fix_jukebox_keys(_lowerCamelCase ,   model.state_dict() ,   _lowerCamelCase ,   _lowerCamelCase   )
        weight_dict.append(_lowerCamelCase   )

    _lowerCAmelCase       :   List[Any]  =    weight_dict.pop(0   )
    model.vqvae.load_state_dict(_lowerCamelCase   )
    for i in range(len(_lowerCamelCase   )   ):
        model.priors[i].load_state_dict(weight_dict[2 - i]   )

    Path(_lowerCamelCase   ).mkdir(exist_ok=_lowerCamelCase   )
    with open(f"""{pytorch_dump_folder_path}/mapping.json""" ,   'w'   ) as txtfile:
        json.dump(_lowerCamelCase ,   _lowerCamelCase   )

    print(f"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
    model.save_pretrained(_lowerCamelCase   )

    return weight_dict


if __name__ == "__main__":
 _lowerCAmelCase						=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default="""jukebox-5b-lyrics""",
     type=str,
     help="""Name of the model you'd like to convert.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""jukebox-5b-lyrics-converted""",
     type=str,
     help="""Path to the output PyTorch model directory.""",
 )
 _lowerCAmelCase						=		parser.parse_args()
 convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)





