





import argparse
import csv
import json
import math
import time

import numpy as np
import torch
import torch.optim as optim
import torch.utils.data as data
from nltk.tokenize.treebank import TreebankWordDetokenizer
from pplm_classification_head import ClassificationHead
from torch import nn
from torchtext import data as torchtext_data
from torchtext import datasets
from tqdm import tqdm, trange

from transformers import GPTaLMHeadModel, GPTaTokenizer


torch.manual_seed(0)
np.random.seed(0)
_SCREAMING_SNAKE_CASE			=							1e-10
_SCREAMING_SNAKE_CASE			=							"This is incredible! I love it, this is the best chicken I have ever had."
_SCREAMING_SNAKE_CASE			=							1_00

class 				lowerCAmelCase_						(						nn.Module						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase="gpt2-medium"     ,       _lowerCAmelCase=False     ,       _lowerCAmelCase="cpu"						)      ->							Optional[int]:
     super().__init__()
     _lowerCAmelCase		     = GPTaTokenizer.from_pretrained(_lowerCAmelCase						)
     _lowerCAmelCase		     = GPTaLMHeadModel.from_pretrained(_lowerCAmelCase						)
     _lowerCAmelCase		     = self.encoder.transformer.config.hidden_size
     _lowerCAmelCase		     = ClassificationHead(class_size=_lowerCAmelCase     ,       embed_size=self.embed_size						)
     _lowerCAmelCase		     = cached_mode
     _lowerCAmelCase		     = device




 def 							_snake_case				(		self						)      ->							Optional[Any]:
     return self.classifier_head




 def 							_snake_case				(		self						)      ->							Tuple:







     for param in self.encoder.parameters():
         _lowerCAmelCase		     = False
     self.classifier_head.train()




 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							Any:
     _lowerCAmelCase		     = x.ne(0						).unsqueeze(2						).repeat(1     ,       1     ,       self.embed_size						).float().to(self.device						).detach()
     _lowerCAmelCase		     = self.encoder.transformer(_lowerCAmelCase						)["last_hidden_state"]
     _lowerCAmelCase		     = hidden * mask
     _lowerCAmelCase		     = torch.sum(_lowerCAmelCase     ,       dim=1						) / (torch.sum(_lowerCAmelCase     ,       dim=1						).detach() + EPSILON)
     return avg_hidden





 def 							_snake_case				(		self     ,       _lowerCAmelCase						)      ->							int:







     if self.cached_mode:
         _lowerCAmelCase		     = x.to(self.device						)
     else:
         _lowerCAmelCase		     = self.avg_representation(x.to(self.device						)						)

     _lowerCAmelCase		     = self.classifier_head(_lowerCAmelCase						)
     _lowerCAmelCase		     = nn.functional.log_softmax(_lowerCAmelCase     ,       dim=-1						)

     return probs







class 				lowerCAmelCase_						(						data.Dataset						):




 def __init__(		self     ,       _lowerCAmelCase     ,       _lowerCAmelCase						)      ->							Tuple:
     _lowerCAmelCase		     = X
     _lowerCAmelCase		     = y




 def __len__(		self						)      ->							str:
     return len(self.X						)





 def __getitem__(		self     ,       _lowerCAmelCase						)      ->							List[str]:
     _lowerCAmelCase		     = {}
     _lowerCAmelCase		     = self.X[index]
     _lowerCAmelCase		     = self.y[index]
     return data







def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[int]      ):



    '''simple docstring'''





    def pad_sequences(SCREAMING_SNAKE_CASE_					:   List[Any]      ):
        _lowerCAmelCase		     = [len(lowercase__      ) for seq in sequences]

        _lowerCAmelCase		     = torch.zeros(len(lowercase__      )						,    max(lowercase__      )      ).long()  # padding value = 0

        for i, seq in enumerate(lowercase__      ):
            _lowerCAmelCase		     = lengths[i]
            _lowerCAmelCase		     = seq[:end]

        return padded_sequences, lengths

    _lowerCAmelCase		     = {}
    for key in data[0].keys():
        _lowerCAmelCase		     = [d[key] for d in data]

    _lowerCAmelCase		     = pad_sequences(item_info["X"]      )
    _lowerCAmelCase		     = torch.tensor(item_info["y"]						,    dtype=torch.long      )

    return x_batch, y_batch


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[Any]      ):



    '''simple docstring'''





    _lowerCAmelCase		     = {}
    for key in data[0].keys():
        _lowerCAmelCase		     = [d[key] for d in data]

    _lowerCAmelCase		     = torch.cat(item_info["X"]						,    0      )
    _lowerCAmelCase		     = torch.tensor(item_info["y"]						,    dtype=torch.long      )

    return x_batch, y_batch


def 		__a(SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   Optional[int]						,    SCREAMING_SNAKE_CASE_					:   int						,    SCREAMING_SNAKE_CASE_					:   List[str]=0						,    SCREAMING_SNAKE_CASE_					:   List[str]=10						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]="cpu"      ):



    '''simple docstring'''





    _lowerCAmelCase		     = 0
    discriminator.train_custom()
    for batch_idx, (input_t, target_t) in enumerate(lowercase__      ):
        _lowerCAmelCase		     = input_t.to(lowercase__      ), target_t.to(lowercase__      )

        optimizer.zero_grad()

        _lowerCAmelCase		     = discriminator(lowercase__      )
        _lowerCAmelCase		     = nn.functional.nll_loss(lowercase__						,    lowercase__      )
        loss.backward(retain_graph=lowercase__      )
        optimizer.step()

        samples_so_far += len(lowercase__      )

        if batch_idx % log_interval == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch + 1						,    lowercase__						,    len(data_loader.dataset      )						,    100 * samples_so_far / len(data_loader.dataset      )						,    loss.item()						,    )      )


def 		__a(SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   List[str]="cpu"      ):



    '''simple docstring'''





    discriminator.eval()
    _lowerCAmelCase		     = 0
    _lowerCAmelCase		     = 0
    with torch.no_grad():
        for input_t, target_t in data_loader:
            _lowerCAmelCase		     = input_t.to(lowercase__      ), target_t.to(lowercase__      )
            _lowerCAmelCase		     = discriminator(lowercase__      )
            # sum up batch loss
            test_loss += nn.functional.nll_loss(lowercase__						,    lowercase__						,    reduction="sum"      ).item()
            # get the index of the max log-probability
            _lowerCAmelCase		     = output_t.argmax(dim=1						,    keepdim=lowercase__      )
            correct += pred_t.eq(target_t.view_as(lowercase__      )      ).sum().item()

    test_loss /= len(data_loader.dataset      )

    print(
        "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
            lowercase__						,    lowercase__						,    len(data_loader.dataset      )						,    100.0 * correct / len(data_loader.dataset      )      )      )


def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Tuple=False						,    SCREAMING_SNAKE_CASE_					:   List[Any]="cpu"      ):



    '''simple docstring'''





    _lowerCAmelCase		     = model.tokenizer.encode(lowercase__      )
    _lowerCAmelCase		     = torch.tensor([input_t]						,    dtype=torch.long						,    device=lowercase__      )
    if cached:
        _lowerCAmelCase		     = model.avg_representation(lowercase__      )

    _lowerCAmelCase		     = model(lowercase__      ).data.cpu().numpy().flatten().tolist()
    print("Input sentence:"						,    lowercase__      )
    print(
        "Predictions:"						,    ", ".join("{}: {:.4f}".format(lowercase__						,    math.exp(lowercase__      )      ) for c, log_prob in zip(lowercase__						,    lowercase__      )      )						,    )


def 		__a(SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   List[Any]						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   List[str]=False						,    SCREAMING_SNAKE_CASE_					:   List[str]="cpu"      ):



    '''simple docstring'''





    _lowerCAmelCase		     = torch.utils.data.DataLoader(dataset=lowercase__						,    batch_size=lowercase__						,    collate_fn=lowercase__      )

    _lowerCAmelCase		     = []
    _lowerCAmelCase		     = []
    for batch_idx, (x, y) in enumerate(tqdm(lowercase__						,    ascii=lowercase__      )      ):
        with torch.no_grad():
            _lowerCAmelCase		     = x.to(lowercase__      )
            _lowerCAmelCase		     = discriminator.avg_representation(lowercase__      ).cpu().detach()
            _lowerCAmelCase		     = torch.unbind(avg_rep.unsqueeze(1      )      )
            xs += avg_rep_list
            ys += y.cpu().numpy().tolist()

    _lowerCAmelCase		     = torch.utils.data.DataLoader(
        dataset=Dataset(lowercase__						,    lowercase__      )						,    batch_size=lowercase__						,    shuffle=lowercase__						,    collate_fn=lowercase__      )

    return data_loader


def 		__a(SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   Any=None						,    SCREAMING_SNAKE_CASE_					:   Dict="gpt2-medium"						,    SCREAMING_SNAKE_CASE_					:   str=10						,    SCREAMING_SNAKE_CASE_					:   Tuple=64						,    SCREAMING_SNAKE_CASE_					:   List[Any]=10						,    SCREAMING_SNAKE_CASE_					:   str=False						,    SCREAMING_SNAKE_CASE_					:   int=False						,    SCREAMING_SNAKE_CASE_					:   Optional[int]=False						,    ):



    '''simple docstring'''





    _lowerCAmelCase		     = "cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

    print("Preprocessing {} dataset...".format(lowercase__      )      )
    _lowerCAmelCase		     = time.time()

    if dataset == "SST":
        _lowerCAmelCase		     = ["positive", "negative", "very positive", "very negative", "neutral"]
        _lowerCAmelCase		     = {c: i for i, c in enumerate(lowercase__      )}

        _lowerCAmelCase		     = Discriminator(
            class_size=len(lowercase__      )						,    pretrained_model=lowercase__						,    cached_mode=lowercase__						,    device=lowercase__      ).to(lowercase__      )

        _lowerCAmelCase		     = torchtext_data.Field()
        _lowerCAmelCase		     = torchtext_data.Field(sequential=lowercase__      )
        _lowerCAmelCase		     = datasets.SST.splits(
            lowercase__						,    lowercase__						,    fine_grained=lowercase__						,    train_subtrees=lowercase__						,    )

        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        for i in trange(len(lowercase__      )						,    ascii=lowercase__      ):
            _lowerCAmelCase		     = TreebankWordDetokenizer().detokenize(vars(train_data[i]      )["text"]      )
            _lowerCAmelCase		     = discriminator.tokenizer.encode(lowercase__      )
            _lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=lowercase__						,    dtype=torch.long      )
            x.append(lowercase__      )
            y.append(classaidx[vars(train_data[i]      )["label"]]      )
        _lowerCAmelCase		     = Dataset(lowercase__						,    lowercase__      )

        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        for i in trange(len(lowercase__      )						,    ascii=lowercase__      ):
            _lowerCAmelCase		     = TreebankWordDetokenizer().detokenize(vars(test_data[i]      )["text"]      )
            _lowerCAmelCase		     = discriminator.tokenizer.encode(lowercase__      )
            _lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=lowercase__						,    dtype=torch.long      )
            test_x.append(lowercase__      )
            test_y.append(classaidx[vars(test_data[i]      )["label"]]      )
        _lowerCAmelCase		     = Dataset(lowercase__						,    lowercase__      )

        _lowerCAmelCase		     = {
            "class_size": len(lowercase__      ),
            "embed_size": discriminator.embed_size,
            "pretrained_model": pretrained_model,
            "class_vocab": classaidx,
            "default_class": 2,
        }

    elif dataset == "clickbait":
        _lowerCAmelCase		     = ["non_clickbait", "clickbait"]
        _lowerCAmelCase		     = {c: i for i, c in enumerate(lowercase__      )}

        _lowerCAmelCase		     = Discriminator(
            class_size=len(lowercase__      )						,    pretrained_model=lowercase__						,    cached_mode=lowercase__						,    device=lowercase__      ).to(lowercase__      )

        with open("datasets/clickbait/clickbait_train_prefix.txt"      ) as f:
            _lowerCAmelCase		     = []
            for i, line in enumerate(lowercase__      ):
                try:
                    data.append(eval(lowercase__      )      )
                except Exception:
                    print("Error evaluating line {}: {}".format(lowercase__						,    lowercase__      )      )
                    continue
        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        with open("datasets/clickbait/clickbait_train_prefix.txt"      ) as f:
            for i, line in enumerate(tqdm(lowercase__						,    ascii=lowercase__      )      ):
                try:
                    _lowerCAmelCase		     = eval(lowercase__      )
                    _lowerCAmelCase		     = discriminator.tokenizer.encode(d["text"]      )

                    if len(lowercase__      ) < max_length_seq:
                        _lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=lowercase__						,    dtype=torch.long      )
                    else:
                        print("Line {} is longer than maximum length {}".format(lowercase__						,    lowercase__      )      )
                        continue
                    x.append(lowercase__      )
                    y.append(d["label"]      )
                except Exception:
                    print("Error evaluating / tokenizing line {}, skipping it".format(lowercase__      )      )
                    pass

        _lowerCAmelCase		     = Dataset(lowercase__						,    lowercase__      )
        _lowerCAmelCase		     = int(0.9 * len(lowercase__      )      )
        _lowerCAmelCase		     = len(lowercase__      ) - train_size
        _lowerCAmelCase		     = torch.utils.data.random_split(lowercase__						,    [train_size, test_size]      )

        _lowerCAmelCase		     = {
            "class_size": len(lowercase__      ),
            "embed_size": discriminator.embed_size,
            "pretrained_model": pretrained_model,
            "class_vocab": classaidx,
            "default_class": 1,
        }

    elif dataset == "toxic":
        _lowerCAmelCase		     = ["non_toxic", "toxic"]
        _lowerCAmelCase		     = {c: i for i, c in enumerate(lowercase__      )}

        _lowerCAmelCase		     = Discriminator(
            class_size=len(lowercase__      )						,    pretrained_model=lowercase__						,    cached_mode=lowercase__						,    device=lowercase__      ).to(lowercase__      )

        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        with open("datasets/toxic/toxic_train.txt"      ) as f:
            for i, line in enumerate(tqdm(lowercase__						,    ascii=lowercase__      )      ):
                try:
                    _lowerCAmelCase		     = eval(lowercase__      )
                    _lowerCAmelCase		     = discriminator.tokenizer.encode(d["text"]      )

                    if len(lowercase__      ) < max_length_seq:
                        _lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=lowercase__						,    dtype=torch.long      )
                    else:
                        print("Line {} is longer than maximum length {}".format(lowercase__						,    lowercase__      )      )
                        continue
                    x.append(lowercase__      )
                    y.append(int(np.sum(d["label"]      ) > 0      )      )
                except Exception:
                    print("Error evaluating / tokenizing line {}, skipping it".format(lowercase__      )      )
                    pass

        _lowerCAmelCase		     = Dataset(lowercase__						,    lowercase__      )
        _lowerCAmelCase		     = int(0.9 * len(lowercase__      )      )
        _lowerCAmelCase		     = len(lowercase__      ) - train_size
        _lowerCAmelCase		     = torch.utils.data.random_split(lowercase__						,    [train_size, test_size]      )

        _lowerCAmelCase		     = {
            "class_size": len(lowercase__      ),
            "embed_size": discriminator.embed_size,
            "pretrained_model": pretrained_model,
            "class_vocab": classaidx,
            "default_class": 0,
        }

    else:  # if dataset == "generic":
        # This assumes the input dataset is a TSV with the following structure:
        # class \t text

        if dataset_fp is None:
            raise ValueError("When generic dataset is selected, dataset_fp needs to be specified aswell."      )

        _lowerCAmelCase		     = set()
        with open(lowercase__      ) as f:
            _lowerCAmelCase		     = csv.reader(lowercase__						,    delimiter="\t"      )
            for row in tqdm(lowercase__						,    ascii=lowercase__      ):
                if row:
                    classes.add(row[0]      )

        _lowerCAmelCase		     = sorted(lowercase__      )
        _lowerCAmelCase		     = {c: i for i, c in enumerate(lowercase__      )}

        _lowerCAmelCase		     = Discriminator(
            class_size=len(lowercase__      )						,    pretrained_model=lowercase__						,    cached_mode=lowercase__						,    device=lowercase__      ).to(lowercase__      )

        _lowerCAmelCase		     = []
        _lowerCAmelCase		     = []
        with open(lowercase__      ) as f:
            _lowerCAmelCase		     = csv.reader(lowercase__						,    delimiter="\t"      )
            for i, row in enumerate(tqdm(lowercase__						,    ascii=lowercase__      )      ):
                if row:
                    _lowerCAmelCase		     = row[0]
                    _lowerCAmelCase		     = row[1]

                    try:
                        _lowerCAmelCase		     = discriminator.tokenizer.encode(lowercase__      )
                        if len(lowercase__      ) < max_length_seq:
                            _lowerCAmelCase		     = torch.tensor([50256] + seq						,    device=lowercase__						,    dtype=torch.long      )

                        else:
                            print("Line {} is longer than maximum length {}".format(lowercase__						,    lowercase__      )      )
                            continue

                        x.append(lowercase__      )
                        y.append(classaidx[label]      )

                    except Exception:
                        print("Error tokenizing line {}, skipping it".format(lowercase__      )      )
                        pass

        _lowerCAmelCase		     = Dataset(lowercase__						,    lowercase__      )
        _lowerCAmelCase		     = int(0.9 * len(lowercase__      )      )
        _lowerCAmelCase		     = len(lowercase__      ) - train_size
        _lowerCAmelCase		     = torch.utils.data.random_split(lowercase__						,    [train_size, test_size]      )

        _lowerCAmelCase		     = {
            "class_size": len(lowercase__      ),
            "embed_size": discriminator.embed_size,
            "pretrained_model": pretrained_model,
            "class_vocab": classaidx,
            "default_class": 0,
        }

    _lowerCAmelCase		     = time.time()
    print("Preprocessed {} data points".format(len(lowercase__      ) + len(lowercase__      )      )      )
    print("Data preprocessing took: {:.3f}s".format(end - start      )      )

    if cached:
        print("Building representation cache..."      )

        _lowerCAmelCase		     = time.time()

        _lowerCAmelCase		     = get_cached_data_loader(lowercase__						,    lowercase__						,    lowercase__						,    shuffle=lowercase__						,    device=lowercase__      )

        _lowerCAmelCase		     = get_cached_data_loader(lowercase__						,    lowercase__						,    lowercase__						,    device=lowercase__      )

        _lowerCAmelCase		     = time.time()
        print("Building representation cache took: {:.3f}s".format(end - start      )      )

    else:
        _lowerCAmelCase		     = torch.utils.data.DataLoader(
            dataset=lowercase__						,    batch_size=lowercase__						,    shuffle=lowercase__						,    collate_fn=lowercase__      )
        _lowerCAmelCase		     = torch.utils.data.DataLoader(dataset=lowercase__						,    batch_size=lowercase__						,    collate_fn=lowercase__      )

    if save_model:
        with open("{}_classifier_head_meta.json".format(lowercase__      )						,    "w"      ) as meta_file:
            json.dump(lowercase__						,    lowercase__      )

    _lowerCAmelCase		     = optim.Adam(discriminator.parameters()						,    lr=0.0001      )

    for epoch in range(lowercase__      ):
        _lowerCAmelCase		     = time.time()
        print("\nEpoch"						,    epoch + 1      )

        train_epoch(
            discriminator=lowercase__						,    data_loader=lowercase__						,    optimizer=lowercase__						,    epoch=lowercase__						,    log_interval=lowercase__						,    device=lowercase__						,    )
        evaluate_performance(data_loader=lowercase__						,    discriminator=lowercase__						,    device=lowercase__      )

        _lowerCAmelCase		     = time.time()
        print("Epoch took: {:.3f}s".format(end - start      )      )

        print("\nExample prediction"      )
        predict(lowercase__						,    lowercase__						,    lowercase__						,    cached=lowercase__						,    device=lowercase__      )

        if save_model:
            # torch.save(discriminator.state_dict(),
            #           "{}_discriminator_{}.pt".format(
            #               args.dataset, epoch + 1
            #               ))
            torch.save(
                discriminator.get_classifier().state_dict()						,    "{}_classifier_head_epoch_{}.pt".format(lowercase__						,    epoch + 1      )						,    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE			=							argparse.ArgumentParser(description="Train a discriminator on top of GPT-2 representations")
       parser.add_argument(
           "--dataset",
           type=str,
           default="SST",
           choices=("SST", "clickbait", "toxic", "generic"),
           help=(
               "dataset to train the discriminator on."
               "In case of generic, the dataset is expected"
               "to be a TSBV file with structure: class \\t text"
           ),
       )
       parser.add_argument(
           "--dataset_fp",
           type=str,
           default="",
           help="File path of the dataset to use. Needed only in case of generic datadset",
       )
       parser.add_argument(
           "--pretrained_model", type=str, default="gpt2-medium", help="Pretrained model to use as encoder"
       )
       parser.add_argument("--epochs", type=int, default=10, metavar="N", help="Number of training epochs")
       parser.add_argument(
           "--batch_size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
       )
       parser.add_argument(
           "--log_interval",
           type=int,
           default=10,
           metavar="N",
           help="how many batches to wait before logging training status",
       )
       parser.add_argument("--save_model", action="store_true", help="whether to save the model")
       parser.add_argument("--cached", action="store_true", help="whether to cache the input representations")
       parser.add_argument("--no_cuda", action="store_true", help="use to turn off cuda")
       _SCREAMING_SNAKE_CASE			=							parser.parse_args()

       train_discriminator(**(vars(args)))









'''simple docstring'''


# Copyright (c) 2021-, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

####################################################################################################

#
# Note: If when running this conversion script you're getting an exception:
#     ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_bert/convert_megatron_bert_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#

import argparse
import os
import re
import zipfile

import torch

from transformers import MegatronBertConfig


def 		__a(SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]						,    SCREAMING_SNAKE_CASE_					:   int=0      ):



    '''simple docstring'''





    if name is None:
        _lowerCAmelCase		     = None
    else:
        _lowerCAmelCase		     = "." * max(0						,    spaces - 2      ) + "# {:" + str(50 - spaces      ) + "s}"
        _lowerCAmelCase		     = fmt.format(SCREAMING_SNAKE_CASE_      )

    # Print and recurse (if needed).
    if isinstance(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      ):
        if msg is not None:
            print(SCREAMING_SNAKE_CASE_      )
        for k in val.keys():
            recursive_print(SCREAMING_SNAKE_CASE_						,    val[k]						,    spaces + 2      )
    elif isinstance(SCREAMING_SNAKE_CASE_						,    torch.Tensor      ):
        print(SCREAMING_SNAKE_CASE_						,    ":"						,    val.size()      )
    else:
        print(SCREAMING_SNAKE_CASE_						,    ":"						,    SCREAMING_SNAKE_CASE_      )


def 		__a(SCREAMING_SNAKE_CASE_					:   List[str]						,    SCREAMING_SNAKE_CASE_					:   Union[str, Any]						,    SCREAMING_SNAKE_CASE_					:   Dict						,    SCREAMING_SNAKE_CASE_					:   str						,    SCREAMING_SNAKE_CASE_					:   int      ):



    '''simple docstring'''





    _lowerCAmelCase		     = param.size()
    if checkpoint_version == 1.0:
        # version 1.0 stores [num_heads * hidden_size * num_splits, :]
        _lowerCAmelCase		     = (num_heads, hidden_size, num_splits) + input_shape[1:]
        _lowerCAmelCase		     = param.view(*SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = param.transpose(0						,    2      )
        _lowerCAmelCase		     = param.transpose(1						,    2      ).contiguous()
    elif checkpoint_version >= 2.0:
        # other versions store [num_heads * num_splits * hidden_size, :]
        _lowerCAmelCase		     = (num_heads, num_splits, hidden_size) + input_shape[1:]
        _lowerCAmelCase		     = param.view(*SCREAMING_SNAKE_CASE_      )
        _lowerCAmelCase		     = param.transpose(0						,    1      ).contiguous()
    _lowerCAmelCase		     = param.view(*SCREAMING_SNAKE_CASE_      )
    return param


def 		__a(SCREAMING_SNAKE_CASE_					:   Tuple						,    SCREAMING_SNAKE_CASE_					:   Any						,    SCREAMING_SNAKE_CASE_					:   Optional[Any]      ):



    '''simple docstring'''





    _lowerCAmelCase		     = {}

    # old versions did not store training args
    _lowerCAmelCase		     = input_state_dict.get("args"						,    SCREAMING_SNAKE_CASE_      )
    if ds_args is not None:
        # do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
        # from pprint import pprint
        # pprint(vars(ds_args))

        _lowerCAmelCase		     = ds_args.tokenizer_type
        _lowerCAmelCase		     = ds_args.padded_vocab_size
        _lowerCAmelCase		     = ds_args.max_position_embeddings
        _lowerCAmelCase		     = ds_args.hidden_size
        _lowerCAmelCase		     = ds_args.num_layers
        _lowerCAmelCase		     = ds_args.num_attention_heads
        _lowerCAmelCase		     = ds_args.ffn_hidden_size if "ffn_hidden_size" in ds_args else 4 * ds_args.hidden_size
        # pprint(config)

    # The number of heads.
    _lowerCAmelCase		     = config.num_attention_heads
    # The hidden_size per head.
    _lowerCAmelCase		     = config.hidden_size // heads
    # Megatron-LM checkpoint version
    if "checkpoint_version" in input_state_dict.keys():
        _lowerCAmelCase		     = input_state_dict["checkpoint_version"]
    else:
        _lowerCAmelCase		     = 0.0

    # The model.
    _lowerCAmelCase		     = input_state_dict["model"]
    # The language model.
    _lowerCAmelCase		     = model["language_model"]
    # The embeddings.
    _lowerCAmelCase		     = lm["embedding"]

    # The word embeddings.
    _lowerCAmelCase		     = embeddings["word_embeddings"]["weight"]
    # Truncate the embedding table to vocab_size rows.
    _lowerCAmelCase		     = word_embeddings[: config.vocab_size, :]
    # Store the word embeddings.
    _lowerCAmelCase		     = word_embeddings

    # The position embeddings.
    _lowerCAmelCase		     = embeddings["position_embeddings"]["weight"]
    assert pos_embeddings.size(0      ) == config.max_position_embeddings and pos_embeddings.size(1      ) == config.hidden_size
    # Store the position embeddings.
    _lowerCAmelCase		     = pos_embeddings

    # The token-type embeddings.
    _lowerCAmelCase		     = embeddings["tokentype_embeddings"]["weight"]
    # Store the position embeddings.
    _lowerCAmelCase		     = tokentype_embeddings

    # The transformer.
    _lowerCAmelCase		     = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]

    # The regex to extract layer names.
    _lowerCAmelCase		     = re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)"      )

    # The simple map of names for "automated" rules.
    _lowerCAmelCase		     = {
        "attention.dense": ".attention.output.dense.",
        "self_attention.dense": ".attention.output.dense.",
        "mlp.dense_h_to_4h": ".intermediate.dense.",
        "mlp.dense_4h_to_h": ".output.dense.",
    }

    # Keep track of the attention/query/value tensor.
    _lowerCAmelCase		     = None

    # Extract the layers.
    for key, val in transformer.items():
        # Match the name.
        _lowerCAmelCase		     = layer_re.match(SCREAMING_SNAKE_CASE_      )

        # Stop if that's not a layer
        if m is None:
            break

        # The index of the layer.
        _lowerCAmelCase		     = int(m.group(1      )      )
        # The name of the operation.
        _lowerCAmelCase		     = m.group(2      )
        # Is it a weight or a bias?
        _lowerCAmelCase		     = m.group(3      )

        # The name of the layer.
        _lowerCAmelCase		     = F'''bert.encoder.layer.{layer_idx}'''

        # For layernorm(s), simply store the layer norm.
        if op_name.endswith("layernorm"      ):
            _lowerCAmelCase		     = "attention.ln" if op_name.startswith("input"      ) else "ln"
            _lowerCAmelCase		     = val

        # Transpose the QKV matrix.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "weight":
            # Make sure the QKV pointer is nil.
            assert attention_qkv_weight is None, ""

            _lowerCAmelCase		     = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    3						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
            # Store the tensor as we need the bias as well to interleave QKV and biases.
            _lowerCAmelCase		     = out_val

        # Transpose the bias.
        elif (
            op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
        ) and weight_or_bias == "bias":
            # Make sure we read the weight tensor.
            assert attention_qkv_weight is not None, ""

            # Split the QKV matrix into Q, K and V. Megatron stores Q,K,V interleaved.
            _lowerCAmelCase		     = attention_qkv_weight[0 * config.hidden_size : 1 * config.hidden_size, :]
            _lowerCAmelCase		     = attention_qkv_weight[1 * config.hidden_size : 2 * config.hidden_size, :]
            _lowerCAmelCase		     = attention_qkv_weight[2 * config.hidden_size : 3 * config.hidden_size, :]

            _lowerCAmelCase		     = fix_query_key_value_ordering(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    3						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )
            # Split the bias.
            _lowerCAmelCase		     = out_val[0 * config.hidden_size : 1 * config.hidden_size]
            _lowerCAmelCase		     = out_val[1 * config.hidden_size : 2 * config.hidden_size]
            _lowerCAmelCase		     = out_val[2 * config.hidden_size : 3 * config.hidden_size]

            # Store.
            _lowerCAmelCase		     = q
            _lowerCAmelCase		     = q_bias
            _lowerCAmelCase		     = k
            _lowerCAmelCase		     = k_bias
            _lowerCAmelCase		     = v
            _lowerCAmelCase		     = v_bias

            # Clear the stored tensor.
            _lowerCAmelCase		     = None

        # Copy weights and biases as is.
        elif weight_or_bias in ["weight", "bias"]:
            _lowerCAmelCase		     = megatron_to_transformers[op_name]
            _lowerCAmelCase		     = val

    # The final layernorm.
    _lowerCAmelCase		     = transformer["final_layernorm.weight"]
    _lowerCAmelCase		     = transformer["final_layernorm.bias"]

    # The pooler.
    _lowerCAmelCase		     = lm["pooler"]

    # Store the matrix and the bias.
    _lowerCAmelCase		     = pooler["dense.weight"]
    _lowerCAmelCase		     = pooler["dense.bias"]

    # The LM head from Megatron (for RACE).
    _lowerCAmelCase		     = model["lm_head"]

    # The transform matrix.
    _lowerCAmelCase		     = lm_head["dense.weight"]
    _lowerCAmelCase		     = lm_head["dense.bias"]

    # The transform LN.
    _lowerCAmelCase		     = lm_head["layernorm.weight"]
    _lowerCAmelCase		     = lm_head["layernorm.bias"]

    # For the decoder, we replicate the weights.
    _lowerCAmelCase		     = word_embeddings
    _lowerCAmelCase		     = lm_head["bias"]

    # The classifier from Megatron (for MLNI).
    _lowerCAmelCase		     = model["binary_head"]

    # Store the classifier.
    _lowerCAmelCase		     = binary_head["weight"]
    _lowerCAmelCase		     = binary_head["bias"]

    # It should be done!
    return output_state_dict


def 		__a():



    '''simple docstring'''





    _lowerCAmelCase		     = argparse.ArgumentParser()
    parser.add_argument("--print-checkpoint-structure"						,    action="store_true"      )
    parser.add_argument("path_to_checkpoint"						,    type=SCREAMING_SNAKE_CASE_						,    help="Path to the ZIP file containing the checkpoint"      )
    parser.add_argument(
        "--config_file"						,    default=""						,    type=SCREAMING_SNAKE_CASE_						,    help="An optional config json file describing the pre-trained model."						,    )
    _lowerCAmelCase		     = parser.parse_args()

    # Extract the basename.
    _lowerCAmelCase		     = os.path.dirname(args.path_to_checkpoint      )

    # Load the model.
    # the .zip is very optional, let's keep it for backward compatibility
    print(F'''Extracting PyTorch state dictionary from "{args.path_to_checkpoint}"'''      )
    if args.path_to_checkpoint.endswith(".zip"      ):
        with zipfile.ZipFile(args.path_to_checkpoint						,    "r"      ) as checkpoint:
            with checkpoint.open("release/mp_rank_00/model_optim_rng.pt"      ) as pytorch_dict:
                _lowerCAmelCase		     = torch.load(SCREAMING_SNAKE_CASE_						,    map_location="cpu"      )
    else:
        _lowerCAmelCase		     = torch.load(args.path_to_checkpoint						,    map_location="cpu"      )

    if args.config_file == "":
        # Default config of megatron-bert 345m
        _lowerCAmelCase		     = MegatronBertConfig()

        # different megatron-bert-*-345m models have different vocab sizes, so override the default
        # config (which is for megatron-bert-cased-345m) with the actual vocab dimension
        _lowerCAmelCase		     = input_state_dict["model"]["lm_head"]["bias"].numel()
    else:
        _lowerCAmelCase		     = MegatronBertConfig.from_json_file(args.config_file      )

    # Convert.
    print("Converting"      )
    _lowerCAmelCase		     = convert_megatron_checkpoint(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )

    # Store the config to file.
    print("Saving config"      )
    config.save_pretrained(SCREAMING_SNAKE_CASE_      )

    # Store the state_dict to file.
    _lowerCAmelCase		     = os.path.join(SCREAMING_SNAKE_CASE_						,    "pytorch_model.bin"      )
    print(F'''Saving checkpoint to "{output_checkpoint_file}"'''      )
    torch.save(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_      )


####################################################################################################

if __name__ == "__main__":
       main()

####################################################################################################


