






"""simple docstring"""




# Copyright (c) 2019 Uber Technologies, Inc.
#
# 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.

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)
UpperCamelCase											=     1e-10
UpperCamelCase											=     """This is incredible! I love it, this is the best chicken I have ever had."""
UpperCamelCase											=     100




class  lowercase_     (nn.Module  ):






      def __init__(     self  ,  a_  ,  a_="gpt2-medium"  ,  a_=False  ,  a_="cpu"						)						->Tuple:







        '''simple docstring'''


        super().__init__()
        _a					      =						GPTaTokenizer.from_pretrained(a_						)
        _a					      =						GPTaLMHeadModel.from_pretrained(a_						)
        _a					      =						self.encoder.transformer.config.hidden_size
        _a					      =						ClassificationHead(class_size=a_  ,  embed_size=self.embed_size						)
        _a					      =						cached_mode
        _a					      =						device






      def       lowerCamelCase__     (     self						)						->Optional[int]:







        '''simple docstring'''


        return self.classifier_head






      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


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






      def       lowerCamelCase__     (     self  ,  a_						)						->int:







        '''simple docstring'''


        _a					      =						x.ne(0						).unsqueeze(2						).repeat(1  ,  1  ,  self.embed_size						).float().to(self.device						).detach()
        _a					      =						self.encoder.transformer(a_						)["last_hidden_state"]
        _a					      =						hidden * mask
        _a					      =						torch.sum(a_  ,  dim=1						) / (torch.sum(a_  ,  dim=1						).detach() + EPSILON)
        return avg_hidden






      def       lowerCamelCase__     (     self  ,  a_						)						->Any:







        '''simple docstring'''


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

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

        return probs







class  lowercase_     (data.Dataset  ):






      def __init__(     self  ,  a_  ,  a_						)						->Tuple:







        '''simple docstring'''


        _a					      =						X
        _a					      =						y






      def __len__(     self						)						->Tuple:







        '''simple docstring'''


        return len(self.X						)






      def __getitem__(     self  ,  a_						)						->int:







        '''simple docstring'''


        _a					      =						{}
        _a					      =						self.X[index]
        _a					      =						self.y[index]
        return data







def 							lowerCAmelCase			(   UpperCamelCase_:				Union[str, Any]       )							->				str:







  '''simple docstring'''







  def pad_sequences(UpperCamelCase_:				List[str]       ):
    _a					      =						[len(UpperCamelCase_       ) for seq in sequences]

    _a					      =						torch.zeros(len(UpperCamelCase_       )  ,			max(UpperCamelCase_       )       ).long()  # padding value = 0

    for i, seq in enumerate(UpperCamelCase_       ):
      _a					      =						lengths[i]
      _a					      =						seq[:end]

    return padded_sequences, lengths

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

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

  return x_batch, y_batch






def 							lowerCAmelCase			(   UpperCamelCase_:				Any       )							->				int:







  '''simple docstring'''







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

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

  return x_batch, y_batch






def 							lowerCAmelCase			(   UpperCamelCase_:				int  ,			UpperCamelCase_:				List[str]  ,			UpperCamelCase_:				str  ,			UpperCamelCase_:				Union[str, Any]=0  ,			UpperCamelCase_:				Optional[Any]=10  ,			UpperCamelCase_:				Tuple="cpu"       )							->				Optional[int]:







  '''simple docstring'''







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

    optimizer.zero_grad()

    _a					      =						discriminator(UpperCamelCase_       )
    _a					      =						nn.functional.nll_loss(UpperCamelCase_  ,			UpperCamelCase_       )
    loss.backward(retain_graph=UpperCamelCase_       )
    optimizer.step()

    samples_so_far += len(UpperCamelCase_       )

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






def 							lowerCAmelCase			(   UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Union[str, Any]  ,			UpperCamelCase_:				str="cpu"       )							->				str:







  '''simple docstring'''







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

  test_loss /= len(data_loader.dataset       )

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






def 							lowerCAmelCase			(   UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				Union[str, Any]  ,			UpperCamelCase_:				Tuple  ,			UpperCamelCase_:				Optional[Any]=False  ,			UpperCamelCase_:				Optional[Any]="cpu"       )							->				int:







  '''simple docstring'''







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

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






def 							lowerCAmelCase			(   UpperCamelCase_:				Optional[int]  ,			UpperCamelCase_:				List[Any]  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				List[str]=False  ,			UpperCamelCase_:				Union[str, Any]="cpu"       )							->				List[Any]:







  '''simple docstring'''







  _a					      =						torch.utils.data.DataLoader(dataset=UpperCamelCase_  ,			batch_size=UpperCamelCase_  ,			collate_fn=UpperCamelCase_       )

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

  _a					      =						torch.utils.data.DataLoader(
      dataset=Dataset(UpperCamelCase_  ,			UpperCamelCase_       )  ,			batch_size=UpperCamelCase_  ,			shuffle=UpperCamelCase_  ,			collate_fn=UpperCamelCase_       )

  return data_loader






def 							lowerCAmelCase			(   UpperCamelCase_:				Any  ,			UpperCamelCase_:				List[str]=None  ,			UpperCamelCase_:				Tuple="gpt2-medium"  ,			UpperCamelCase_:				Union[str, Any]=10  ,			UpperCamelCase_:				Optional[int]=64  ,			UpperCamelCase_:				List[Any]=10  ,			UpperCamelCase_:				Optional[Any]=False  ,			UpperCamelCase_:				int=False  ,			UpperCamelCase_:				Optional[Any]=False  ,			)							->				int:







  '''simple docstring'''







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

  print("Preprocessing {} dataset...".format(UpperCamelCase_       )       )
  _a					      =						time.time()

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

    _a					      =						Discriminator(
        class_size=len(UpperCamelCase_       )  ,			pretrained_model=UpperCamelCase_  ,			cached_mode=UpperCamelCase_  ,			device=UpperCamelCase_       ).to(UpperCamelCase_       )

    _a					      =						torchtext_data.Field()
    _a					      =						torchtext_data.Field(sequential=UpperCamelCase_       )
    _a      ,						_a      ,						_a					      =						datasets.SST.splits(
        UpperCamelCase_  ,			UpperCamelCase_  ,			fine_grained=UpperCamelCase_  ,			train_subtrees=UpperCamelCase_  ,			)

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

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

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

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

    _a					      =						Discriminator(
        class_size=len(UpperCamelCase_       )  ,			pretrained_model=UpperCamelCase_  ,			cached_mode=UpperCamelCase_  ,			device=UpperCamelCase_       ).to(UpperCamelCase_       )

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

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

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

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

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

    _a					      =						Discriminator(
        class_size=len(UpperCamelCase_       )  ,			pretrained_model=UpperCamelCase_  ,			cached_mode=UpperCamelCase_  ,			device=UpperCamelCase_       ).to(UpperCamelCase_       )

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

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

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

    _a					      =						{
        "class_size": len(UpperCamelCase_       ),
        "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."       )

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

    _a					      =						sorted(UpperCamelCase_       )
    _a					      =						{c: i for i, c in enumerate(UpperCamelCase_       )}

    _a					      =						Discriminator(
        class_size=len(UpperCamelCase_       )  ,			pretrained_model=UpperCamelCase_  ,			cached_mode=UpperCamelCase_  ,			device=UpperCamelCase_       ).to(UpperCamelCase_       )

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

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

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

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

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

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

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

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

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

    _a					      =						time.time()

    _a					      =						get_cached_data_loader(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			shuffle=UpperCamelCase_  ,			device=UpperCamelCase_       )

    _a					      =						get_cached_data_loader(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_  ,			device=UpperCamelCase_       )

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

  else:
    _a					      =						torch.utils.data.DataLoader(
        dataset=UpperCamelCase_  ,			batch_size=UpperCamelCase_  ,			shuffle=UpperCamelCase_  ,			collate_fn=UpperCamelCase_       )
    _a					      =						torch.utils.data.DataLoader(dataset=UpperCamelCase_  ,			batch_size=UpperCamelCase_  ,			collate_fn=UpperCamelCase_       )

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

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

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

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

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

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

    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(UpperCamelCase_  ,			epoch + 1       )  ,			)


if __name__ == "__main__":
     UpperCamelCase											=     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""")
     UpperCamelCase											=     parser.parse_args()

     train_discriminator(**(vars(args)))










"""simple docstring"""




import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 							lowerCAmelCase			(   UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Dict  ,			UpperCamelCase_:				Any       )							->				Tuple:







  '''simple docstring'''







  _a					      =						{"small": (512, 8, 24), "medium": (1024, 8, 36), "large": (1536, 16, 48)}
  _a      ,						_a      ,						_a					      =						MODELS[model_size]  # set model hyperparameters
  _a					      =						ImageGPTConfig(n_embd=UpperCamelCase_  ,			n_layer=UpperCamelCase_  ,			n_head=UpperCamelCase_       )
  _a					      =						ImageGPTForCausalLM(UpperCamelCase_       )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(UpperCamelCase_  ,			UpperCamelCase_  ,			UpperCamelCase_       )

  # Save pytorch-model
  _a					      =						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
  _a					      =						pytorch_dump_folder_path + "/" + CONFIG_NAME
  print(f'''Save PyTorch model to {pytorch_weights_dump_path}'''       )
  torch.save(model.state_dict()  ,			UpperCamelCase_       )
  print(f'''Save configuration file to {pytorch_config_dump_path}'''       )
  with open(UpperCamelCase_  ,			"w"  ,			encoding="utf-8"       ) as f:
    f.write(config.to_json_string()       )


if __name__ == "__main__":
     UpperCamelCase											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--imagegpt_checkpoint_path""",
         default=None,
         type=str,
         required=True,
         help="""Path to the TensorFlow checkpoint path.""",
     )
     parser.add_argument(
         """--model_size""",
         default=None,
         type=str,
         required=True,
         help="""Size of the model (can be either 'small', 'medium' or 'large').""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     UpperCamelCase											=     parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )


