





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)
A_        =  1e-10
A_        =  'This is incredible! I love it, this is the best chicken I have ever had.'
A_        =  1_0_0

class SCREAMING_SNAKE_CASE_			(       nn.Module ):





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase="gpt2-medium"						,	_lowerCAmelCase=False						,	_lowerCAmelCase="cpu"						):
										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        __magic_name__			(			self						):
										return self.classifier_head





					def        __magic_name__			(			self						):





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





					def        __magic_name__			(			self						,	_lowerCAmelCase						):
										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        __magic_name__			(			self						,	_lowerCAmelCase						):





										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 SCREAMING_SNAKE_CASE_			(       data.Dataset ):





					"""simple docstring"""


					def __init__(			self						,	_lowerCAmelCase						,	_lowerCAmelCase						):
										lowerCamelCase__      =							X
										lowerCamelCase__      =							y





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






					def __getitem__(			self						,	_lowerCAmelCase						):
										lowerCamelCase__      =							{}
										lowerCamelCase__      =							self.X[index]
										lowerCamelCase__      =							self.y[index]
										return data





def  __UpperCamelCase   (     a)			->Union[str, Any]:

					def pad_sequences(a):
										lowerCamelCase__      =							[len(__SCREAMING_SNAKE_CASE) for seq in sequences]

										lowerCamelCase__      =							torch.zeros(len(__SCREAMING_SNAKE_CASE),  max(__SCREAMING_SNAKE_CASE)).long()  # padding value = 0

										for i, seq in enumerate(__SCREAMING_SNAKE_CASE):
															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  __UpperCamelCase   (     a)			->str:
					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  __UpperCamelCase   (     a,  a,  a,  a=0,  a=10,  a="cpu")			->List[str]:
					lowerCamelCase__      =							0
					discriminator.train_custom()
					for batch_idx, (input_t, target_t) in enumerate(__SCREAMING_SNAKE_CASE):
										lowerCamelCase__      =							input_t.to(__SCREAMING_SNAKE_CASE), target_t.to(__SCREAMING_SNAKE_CASE)

										optimizer.zero_grad()

										lowerCamelCase__      =							discriminator(__SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							nn.functional.nll_loss(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE)
										loss.backward(retain_graph=__SCREAMING_SNAKE_CASE)
										optimizer.step()

										samples_so_far += len(__SCREAMING_SNAKE_CASE)

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





def  __UpperCamelCase   (     a,  a,  a="cpu")			->List[str]:
					discriminator.eval()
					lowerCamelCase__      =							0
					lowerCamelCase__      =							0
					with torch.no_grad():
										for input_t, target_t in data_loader:
															lowerCamelCase__      =							input_t.to(__SCREAMING_SNAKE_CASE), target_t.to(__SCREAMING_SNAKE_CASE)
															lowerCamelCase__      =							discriminator(__SCREAMING_SNAKE_CASE)
															# sum up batch loss
															test_loss += nn.functional.nll_loss(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  reduction="sum").item()
															# get the index of the max log-probability
															lowerCamelCase__      =							output_t.argmax(dim=1,  keepdim=__SCREAMING_SNAKE_CASE)
															correct += pred_t.eq(target_t.view_as(__SCREAMING_SNAKE_CASE)).sum().item()

					test_loss /= len(data_loader.dataset)

					print(
					    "Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)".format(
					        __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  len(data_loader.dataset),  1_0_0.0 * correct / len(data_loader.dataset)))





def  __UpperCamelCase   (     a,  a,  a,  a=False,  a="cpu")			->List[str]:
					lowerCamelCase__      =							model.tokenizer.encode(__SCREAMING_SNAKE_CASE)
					lowerCamelCase__      =							torch.tensor([input_t],  dtype=torch.long,  device=__SCREAMING_SNAKE_CASE)
					if cached:
										lowerCamelCase__      =							model.avg_representation(__SCREAMING_SNAKE_CASE)

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





def  __UpperCamelCase   (     a,  a,  a,  a=False,  a="cpu")			->Union[str, Any]:
					lowerCamelCase__      =							torch.utils.data.DataLoader(dataset=__SCREAMING_SNAKE_CASE,  batch_size=__SCREAMING_SNAKE_CASE,  collate_fn=__SCREAMING_SNAKE_CASE)

					lowerCamelCase__      =							[]
					lowerCamelCase__      =							[]
					for batch_idx, (x, y) in enumerate(tqdm(__SCREAMING_SNAKE_CASE,  ascii=__SCREAMING_SNAKE_CASE)):
										with torch.no_grad():
															lowerCamelCase__      =							x.to(__SCREAMING_SNAKE_CASE)
															lowerCamelCase__      =							discriminator.avg_representation(__SCREAMING_SNAKE_CASE).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(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE),  batch_size=__SCREAMING_SNAKE_CASE,  shuffle=__SCREAMING_SNAKE_CASE,  collate_fn=__SCREAMING_SNAKE_CASE)

					return data_loader





def  __UpperCamelCase   (     a,  a=None,  a="gpt2-medium",  a=10,  a=64,  a=10,  a=False,  a=False,  a=False,  )			->Any:
					lowerCamelCase__      =							"""cuda""" if torch.cuda.is_available() and not no_cuda else """cpu"""

					print("Preprocessing {} dataset...".format(__SCREAMING_SNAKE_CASE))
					lowerCamelCase__      =							time.time()

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

										lowerCamelCase__      =							Discriminator(
										    class_size=len(__SCREAMING_SNAKE_CASE),  pretrained_model=__SCREAMING_SNAKE_CASE,  cached_mode=__SCREAMING_SNAKE_CASE,  device=__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE)

										lowerCamelCase__      =							torchtext_data.Field()
										lowerCamelCase__      =							torchtext_data.Field(sequential=__SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							datasets.SST.splits(
										    __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  fine_grained=__SCREAMING_SNAKE_CASE,  train_subtrees=__SCREAMING_SNAKE_CASE,  )

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

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

										lowerCamelCase__      =							{
										    """class_size""": len(__SCREAMING_SNAKE_CASE),
										    """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(__SCREAMING_SNAKE_CASE)}

										lowerCamelCase__      =							Discriminator(
										    class_size=len(__SCREAMING_SNAKE_CASE),  pretrained_model=__SCREAMING_SNAKE_CASE,  cached_mode=__SCREAMING_SNAKE_CASE,  device=__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE)

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

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

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

										lowerCamelCase__      =							{
										    """class_size""": len(__SCREAMING_SNAKE_CASE),
										    """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(__SCREAMING_SNAKE_CASE)}

										lowerCamelCase__      =							Discriminator(
										    class_size=len(__SCREAMING_SNAKE_CASE),  pretrained_model=__SCREAMING_SNAKE_CASE,  cached_mode=__SCREAMING_SNAKE_CASE,  device=__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE)

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

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

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

										lowerCamelCase__      =							{
										    """class_size""": len(__SCREAMING_SNAKE_CASE),
										    """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(__SCREAMING_SNAKE_CASE) as f:
															lowerCamelCase__      =							csv.reader(__SCREAMING_SNAKE_CASE,  delimiter="\t")
															for row in tqdm(__SCREAMING_SNAKE_CASE,  ascii=__SCREAMING_SNAKE_CASE):
																				if row:
																									classes.add(row[0])

										lowerCamelCase__      =							sorted(__SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							{c: i for i, c in enumerate(__SCREAMING_SNAKE_CASE)}

										lowerCamelCase__      =							Discriminator(
										    class_size=len(__SCREAMING_SNAKE_CASE),  pretrained_model=__SCREAMING_SNAKE_CASE,  cached_mode=__SCREAMING_SNAKE_CASE,  device=__SCREAMING_SNAKE_CASE).to(__SCREAMING_SNAKE_CASE)

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

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

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

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

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

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

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

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

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

										lowerCamelCase__      =							time.time()

										lowerCamelCase__      =							get_cached_data_loader(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  shuffle=__SCREAMING_SNAKE_CASE,  device=__SCREAMING_SNAKE_CASE)

										lowerCamelCase__      =							get_cached_data_loader(__SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  __SCREAMING_SNAKE_CASE,  device=__SCREAMING_SNAKE_CASE)

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

					else:
										lowerCamelCase__      =							torch.utils.data.DataLoader(
										    dataset=__SCREAMING_SNAKE_CASE,  batch_size=__SCREAMING_SNAKE_CASE,  shuffle=__SCREAMING_SNAKE_CASE,  collate_fn=__SCREAMING_SNAKE_CASE)
										lowerCamelCase__      =							torch.utils.data.DataLoader(dataset=__SCREAMING_SNAKE_CASE,  batch_size=__SCREAMING_SNAKE_CASE,  collate_fn=__SCREAMING_SNAKE_CASE)

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

					lowerCamelCase__      =							optim.Adam(discriminator.parameters(),  lr=0.0_0_0_1)

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

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

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

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

										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(__SCREAMING_SNAKE_CASE,  epoch + 1),  )


if __name__ == "__main__":
						A_        =  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=1_0, metavar="N", help="Number of training epochs")
						parser.add_argument(
						    "--batch_size", type=int, default=6_4, metavar="N", help="input batch size for training (default: 64)"
						)
						parser.add_argument(
						    "--log_interval",
						    type=int,
						    default=1_0,
						    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")
						A_        =  parser.parse_args()

						train_discriminator(**(vars(args)))



'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



