




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)
lowercase_      :							Optional[int]    							=					1E-1_0
lowercase_      :							Union[str, Any]    							=					'This is incredible! I love it, this is the best chicken I have ever had.'
lowercase_      :							Any    							=					1_00






class        __lowerCAmelCase					(			nn.Module					):







			def __init__(			self       :     List[Any]       ,       snake_case__       :     List[Any]       ,       snake_case__       :     str="gpt2-medium"       ,       snake_case__       :     str=False       ,       snake_case__       :     Any="cpu" ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									return self.classifier_head







			def UpperCamelCase     (			self       :     Union[str, Any] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Union[str, Any]       ,       snake_case__       :     List[Any] ):




									"""simple docstring"""


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







			def UpperCamelCase     (			self       :     Any       ,       snake_case__       :     Optional[int] ):




									"""simple docstring"""


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

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

									return probs






class        __lowerCAmelCase					(			data.Dataset					):







			def __init__(			self       :     Union[str, Any]       ,       snake_case__       :     List[str]       ,       snake_case__       :     Union[str, Any] ):




									"""simple docstring"""


									_UpperCAmelCase						=	X
									_UpperCAmelCase						=	y







			def __len__(			self       :     int ):




									"""simple docstring"""


									return len(self.X )







			def __getitem__(			self       :     List[str]       ,       snake_case__       :     Union[str, Any] ):




									"""simple docstring"""


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




def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


						def pad_sequences(snake_case_		):
												_UpperCAmelCase						=	[len(snake_case_		) for seq in sequences]

												_UpperCAmelCase						=	torch.zeros(len(snake_case_		)      ,       max(snake_case_		)		).long()  # padding value = 0

												for i, seq in enumerate(snake_case_		):
																		_UpperCAmelCase						=	lengths[i]
																		_UpperCAmelCase						=	seq[:end]

												return padded_sequences, lengths

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

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

						return x_batch, y_batch


def 				__SCREAMING_SNAKE_CASE    (     snake_case_		):

						'''simple docstring'''


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

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

						return x_batch, y_batch


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_=0      ,       snake_case_=10      ,       snake_case_="cpu"		):

						'''simple docstring'''


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

												optimizer.zero_grad()

												_UpperCAmelCase						=	discriminator(snake_case_		)
												_UpperCAmelCase						=	nn.functional.nll_loss(snake_case_      ,       snake_case_		)
												loss.backward(retain_graph=snake_case_		)
												optimizer.step()

												samples_so_far += len(snake_case_		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_="cpu"		):

						'''simple docstring'''


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

						test_loss /= len(data_loader.dataset		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_=False      ,       snake_case_="cpu"		):

						'''simple docstring'''


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

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_=False      ,       snake_case_="cpu"		):

						'''simple docstring'''


						_UpperCAmelCase						=	torch.utils.data.DataLoader(dataset=snake_case_      ,       batch_size=snake_case_      ,       collate_fn=snake_case_		)

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

						_UpperCAmelCase						=	torch.utils.data.DataLoader(
						    dataset=Dataset(snake_case_      ,       snake_case_		)      ,       batch_size=snake_case_      ,       shuffle=snake_case_      ,       collate_fn=snake_case_		)

						return data_loader


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_=None      ,       snake_case_="gpt2-medium"      ,       snake_case_=10      ,       snake_case_=64      ,       snake_case_=10      ,       snake_case_=False      ,       snake_case_=False      ,       snake_case_=False      ,       ):

						'''simple docstring'''


						_UpperCAmelCase						=	'cuda' if torch.cuda.is_available() and not no_cuda else 'cpu'

						print("Preprocessing {} dataset...".format(snake_case_		)		)
						_UpperCAmelCase						=	time.time()

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

												_UpperCAmelCase						=	Discriminator(
												    class_size=len(snake_case_		)      ,       pretrained_model=snake_case_      ,       cached_mode=snake_case_      ,       device=snake_case_		).to(snake_case_		)

												_UpperCAmelCase						=	torchtext_data.Field()
												_UpperCAmelCase						=	torchtext_data.Field(sequential=snake_case_		)
												_UpperCAmelCase						=	datasets.SST.splits(
												    snake_case_      ,       snake_case_      ,       fine_grained=snake_case_      ,       train_subtrees=snake_case_      ,       )

												_UpperCAmelCase						=	[]
												_UpperCAmelCase						=	[]
												for i in trange(len(snake_case_		)      ,       ascii=snake_case_		):
																		_UpperCAmelCase						=	TreebankWordDetokenizer().detokenize(vars(train_data[i]		)["text"]		)
																		_UpperCAmelCase						=	discriminator.tokenizer.encode(snake_case_		)
																		_UpperCAmelCase						=	torch.tensor([5_0256] + seq      ,       device=snake_case_      ,       dtype=torch.long		)
																		x.append(snake_case_		)
																		y.append(classaidx[vars(train_data[i]		)["label"]]		)
												_UpperCAmelCase						=	Dataset(snake_case_      ,       snake_case_		)

												_UpperCAmelCase						=	[]
												_UpperCAmelCase						=	[]
												for i in trange(len(snake_case_		)      ,       ascii=snake_case_		):
																		_UpperCAmelCase						=	TreebankWordDetokenizer().detokenize(vars(test_data[i]		)["text"]		)
																		_UpperCAmelCase						=	discriminator.tokenizer.encode(snake_case_		)
																		_UpperCAmelCase						=	torch.tensor([5_0256] + seq      ,       device=snake_case_      ,       dtype=torch.long		)
																		test_x.append(snake_case_		)
																		test_y.append(classaidx[vars(test_data[i]		)["label"]]		)
												_UpperCAmelCase						=	Dataset(snake_case_      ,       snake_case_		)

												_UpperCAmelCase						=	{
												    'class_size': len(snake_case_		),
												    'embed_size': discriminator.embed_size,
												    'pretrained_model': pretrained_model,
												    'class_vocab': classaidx,
												    'default_class': 2,
												}

						elif dataset == "clickbait":
												_UpperCAmelCase						=	['non_clickbait', 'clickbait']
												_UpperCAmelCase						=	{c: i for i, c in enumerate(snake_case_		)}

												_UpperCAmelCase						=	Discriminator(
												    class_size=len(snake_case_		)      ,       pretrained_model=snake_case_      ,       cached_mode=snake_case_      ,       device=snake_case_		).to(snake_case_		)

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

																														if len(snake_case_		) < max_length_seq:
																																				_UpperCAmelCase						=	torch.tensor([5_0256] + seq      ,       device=snake_case_      ,       dtype=torch.long		)
																														else:
																																				print("Line {} is longer than maximum length {}".format(snake_case_      ,       snake_case_		)		)
																																				continue
																														x.append(snake_case_		)
																														y.append(d["label"]		)
																								except Exception:
																														print("Error evaluating / tokenizing line {}, skipping it".format(snake_case_		)		)
																														pass

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

												_UpperCAmelCase						=	{
												    'class_size': len(snake_case_		),
												    'embed_size': discriminator.embed_size,
												    'pretrained_model': pretrained_model,
												    'class_vocab': classaidx,
												    'default_class': 1,
												}

						elif dataset == "toxic":
												_UpperCAmelCase						=	['non_toxic', 'toxic']
												_UpperCAmelCase						=	{c: i for i, c in enumerate(snake_case_		)}

												_UpperCAmelCase						=	Discriminator(
												    class_size=len(snake_case_		)      ,       pretrained_model=snake_case_      ,       cached_mode=snake_case_      ,       device=snake_case_		).to(snake_case_		)

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

																														if len(snake_case_		) < max_length_seq:
																																				_UpperCAmelCase						=	torch.tensor([5_0256] + seq      ,       device=snake_case_      ,       dtype=torch.long		)
																														else:
																																				print("Line {} is longer than maximum length {}".format(snake_case_      ,       snake_case_		)		)
																																				continue
																														x.append(snake_case_		)
																														y.append(int(np.sum(d["label"]		) > 0		)		)
																								except Exception:
																														print("Error evaluating / tokenizing line {}, skipping it".format(snake_case_		)		)
																														pass

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

												_UpperCAmelCase						=	{
												    'class_size': len(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."		)

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

												_UpperCAmelCase						=	sorted(snake_case_		)
												_UpperCAmelCase						=	{c: i for i, c in enumerate(snake_case_		)}

												_UpperCAmelCase						=	Discriminator(
												    class_size=len(snake_case_		)      ,       pretrained_model=snake_case_      ,       cached_mode=snake_case_      ,       device=snake_case_		).to(snake_case_		)

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

																														try:
																																				_UpperCAmelCase						=	discriminator.tokenizer.encode(snake_case_		)
																																				if len(snake_case_		) < max_length_seq:
																																										_UpperCAmelCase						=	torch.tensor([5_0256] + seq      ,       device=snake_case_      ,       dtype=torch.long		)

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

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

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

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

												_UpperCAmelCase						=	{
												    'class_size': len(snake_case_		),
												    'embed_size': discriminator.embed_size,
												    'pretrained_model': pretrained_model,
												    'class_vocab': classaidx,
												    'default_class': 0,
												}

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

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

												_UpperCAmelCase						=	time.time()

												_UpperCAmelCase						=	get_cached_data_loader(snake_case_      ,       snake_case_      ,       snake_case_      ,       shuffle=snake_case_      ,       device=snake_case_		)

												_UpperCAmelCase						=	get_cached_data_loader(snake_case_      ,       snake_case_      ,       snake_case_      ,       device=snake_case_		)

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

						else:
												_UpperCAmelCase						=	torch.utils.data.DataLoader(
												    dataset=snake_case_      ,       batch_size=snake_case_      ,       shuffle=snake_case_      ,       collate_fn=snake_case_		)
												_UpperCAmelCase						=	torch.utils.data.DataLoader(dataset=snake_case_      ,       batch_size=snake_case_      ,       collate_fn=snake_case_		)

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

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

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

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

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

												print("\nExample prediction"		)
												predict(snake_case_      ,       snake_case_      ,       snake_case_      ,       cached=snake_case_      ,       device=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(snake_case_      ,       epoch + 1		)      ,       )


if __name__ == "__main__":
				lowercase_      :							int    							=					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')
				lowercase_      :							List[str]    							=					parser.parse_args()

				train_discriminator(**(vars(args)))







'''simple docstring'''


import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


lowerCAmelCase__      				=			[nn.LayerNorm]

lowerCAmelCase__      				=			logging.get_logger(__name__)

lowerCAmelCase__      				=			version.parse(version.parse(torch.__version__).base_version)

lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("2.0")
lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("1.12")
lowerCAmelCase__      				=			parsed_torch_version_base >= version.parse("1.11")
lowerCAmelCase__      				=			parsed_torch_version_base < version.parse("1.11")







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,self	)     ->     List[Any]:
	from torch import _softmax_backward_data

	if is_torch_less_than_1_11:
		return _softmax_backward_data(UpperCamelCase ,UpperCamelCase ,parent.dim ,self	)
	else:
		return _softmax_backward_data(UpperCamelCase ,UpperCamelCase ,parent.dim ,self.dtype	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 0	)     ->     nn.Linear:
	UpperCAmelCase_		:   Tuple													=       index.to(layer.weight.device	)
	UpperCAmelCase_		:   Tuple													=       layer.weight.index_select(UpperCamelCase ,UpperCamelCase	).clone().detach()
	if layer.bias is not None:
		if dim == 1:
			UpperCAmelCase_		:   List[Any]													=       layer.bias.clone().detach()
		else:
			UpperCAmelCase_		:   Tuple													=       layer.bias[index].clone().detach()
	UpperCAmelCase_		:   Optional[Any]													=       list(layer.weight.size()	)
	UpperCAmelCase_		:   int													=       len(UpperCamelCase	)
	UpperCAmelCase_		:   str													=       nn.Linear(new_size[1] ,new_size[0] ,bias=layer.bias is not None	).to(layer.weight.device	)
	UpperCAmelCase_		:   str													=       False
	new_layer.weight.copy_(W.contiguous()	)
	UpperCAmelCase_		:   Tuple													=       True
	if layer.bias is not None:
		UpperCAmelCase_		:   Union[str, Any]													=       False
		new_layer.bias.copy_(b.contiguous()	)
		UpperCAmelCase_		:   Any													=       True
	return new_layer



class        lowercase    (	nn.Module			):


							def __init__(      self     ,      _snake_case     ,      _snake_case)				->					int:
								super().__init__()
								UpperCAmelCase_		:   List[Any]													=       nf
								UpperCAmelCase_		:   List[Any]													=       nn.Parameter(torch.empty(_snake_case     ,      _snake_case))
								UpperCAmelCase_		:   List[Any]													=       nn.Parameter(torch.zeros(_snake_case))
								nn.init.normal_(self.weight     ,      std=0.02)





							def     _snake_case				(      self     ,      _snake_case)				->					Optional[int]:
								UpperCAmelCase_		:   Dict													=       x.size()[:-1] + (self.nf,)
								UpperCAmelCase_		:   Optional[Any]													=       torch.addmm(self.bias     ,      x.view(-1     ,      x.size(-1))     ,      self.weight)
								UpperCAmelCase_		:   List[str]													=       x.view(_snake_case)
								return x







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = 1	)     ->     ConvaD:
	UpperCAmelCase_		:   List[str]													=       index.to(layer.weight.device	)
	UpperCAmelCase_		:   Optional[Any]													=       layer.weight.index_select(UpperCamelCase ,UpperCamelCase	).clone().detach()
	if dim == 0:
		UpperCAmelCase_		:   Tuple													=       layer.bias.clone().detach()
	else:
		UpperCAmelCase_		:   str													=       layer.bias[index].clone().detach()
	UpperCAmelCase_		:   str													=       list(layer.weight.size()	)
	UpperCAmelCase_		:   List[str]													=       len(UpperCamelCase	)
	UpperCAmelCase_		:   List[Any]													=       ConvaD(new_size[1] ,new_size[0]	).to(layer.weight.device	)
	UpperCAmelCase_		:   Tuple													=       False
	new_layer.weight.copy_(W.contiguous()	)
	UpperCAmelCase_		:   Optional[Any]													=       True
	UpperCAmelCase_		:   Any													=       False
	new_layer.bias.copy_(b.contiguous()	)
	UpperCAmelCase_		:   Union[str, Any]													=       True
	return new_layer







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None	)     ->     Union[nn.Linear, ConvaD]:
	if isinstance(UpperCamelCase ,nn.Linear	):
		return prune_linear_layer(UpperCamelCase ,UpperCamelCase ,dim=0 if dim is None else dim	)
	elif isinstance(UpperCamelCase ,UpperCamelCase	):
		return prune_convad_layer(UpperCamelCase ,UpperCamelCase ,dim=1 if dim is None else dim	)
	else:
		raise ValueError(f"""Can't prune layer of class {layer.__class__}"""	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,*UpperCamelCase	)     ->     torch.Tensor:
	assert len(UpperCamelCase	) > 0, f"""{input_tensors} has to be a tuple/list of tensors"""

	# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
	UpperCAmelCase_		:   str													=       len(inspect.signature(UpperCamelCase	).parameters	)
	if num_args_in_forward_chunk_fn != len(UpperCamelCase	):
		raise ValueError(
		    f"""forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(UpperCamelCase	)} input """
		    'tensors are given'	)

	if chunk_size > 0:
		UpperCAmelCase_		:   Optional[int]													=       input_tensors[0].shape[chunk_dim]
		for input_tensor in input_tensors:
			if input_tensor.shape[chunk_dim] != tensor_shape:
				raise ValueError(
				    f"""All input tenors have to be of the same shape: {tensor_shape}, """
				    f"""found shape {input_tensor.shape[chunk_dim]}"""	)

		if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
			raise ValueError(
			    f"""The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk """
			    f"""size {chunk_size}"""	)

		UpperCAmelCase_		:   Any													=       input_tensors[0].shape[chunk_dim] // chunk_size

		# chunk input tensor into tuples
		UpperCAmelCase_		:   int													=       tuple(input_tensor.chunk(UpperCamelCase ,dim=UpperCamelCase	) for input_tensor in input_tensors	)
		# apply forward fn to every tuple
		UpperCAmelCase_		:   List[str]													=       tuple(forward_fn(*UpperCamelCase	) for input_tensors_chunk in zip(*UpperCamelCase	)	)
		# concatenate output at same dimension
		return torch.cat(UpperCamelCase ,dim=UpperCamelCase	)

	return forward_fn(*UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase	)     ->     Tuple[Set[int], torch.LongTensor]:
	UpperCAmelCase_		:   List[Any]													=       torch.ones(UpperCamelCase ,UpperCamelCase	)
	UpperCAmelCase_		:   Optional[int]													=       set(UpperCamelCase	) - already_pruned_heads  # Convert to set and remove already pruned heads
	for head in heads:
		# Compute how many pruned heads are before the head and move the index accordingly
		UpperCAmelCase_		:   Any													=       head - sum(1 if h < head else 0 for h in already_pruned_heads	)
		UpperCAmelCase_		:   List[Any]													=       0
	UpperCAmelCase_		:   str													=       mask.view(-1	).contiguous().eq(1	)
	UpperCAmelCase_		:   torch.LongTensor													=       torch.arange(len(UpperCamelCase	)	)[mask].long()
	return heads, index







def     SCREAMING_SNAKE_CASE(	*UpperCamelCase ,UpperCamelCase = None	)     ->     Tuple[torch.Tensor, ...]:
	return torch.meshgrid(*UpperCamelCase ,indexing=UpperCamelCase	)







def     SCREAMING_SNAKE_CASE(	UpperCamelCase	)     ->     Tuple[torch.device, int, int]:
	return tensor.device, storage_ptr(UpperCamelCase	), storage_size(UpperCamelCase	)






