


'''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					:			Optional[Any]				     =					1E-10
_UpperCAmelCase					:			Dict				     =					"""This is incredible! I love it, this is the best chicken I have ever had."""
_UpperCAmelCase					:			str				     =					1_0_0


class    a__					(			nn.Module   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase="gpt2-medium"							,      __lowercase=False							,      __lowercase="cpu"		):
										super().__init__()
										__lowerCAmelCase											=					GPTaTokenizer.from_pretrained(__lowercase		)
										__lowerCAmelCase											=					GPTaLMHeadModel.from_pretrained(__lowercase		)
										__lowerCAmelCase											=					self.encoder.transformer.config.hidden_size
										__lowerCAmelCase											=					ClassificationHead(class_size=__lowercase							,      embed_size=self.embed_size		)
										__lowerCAmelCase											=					cached_mode
										__lowerCAmelCase											=					device




				def        _snake_case	(self		):
										return self.classifier_head




				def        _snake_case	(self		):
										for param in self.encoder.parameters():
																__lowerCAmelCase											=					False
										self.classifier_head.train()




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




				def        _snake_case	(self							,      __lowercase		):
										if self.cached_mode:
																__lowerCAmelCase											=					x.to(self.device		)
										else:
																__lowerCAmelCase											=					self.avg_representation(x.to(self.device		)		)

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

										return probs


class    a__					(			data.Dataset   ):







				"""simple docstring"""



				def __init__(self							,      __lowercase							,      __lowercase		):
										__lowerCAmelCase											=					X
										__lowerCAmelCase											=					y




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




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

def 				__magic_name__(    lowerCamelCase):
						def pad_sequences(lowerCamelCase):
												__lowerCAmelCase											=					[len(lowerCamelCase) for seq in sequences]

												__lowerCAmelCase											=					torch.zeros(len(lowerCamelCase),					max(lowerCamelCase)).long()  # padding value = 0

												for i, seq in enumerate(lowerCamelCase):
																		__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							,       __lowerCAmelCase											=					pad_sequences(item_info['''X'''])
						__lowerCAmelCase											=					torch.tensor(item_info['''y'''],					dtype=torch.long)

						return x_batch, y_batch

def 				__magic_name__(    lowerCamelCase):
						__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 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase=0,					lowerCamelCase=1_0,					lowerCamelCase="cpu"):
						__lowerCAmelCase											=					0
						discriminator.train_custom()
						for batch_idx, (input_t, target_t) in enumerate(lowerCamelCase):
												__lowerCAmelCase							,       __lowerCAmelCase											=					input_t.to(lowerCamelCase), target_t.to(lowerCamelCase)

												optimizer.zero_grad()

												__lowerCAmelCase											=					discriminator(lowerCamelCase)
												__lowerCAmelCase											=					nn.functional.nll_loss(lowerCamelCase,					lowerCamelCase)
												loss.backward(retain_graph=lowerCamelCase)
												optimizer.step()

												samples_so_far += len(lowerCamelCase)

												if batch_idx % log_interval == 0:
																		print(
																		    '''Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'''.format(
																		        epoch + 1,					lowerCamelCase,					len(data_loader.dataset),					1_0_0 * samples_so_far / len(data_loader.dataset),					loss.item(),					))

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

						test_loss /= len(data_loader.dataset)

						print(
						    '''Performance on test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)'''.format(
						        lowerCamelCase,					lowerCamelCase,					len(data_loader.dataset),					1_00.0 * correct / len(data_loader.dataset)))

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase=False,					lowerCamelCase="cpu"):
						__lowerCAmelCase											=					model.tokenizer.encode(lowerCamelCase)
						__lowerCAmelCase											=					torch.tensor([input_t],					dtype=torch.long,					device=lowerCamelCase)
						if cached:
												__lowerCAmelCase											=					model.avg_representation(lowerCamelCase)

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

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					lowerCamelCase=False,					lowerCamelCase="cpu"):
						__lowerCAmelCase											=					torch.utils.data.DataLoader(dataset=lowerCamelCase,					batch_size=lowerCamelCase,					collate_fn=lowerCamelCase)

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

						return data_loader

def 				__magic_name__(    lowerCamelCase,					lowerCamelCase=None,					lowerCamelCase="gpt2-medium",					lowerCamelCase=1_0,					lowerCamelCase=6_4,					lowerCamelCase=1_0,					lowerCamelCase=False,					lowerCamelCase=False,					lowerCamelCase=False,					):
						__lowerCAmelCase											=					'''cuda''' if torch.cuda.is_available() and not no_cuda else '''cpu'''

						print('''Preprocessing {} dataset...'''.format(lowerCamelCase))
						__lowerCAmelCase											=					time.time()

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

												__lowerCAmelCase											=					Discriminator(
												    class_size=len(lowerCamelCase),					pretrained_model=lowerCamelCase,					cached_mode=lowerCamelCase,					device=lowerCamelCase).to(lowerCamelCase)

												__lowerCAmelCase											=					torchtext_data.Field()
												__lowerCAmelCase											=					torchtext_data.Field(sequential=lowerCamelCase)
												__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase											=					datasets.SST.splits(
												    lowerCamelCase,					lowerCamelCase,					fine_grained=lowerCamelCase,					train_subtrees=lowerCamelCase,					)

												__lowerCAmelCase											=					[]
												__lowerCAmelCase											=					[]
												for i in trange(len(lowerCamelCase),					ascii=lowerCamelCase):
																		__lowerCAmelCase											=					TreebankWordDetokenizer().detokenize(vars(train_data[i])['''text'''])
																		__lowerCAmelCase											=					discriminator.tokenizer.encode(lowerCamelCase)
																		__lowerCAmelCase											=					torch.tensor([5_0_2_5_6] + seq,					device=lowerCamelCase,					dtype=torch.long)
																		x.append(lowerCamelCase)
																		y.append(classaidx[vars(train_data[i])['''label''']])
												__lowerCAmelCase											=					Dataset(lowerCamelCase,					lowerCamelCase)

												__lowerCAmelCase											=					[]
												__lowerCAmelCase											=					[]
												for i in trange(len(lowerCamelCase),					ascii=lowerCamelCase):
																		__lowerCAmelCase											=					TreebankWordDetokenizer().detokenize(vars(test_data[i])['''text'''])
																		__lowerCAmelCase											=					discriminator.tokenizer.encode(lowerCamelCase)
																		__lowerCAmelCase											=					torch.tensor([5_0_2_5_6] + seq,					device=lowerCamelCase,					dtype=torch.long)
																		test_x.append(lowerCamelCase)
																		test_y.append(classaidx[vars(test_data[i])['''label''']])
												__lowerCAmelCase											=					Dataset(lowerCamelCase,					lowerCamelCase)

												__lowerCAmelCase											=					{
												    '''class_size''': len(lowerCamelCase),
												    '''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(lowerCamelCase)}

												__lowerCAmelCase											=					Discriminator(
												    class_size=len(lowerCamelCase),					pretrained_model=lowerCamelCase,					cached_mode=lowerCamelCase,					device=lowerCamelCase).to(lowerCamelCase)

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

																														if len(lowerCamelCase) < max_length_seq:
																																				__lowerCAmelCase											=					torch.tensor([5_0_2_5_6] + seq,					device=lowerCamelCase,					dtype=torch.long)
																														else:
																																				print('''Line {} is longer than maximum length {}'''.format(lowerCamelCase,					lowerCamelCase))
																																				continue
																														x.append(lowerCamelCase)
																														y.append(d['''label'''])
																								except Exception:
																														print('''Error evaluating / tokenizing line {}, skipping it'''.format(lowerCamelCase))
																														pass

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

												__lowerCAmelCase											=					{
												    '''class_size''': len(lowerCamelCase),
												    '''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(lowerCamelCase)}

												__lowerCAmelCase											=					Discriminator(
												    class_size=len(lowerCamelCase),					pretrained_model=lowerCamelCase,					cached_mode=lowerCamelCase,					device=lowerCamelCase).to(lowerCamelCase)

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

																														if len(lowerCamelCase) < max_length_seq:
																																				__lowerCAmelCase											=					torch.tensor([5_0_2_5_6] + seq,					device=lowerCamelCase,					dtype=torch.long)
																														else:
																																				print('''Line {} is longer than maximum length {}'''.format(lowerCamelCase,					lowerCamelCase))
																																				continue
																														x.append(lowerCamelCase)
																														y.append(int(np.sum(d['''label''']) > 0))
																								except Exception:
																														print('''Error evaluating / tokenizing line {}, skipping it'''.format(lowerCamelCase))
																														pass

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

												__lowerCAmelCase											=					{
												    '''class_size''': len(lowerCamelCase),
												    '''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(lowerCamelCase) as f:
																		__lowerCAmelCase											=					csv.reader(lowerCamelCase,					delimiter='''\t''')
																		for row in tqdm(lowerCamelCase,					ascii=lowerCamelCase):
																								if row:
																														classes.add(row[0])

												__lowerCAmelCase											=					sorted(lowerCamelCase)
												__lowerCAmelCase											=					{c: i for i, c in enumerate(lowerCamelCase)}

												__lowerCAmelCase											=					Discriminator(
												    class_size=len(lowerCamelCase),					pretrained_model=lowerCamelCase,					cached_mode=lowerCamelCase,					device=lowerCamelCase).to(lowerCamelCase)

												__lowerCAmelCase											=					[]
												__lowerCAmelCase											=					[]
												with open(lowerCamelCase) as f:
																		__lowerCAmelCase											=					csv.reader(lowerCamelCase,					delimiter='''\t''')
																		for i, row in enumerate(tqdm(lowerCamelCase,					ascii=lowerCamelCase)):
																								if row:
																														__lowerCAmelCase											=					row[0]
																														__lowerCAmelCase											=					row[1]

																														try:
																																				__lowerCAmelCase											=					discriminator.tokenizer.encode(lowerCamelCase)
																																				if len(lowerCamelCase) < max_length_seq:
																																										__lowerCAmelCase											=					torch.tensor([5_0_2_5_6] + seq,					device=lowerCamelCase,					dtype=torch.long)

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

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

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

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

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

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

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

												__lowerCAmelCase											=					time.time()

												__lowerCAmelCase											=					get_cached_data_loader(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					shuffle=lowerCamelCase,					device=lowerCamelCase)

												__lowerCAmelCase											=					get_cached_data_loader(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					device=lowerCamelCase)

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

						else:
												__lowerCAmelCase											=					torch.utils.data.DataLoader(
												    dataset=lowerCamelCase,					batch_size=lowerCamelCase,					shuffle=lowerCamelCase,					collate_fn=lowerCamelCase)
												__lowerCAmelCase											=					torch.utils.data.DataLoader(dataset=lowerCamelCase,					batch_size=lowerCamelCase,					collate_fn=lowerCamelCase)

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

						__lowerCAmelCase											=					optim.Adam(discriminator.parameters(),					lr=0.00_01)

						for epoch in range(lowerCamelCase):
												__lowerCAmelCase											=					time.time()
												print('''\nEpoch''',					epoch + 1)

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

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

												print('''\nExample prediction''')
												predict(lowerCamelCase,					lowerCamelCase,					lowerCamelCase,					cached=lowerCamelCase,					device=lowerCamelCase)

												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(lowerCamelCase,					epoch + 1),					)


if __name__ == "__main__":
	_UpperCAmelCase					:			Union[str, Any]				     =					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""")
	_UpperCAmelCase					:			Optional[int]				     =					parser.parse_args()

	train_discriminator(**(vars(args)))





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_UpperCAmelCase					:			Optional[int]				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			str				     =					{
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
	import detectrona


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		List[Any]						=						'layoutlmv2'




				def __init__(self							,      __lowercase=3_05_22							,      __lowercase=7_68							,      __lowercase=12							,      __lowercase=12							,      __lowercase=30_72							,      __lowercase="gelu"							,      __lowercase=0.1							,      __lowercase=0.1							,      __lowercase=5_12							,      __lowercase=2							,      __lowercase=0.0_2							,      __lowercase=1e-12							,      __lowercase=0							,      __lowercase=10_24							,      __lowercase=1_28							,      __lowercase=32							,      __lowercase=True							,      __lowercase=2_56							,      __lowercase=64							,      __lowercase=True							,      __lowercase=[7, 7, 2_56]							,      __lowercase=1_28							,      __lowercase=1_28							,      __lowercase=True							,      __lowercase=True							,      __lowercase=False							,      __lowercase=None							,      **__lowercase							,      ):
										super().__init__(
										    vocab_size=__lowercase							,      hidden_size=__lowercase							,      num_hidden_layers=__lowercase							,      num_attention_heads=__lowercase							,      intermediate_size=__lowercase							,      hidden_act=__lowercase							,      hidden_dropout_prob=__lowercase							,      attention_probs_dropout_prob=__lowercase							,      max_position_embeddings=__lowercase							,      type_vocab_size=__lowercase							,      initializer_range=__lowercase							,      layer_norm_eps=__lowercase							,      pad_token_id=__lowercase							,      **__lowercase							,      )
										__lowerCAmelCase											=					max_ad_position_embeddings
										__lowerCAmelCase											=					max_rel_pos
										__lowerCAmelCase											=					rel_pos_bins
										__lowerCAmelCase											=					fast_qkv
										__lowerCAmelCase											=					max_rel_ad_pos
										__lowerCAmelCase											=					rel_ad_pos_bins
										__lowerCAmelCase											=					convert_sync_batchnorm
										__lowerCAmelCase											=					image_feature_pool_shape
										__lowerCAmelCase											=					coordinate_size
										__lowerCAmelCase											=					shape_size
										__lowerCAmelCase											=					has_relative_attention_bias
										__lowerCAmelCase											=					has_spatial_attention_bias
										__lowerCAmelCase											=					has_visual_segment_embedding
										__lowerCAmelCase											=					(
										    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
										)




				@classmethod
				def        _snake_case	(self		):
										return {
										    "MODEL.MASK_ON": True,
										    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
										    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
										    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
										    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
										    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
										    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
										    "MODEL.POST_NMS_TOPK_TEST": 10_00,
										    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
										    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
										    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
										    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
										    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
										    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
										    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
										    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
										    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
										    "MODEL.RESNETS.DEPTH": 1_01,
										    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
										    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.RESNETS.NUM_GROUPS": 32,
										    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
										    "MODEL.RESNETS.STRIDE_IN_1X1": False,
										}




				def        _snake_case	(self		):
										__lowerCAmelCase											=					detectrona.config.get_cfg()
										for k, v in self.detectrona_config_args.items():
																__lowerCAmelCase											=					k.split('''.'''		)
																__lowerCAmelCase											=					detectrona_config
																for attribute in attributes[:-1]:
																						__lowerCAmelCase											=					getattr(__lowercase							,      __lowercase		)
																setattr(__lowercase							,      attributes[-1]							,      __lowercase		)

										return detectrona_config

