




"""simple docstring"""

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-1_0
__A							=     "This is incredible! I love it, this is the best chicken I have ever had."
__A							=     100
class      lowerCamelCase__      (					nn.Module		):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE="gpt2-medium"			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE="cpu"							):



					"""simple docstring"""







					super().__init__()
					snake_case						:  Optional[Any]        =			GPTaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							)
					snake_case						:  Tuple        =			GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[Any]        =			self.encoder.transformer.config.hidden_size
					snake_case						:  Optional[int]        =			ClassificationHead(class_size=SCREAMING_SNAKE_CASE			,	embed_size=self.embed_size							)
					snake_case						:  Optional[Any]        =			cached_mode
					snake_case						:  str        =			device


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return self.classifier_head


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Any        =			x.ne(0							).unsqueeze(2							).repeat(1			,	1			,	self.embed_size							).float().to(self.device							).detach()
					snake_case						:  Optional[Any]        =			self.encoder.transformer(SCREAMING_SNAKE_CASE							)["last_hidden_state"]
					snake_case						:  Dict        =			hidden * mask
					snake_case						:  Optional[int]        =			torch.sum(SCREAMING_SNAKE_CASE			,	dim=1							) / (torch.sum(SCREAMING_SNAKE_CASE			,	dim=1							).detach() + EPSILON)
					return avg_hidden



			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if self.cached_mode:
							snake_case						:  Any        =			x.to(self.device							)
					else:
							snake_case						:  str        =			self.avg_representation(x.to(self.device							)							)

					snake_case						:  Union[str, Any]        =			self.classifier_head(SCREAMING_SNAKE_CASE							)
					snake_case						:  Optional[Any]        =			nn.functional.log_softmax(SCREAMING_SNAKE_CASE			,	dim=-1							)

					return probs







class      lowerCamelCase__      (					data.Dataset		):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  List[str]        =			X
					snake_case						:  Dict        =			y


			def __len__(       self							):



					"""simple docstring"""







					return len(self.X							)



			def __getitem__(       self			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case						:  Optional[Any]        =			{}
					snake_case						:  Optional[Any]        =			self.X[index]
					snake_case						:  Optional[int]        =			self.y[index]
					return data







def 					UpperCamelCase__       (						lowercase__					:       Optional[int]				):
		def pad_sequences(lowercase__					:       List[Any]				):
				snake_case						:  Union[str, Any]        =			[len(lowercase__				) for seq in sequences]

				snake_case						:  List[str]        =			torch.zeros(len(lowercase__				)    ,       max(lowercase__				)				).long()  # padding value = 0

				for i, seq in enumerate(lowercase__				):
						snake_case						:  Dict        =			lengths[i]
						snake_case						:  str        =			seq[:end]

				return padded_sequences, lengths

		snake_case						:  Any        =			{}
		for key in data[0].keys():
				snake_case						:  Union[str, Any]        =			[d[key] for d in data]

		snake_case						:  Tuple        =			pad_sequences(item_info["X"]				)
		snake_case						:  Any        =			torch.tensor(item_info["y"]    ,       dtype=torch.long				)

		return x_batch, y_batch



def 					UpperCamelCase__       (						lowercase__					:       Optional[Any]				):
		snake_case						:  Optional[Any]        =			{}
		for key in data[0].keys():
				snake_case						:  List[str]        =			[d[key] for d in data]

		snake_case						:  str        =			torch.cat(item_info["X"]    ,       0				)
		snake_case						:  Union[str, Any]        =			torch.tensor(item_info["y"]    ,       dtype=torch.long				)

		return x_batch, y_batch



def 					UpperCamelCase__       (						lowercase__					:       List[Any]    ,       lowercase__					:       Optional[int]    ,       lowercase__					:       int    ,       lowercase__					:       List[str]=0    ,       lowercase__					:       List[str]=10    ,       lowercase__					:       Optional[Any]="cpu"				):
		snake_case						:  int        =			0
		discriminator.train_custom()
		for batch_idx, (input_t, target_t) in enumerate(lowercase__				):
				snake_case						:  Union[str, Any]        =			input_t.to(lowercase__				), target_t.to(lowercase__				)

				optimizer.zero_grad()

				snake_case						:  List[Any]        =			discriminator(lowercase__				)
				snake_case						:  List[Any]        =			nn.functional.nll_loss(lowercase__    ,       lowercase__				)
				loss.backward(retain_graph=lowercase__				)
				optimizer.step()

				samples_so_far += len(lowercase__				)

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



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

		test_loss /= len(data_loader.dataset				)

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



def 					UpperCamelCase__       (						lowercase__					:       Tuple    ,       lowercase__					:       str    ,       lowercase__					:       Any    ,       lowercase__					:       Tuple=False    ,       lowercase__					:       List[Any]="cpu"				):
		snake_case						:  int        =			model.tokenizer.encode(lowercase__				)
		snake_case						:  Optional[int]        =			torch.tensor([input_t]    ,       dtype=torch.long    ,       device=lowercase__				)
		if cached:
				snake_case						:  Tuple        =			model.avg_representation(lowercase__				)

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



def 					UpperCamelCase__       (						lowercase__					:       str    ,       lowercase__					:       List[Any]    ,       lowercase__					:       Any    ,       lowercase__					:       List[str]=False    ,       lowercase__					:       List[str]="cpu"				):
		snake_case						:  str        =			torch.utils.data.DataLoader(dataset=lowercase__    ,       batch_size=lowercase__    ,       collate_fn=lowercase__				)

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

		snake_case						:  Tuple        =			torch.utils.data.DataLoader(
		    dataset=Dataset(lowercase__    ,       lowercase__				)    ,       batch_size=lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__				)

		return data_loader



def 					UpperCamelCase__       (						lowercase__					:       Optional[Any]    ,       lowercase__					:       Any=None    ,       lowercase__					:       Dict="gpt2-medium"    ,       lowercase__					:       str=10    ,       lowercase__					:       Tuple=64    ,       lowercase__					:       List[Any]=10    ,       lowercase__					:       str=False    ,       lowercase__					:       int=False    ,       lowercase__					:       Optional[int]=False    ,       ):
		snake_case						:  List[str]        =			"cuda" if torch.cuda.is_available() and not no_cuda else "cpu"

		print("Preprocessing {} dataset...".format(lowercase__				)				)
		snake_case						:  str        =			time.time()

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

				snake_case						:  str        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

				snake_case						:  Dict        =			torchtext_data.Field()
				snake_case						:  int        =			torchtext_data.Field(sequential=lowercase__				)
				snake_case						:  Dict        =			datasets.SST.splits(
				    lowercase__    ,       lowercase__    ,       fine_grained=lowercase__    ,       train_subtrees=lowercase__    ,       )

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

				snake_case						:  int        =			[]
				snake_case						:  Dict        =			[]
				for i in trange(len(lowercase__				)    ,       ascii=lowercase__				):
						snake_case						:  List[Any]        =			TreebankWordDetokenizer().detokenize(vars(test_data[i]				)["text"]				)
						snake_case						:  int        =			discriminator.tokenizer.encode(lowercase__				)
						snake_case						:  Union[str, Any]        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)
						test_x.append(lowercase__				)
						test_y.append(classaidx[vars(test_data[i]				)["label"]]				)
				snake_case						:  Optional[Any]        =			Dataset(lowercase__    ,       lowercase__				)

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

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

				snake_case						:  Dict        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

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

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

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

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

		elif dataset == "toxic":
				snake_case						:  List[Any]        =			["non_toxic", "toxic"]
				snake_case						:  Union[str, Any]        =			{c: i for i, c in enumerate(lowercase__				)}

				snake_case						:  str        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

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

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

				snake_case						:  Dict        =			Dataset(lowercase__    ,       lowercase__				)
				snake_case						:  Optional[int]        =			int(0.9 * len(lowercase__				)				)
				snake_case						:  List[Any]        =			len(lowercase__				) - train_size
				snake_case						:  Dict        =			torch.utils.data.random_split(lowercase__    ,       [train_size, test_size]				)

				snake_case						:  Optional[Any]        =			{
				    "class_size": len(lowercase__				),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 0,
				}

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

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

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

				snake_case						:  List[Any]        =			sorted(lowercase__				)
				snake_case						:  Optional[int]        =			{c: i for i, c in enumerate(lowercase__				)}

				snake_case						:  int        =			Discriminator(
				    class_size=len(lowercase__				)    ,       pretrained_model=lowercase__    ,       cached_mode=lowercase__    ,       device=lowercase__				).to(lowercase__				)

				snake_case						:  Dict        =			[]
				snake_case						:  List[Any]        =			[]
				with open(lowercase__				) as f:
						snake_case						:  List[str]        =			csv.reader(lowercase__    ,       delimiter="\t"				)
						for i, row in enumerate(tqdm(lowercase__    ,       ascii=lowercase__				)				):
								if row:
										snake_case						:  Optional[int]        =			row[0]
										snake_case						:  Optional[Any]        =			row[1]

										try:
												snake_case						:  Union[str, Any]        =			discriminator.tokenizer.encode(lowercase__				)
												if len(lowercase__				) < max_length_seq:
														snake_case						:  Union[str, Any]        =			torch.tensor([5_0256] + seq    ,       device=lowercase__    ,       dtype=torch.long				)

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

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

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

				snake_case						:  List[Any]        =			Dataset(lowercase__    ,       lowercase__				)
				snake_case						:  int        =			int(0.9 * len(lowercase__				)				)
				snake_case						:  List[str]        =			len(lowercase__				) - train_size
				snake_case						:  Dict        =			torch.utils.data.random_split(lowercase__    ,       [train_size, test_size]				)

				snake_case						:  List[str]        =			{
				    "class_size": len(lowercase__				),
				    "embed_size": discriminator.embed_size,
				    "pretrained_model": pretrained_model,
				    "class_vocab": classaidx,
				    "default_class": 0,
				}

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

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

				snake_case						:  int        =			time.time()

				snake_case						:  Union[str, Any]        =			get_cached_data_loader(lowercase__    ,       lowercase__    ,       lowercase__    ,       shuffle=lowercase__    ,       device=lowercase__				)

				snake_case						:  List[Any]        =			get_cached_data_loader(lowercase__    ,       lowercase__    ,       lowercase__    ,       device=lowercase__				)

				snake_case						:  Optional[int]        =			time.time()
				print("Building representation cache took: {:.3f}s".format(end - start				)				)

		else:
				snake_case						:  Union[str, Any]        =			torch.utils.data.DataLoader(
				    dataset=lowercase__    ,       batch_size=lowercase__    ,       shuffle=lowercase__    ,       collate_fn=lowercase__				)
				snake_case						:  int        =			torch.utils.data.DataLoader(dataset=lowercase__    ,       batch_size=lowercase__    ,       collate_fn=lowercase__				)

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

		snake_case						:  Union[str, Any]        =			optim.Adam(discriminator.parameters()    ,       lr=0.00_01				)

		for epoch in range(lowercase__				):
				snake_case						:  Union[str, Any]        =			time.time()
				print("\nEpoch"    ,       epoch + 1				)

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

				snake_case						:  List[Any]        =			time.time()
				print("Epoch took: {:.3f}s".format(end - start				)				)

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

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


if __name__ == "__main__":
							__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=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")
							__A							=     parser.parse_args()

							train_discriminator(**(vars(args)))







"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

