"""simple docstring"""




import argparse
import json
import os

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCamelCase									=	logging.get_logger(__name__)






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				ConvNextVaConfig()

							if "atto" in checkpoint_url:
														A__						  =				[2, 2, 6, 2]
														A__						  =				[40, 80, 160, 320]
							if "femto" in checkpoint_url:
														A__						  =				[2, 2, 6, 2]
														A__						  =				[48, 96, 192, 384]
							if "pico" in checkpoint_url:
														A__						  =				[2, 2, 6, 2]
														A__						  =				[64, 128, 256, 512]
							if "nano" in checkpoint_url:
														A__						  =				[2, 2, 8, 2]
														A__						  =				[80, 160, 320, 640]
							if "tiny" in checkpoint_url:
														A__						  =				[3, 3, 9, 3]
														A__						  =				[96, 192, 384, 768]
							if "base" in checkpoint_url:
														A__						  =				[3, 3, 27, 3]
														A__						  =				[128, 256, 512, 1_024]
							if "large" in checkpoint_url:
														A__						  =				[3, 3, 27, 3]
														A__						  =				[192, 384, 768, 1_536]
							if "huge" in checkpoint_url:
														A__						  =				[3, 3, 27, 3]
														A__						  =				[352, 704, 1_408, 2_816]

							A__						  =				1_000
							A__						  =				'imagenet-1k-id2label.json'
							A__						  =				(1, 1_000)

							A__						  =				'huggingface/label-files'
							A__						  =				num_labels
							A__						  =				json.load(open(hf_hub_download(UpperCamelCase__							,     UpperCamelCase__							,     repo_type='dataset'					)							,     'r'					)					)
							A__						  =				{int(UpperCamelCase__					): v for k, v in idalabel.items()}

							A__						  =				idalabel
							A__						  =				{v: k for k, v in idalabel.items()}
							A__						  =				hidden_sizes
							A__						  =				depths

							return config, expected_shape






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							if "downsample_layers.0.0" in name:
														A__						  =				name.replace('downsample_layers.0.0'							,     'embeddings.patch_embeddings'					)
							if "downsample_layers.0.1" in name:
														A__						  =				name.replace('downsample_layers.0.1'							,     'embeddings.norm'					)  # we rename to layernorm later on
							if "downsample_layers.1.0" in name:
														A__						  =				name.replace('downsample_layers.1.0'							,     'stages.1.downsampling_layer.0'					)
							if "downsample_layers.1.1" in name:
														A__						  =				name.replace('downsample_layers.1.1'							,     'stages.1.downsampling_layer.1'					)
							if "downsample_layers.2.0" in name:
														A__						  =				name.replace('downsample_layers.2.0'							,     'stages.2.downsampling_layer.0'					)
							if "downsample_layers.2.1" in name:
														A__						  =				name.replace('downsample_layers.2.1'							,     'stages.2.downsampling_layer.1'					)
							if "downsample_layers.3.0" in name:
														A__						  =				name.replace('downsample_layers.3.0'							,     'stages.3.downsampling_layer.0'					)
							if "downsample_layers.3.1" in name:
														A__						  =				name.replace('downsample_layers.3.1'							,     'stages.3.downsampling_layer.1'					)
							if "stages" in name and "downsampling_layer" not in name:
														# stages.0.0. for instance should be renamed to stages.0.layers.0.
														A__						  =				name[: len('stages.0'					)] + '.layers' + name[len('stages.0'					) :]
							if "gamma" in name:
														A__						  =				name.replace('gamma'							,     'weight'					)
							if "beta" in name:
														A__						  =				name.replace('beta'							,     'bias'					)
							if "stages" in name:
														A__						  =				name.replace('stages'							,     'encoder.stages'					)
							if "norm" in name:
														A__						  =				name.replace('norm'							,     'layernorm'					)
							if "head" in name:
														A__						  =				name.replace('head'							,     'classifier'					)

							return name






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				'http://images.cocodataset.org/val2017/000000039769.jpg'
							A__						  =				Image.open(requests.get(UpperCamelCase__							,     stream=UpperCamelCase__					).raw					)
							return im






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							if "224" in checkpoint_url:
														A__						  =				224
														A__						  =				224 / 256
							elif "384" in checkpoint_url:
														A__						  =				384
														A__						  =				None
							else:
														A__						  =				512
														A__						  =				None

							return ConvNextImageProcessor(
							    size=UpperCamelCase__							,     crop_pct=UpperCamelCase__							,     image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]							,     image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]							,     resample=PILImageResampling.BICUBIC							,     )






@torch.no_grad()
def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							print('Downloading original model from checkpoint...'					)
							# define ConvNeXTV2 configuration based on URL
							A__  ,	A__						  =				get_convnextva_config(UpperCamelCase__					)
							# load original state_dict from URL
							A__						  =				torch.hub.load_state_dict_from_url(UpperCamelCase__					)['model']

							print('Converting model parameters...'					)
							# rename keys
							for key in state_dict.copy().keys():
														A__						  =				state_dict.pop(UpperCamelCase__					)
														A__						  =				val
							# add prefix to all keys expect classifier head
							for key in state_dict.copy().keys():
														A__						  =				state_dict.pop(UpperCamelCase__					)
														if not key.startswith('classifier'					):
																					A__						  =				'convnextv2.' + key
														A__						  =				val

							# load HuggingFace model
							A__						  =				ConvNextVaForImageClassification(UpperCamelCase__					)
							model.load_state_dict(UpperCamelCase__					)
							model.eval()

							# Check outputs on an image, prepared by ConvNextImageProcessor
							A__						  =				convert_preprocessor(UpperCamelCase__					)
							A__						  =				preprocessor(images=prepare_img()							,     return_tensors='pt'					)
							A__						  =				model(**UpperCamelCase__					).logits

							# note: the logits below were obtained without center cropping
							if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
														A__						  =				torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
														A__						  =				torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
														A__						  =				torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
														A__						  =				torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
														A__						  =				torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
														A__						  =				torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
														A__						  =				torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
														A__						  =				torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
														A__						  =				torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
														A__						  =				torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
														A__						  =				torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9]					)
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
														A__						  =				torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6]					)
							else:
														raise ValueError(F'''Unknown URL: {checkpoint_url}'''					)

							assert torch.allclose(logits[0, :5]							,     UpperCamelCase__							,     atol=1E-3					)
							assert logits.shape == expected_shape
							print('Model outputs match the original results!'					)

							if save_model:
														print('Saving model to local...'					)
														# Create folder to save model
														if not os.path.isdir(UpperCamelCase__					):
																					os.mkdir(UpperCamelCase__					)

														model.save_pretrained(UpperCamelCase__					)
														preprocessor.save_pretrained(UpperCamelCase__					)

							A__						  =				'convnextv2'
							if "atto" in checkpoint_url:
														model_name += "-atto"
							if "femto" in checkpoint_url:
														model_name += "-femto"
							if "pico" in checkpoint_url:
														model_name += "-pico"
							if "nano" in checkpoint_url:
														model_name += "-nano"
							elif "tiny" in checkpoint_url:
														model_name += "-tiny"
							elif "base" in checkpoint_url:
														model_name += "-base"
							elif "large" in checkpoint_url:
														model_name += "-large"
							elif "huge" in checkpoint_url:
														model_name += "-huge"
							if "22k" in checkpoint_url and "1k" not in checkpoint_url:
														model_name += "-22k"
							elif "22k" in checkpoint_url and "1k" in checkpoint_url:
														model_name += "-22k-1k"
							elif "1k" in checkpoint_url:
														model_name += "-1k"
							if "224" in checkpoint_url:
														model_name += "-224"
							elif "384" in checkpoint_url:
														model_name += "-384"
							elif "512" in checkpoint_url:
														model_name += "-512"

							if push_to_hub:
														print(F'''Pushing {model_name} to the hub...'''					)
														model.push_to_hub(UpperCamelCase__					)
														preprocessor.push_to_hub(UpperCamelCase__					)


if __name__ == "__main__":
				__lowerCamelCase									=	argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
				    type=str,
				    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default="model",
				    type=str,
				    help="Path to the output PyTorch model directory.",
				)
				parser.add_argument("--save_model", action="store_true", help="Save model to local")
				parser.add_argument("--push_to_hub", action="store_true", help="Push model and image preprocessor to the hub")

				__lowerCamelCase									=	parser.parse_args()
				convert_convnextva_checkpoint(
				    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
				)






"""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)
__lowerCamelCase									=	1E-10
__lowerCamelCase									=	"This is incredible! I love it, this is the best chicken I have ever had."
__lowerCamelCase									=	1_00
class      UpperCamelCase__(      nn.Module			):





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





						def 					snake_case__						(							self  )       ->					List[str]:
													return self.classifier_head





						def 					snake_case__						(							self  )       ->					Optional[int]:
													for param in self.encoder.parameters():
																				A__						  =				False
													self.classifier_head.train()





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					List[Any]:
													A__						  =				x.ne(0  ).unsqueeze(2  ).repeat(1		,1		,self.embed_size  ).float().to(self.device  ).detach()
													A__						  =				self.encoder.transformer(__UpperCAmelCase  )['last_hidden_state']
													A__						  =				hidden * mask
													A__						  =				torch.sum(__UpperCAmelCase		,dim=1  ) / (torch.sum(__UpperCAmelCase		,dim=1  ).detach() + EPSILON)
													return avg_hidden





						def 					snake_case__						(							self		,__UpperCAmelCase  )       ->					Dict:
													if self.cached_mode:
																				A__						  =				x.to(self.device  )
													else:
																				A__						  =				self.avg_representation(x.to(self.device  )  )

													A__						  =				self.classifier_head(__UpperCAmelCase  )
													A__						  =				nn.functional.log_softmax(__UpperCAmelCase		,dim=-1  )

													return probs






class      UpperCamelCase__(      data.Dataset			):





						def __init__(							self		,__UpperCAmelCase		,__UpperCAmelCase  )       ->					Union[str, Any]:
													A__						  =				X
													A__						  =				y





						def __len__(							self  )       ->					List[str]:
													return len(self.X  )





						def __getitem__(							self		,__UpperCAmelCase  )       ->					Any:
													A__						  =				{}
													A__						  =				self.X[index]
													A__						  =				self.y[index]
													return data






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





							def pad_sequences(UpperCamelCase__					):
														A__						  =				[len(UpperCamelCase__					) for seq in sequences]

														A__						  =				torch.zeros(len(UpperCamelCase__					)							,     max(UpperCamelCase__					)					).long()  # padding value = 0

														for i, seq in enumerate(UpperCamelCase__					):
																					A__						  =				lengths[i]
																					A__						  =				seq[:end]

														return padded_sequences, lengths

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

							A__  ,	A__						  =				pad_sequences(item_info['X']					)
							A__						  =				torch.tensor(item_info['y']							,     dtype=torch.long					)

							return x_batch, y_batch






def 	UpperCAmelCase       (						UpperCamelCase__					):






							"""simple docstring"""





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

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

							return x_batch, y_batch






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=0							,     UpperCamelCase__=10							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





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

														optimizer.zero_grad()

														A__						  =				discriminator(UpperCamelCase__					)
														A__						  =				nn.functional.nll_loss(UpperCamelCase__							,     UpperCamelCase__					)
														loss.backward(retain_graph=UpperCamelCase__					)
														optimizer.step()

														samples_so_far += len(UpperCamelCase__					)

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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





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

							test_loss /= len(data_loader.dataset					)

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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=False							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





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

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






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__=False							,     UpperCamelCase__="cpu"					):






							"""simple docstring"""





							A__						  =				torch.utils.data.DataLoader(dataset=UpperCamelCase__							,     batch_size=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)

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

							A__						  =				torch.utils.data.DataLoader(
							    dataset=Dataset(UpperCamelCase__							,     UpperCamelCase__					)							,     batch_size=UpperCamelCase__							,     shuffle=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)

							return data_loader






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__=None							,     UpperCamelCase__="gpt2-medium"							,     UpperCamelCase__=10							,     UpperCamelCase__=64							,     UpperCamelCase__=10							,     UpperCamelCase__=False							,     UpperCamelCase__=False							,     UpperCamelCase__=False							,     ):






							"""simple docstring"""





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

							print('Preprocessing {} dataset...'.format(UpperCamelCase__					)					)
							A__						  =				time.time()

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

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

														A__						  =				torchtext_data.Field()
														A__						  =				torchtext_data.Field(sequential=UpperCamelCase__					)
														A__  ,	A__  ,	A__						  =				datasets.SST.splits(
														    UpperCamelCase__							,     UpperCamelCase__							,     fine_grained=UpperCamelCase__							,     train_subtrees=UpperCamelCase__							,     )

														A__						  =				[]
														A__						  =				[]
														for i in trange(len(UpperCamelCase__					)							,     ascii=UpperCamelCase__					):
																					A__						  =				TreebankWordDetokenizer().detokenize(vars(train_data[i]					)['text']					)
																					A__						  =				discriminator.tokenizer.encode(UpperCamelCase__					)
																					A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)
																					x.append(UpperCamelCase__					)
																					y.append(classaidx[vars(train_data[i]					)['label']]					)
														A__						  =				Dataset(UpperCamelCase__							,     UpperCamelCase__					)

														A__						  =				[]
														A__						  =				[]
														for i in trange(len(UpperCamelCase__					)							,     ascii=UpperCamelCase__					):
																					A__						  =				TreebankWordDetokenizer().detokenize(vars(test_data[i]					)['text']					)
																					A__						  =				discriminator.tokenizer.encode(UpperCamelCase__					)
																					A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)
																					test_x.append(UpperCamelCase__					)
																					test_y.append(classaidx[vars(test_data[i]					)['label']]					)
														A__						  =				Dataset(UpperCamelCase__							,     UpperCamelCase__					)

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

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

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

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

																																			if len(UpperCamelCase__					) < max_length_seq:
																																										A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)
																																			else:
																																										print('Line {} is longer than maximum length {}'.format(UpperCamelCase__							,     UpperCamelCase__					)					)
																																										continue
																																			x.append(UpperCamelCase__					)
																																			y.append(d['label']					)
																												except Exception:
																																			print('Error evaluating / tokenizing line {}, skipping it'.format(UpperCamelCase__					)					)
																																			pass

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

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

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

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

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

																																			if len(UpperCamelCase__					) < max_length_seq:
																																										A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)
																																			else:
																																										print('Line {} is longer than maximum length {}'.format(UpperCamelCase__							,     UpperCamelCase__					)					)
																																										continue
																																			x.append(UpperCamelCase__					)
																																			y.append(int(np.sum(d['label']					) > 0					)					)
																												except Exception:
																																			print('Error evaluating / tokenizing line {}, skipping it'.format(UpperCamelCase__					)					)
																																			pass

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

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

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

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

														A__						  =				set()
														with open(UpperCamelCase__					) as f:
																					A__						  =				csv.reader(UpperCamelCase__							,     delimiter='\t'					)
																					for row in tqdm(UpperCamelCase__							,     ascii=UpperCamelCase__					):
																												if row:
																																			classes.add(row[0]					)

														A__						  =				sorted(UpperCamelCase__					)
														A__						  =				{c: i for i, c in enumerate(UpperCamelCase__					)}

														A__						  =				Discriminator(
														    class_size=len(UpperCamelCase__					)							,     pretrained_model=UpperCamelCase__							,     cached_mode=UpperCamelCase__							,     device=UpperCamelCase__					).to(UpperCamelCase__					)

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

																																			try:
																																										A__						  =				discriminator.tokenizer.encode(UpperCamelCase__					)
																																										if len(UpperCamelCase__					) < max_length_seq:
																																																	A__						  =				torch.tensor([50_256] + seq							,     device=UpperCamelCase__							,     dtype=torch.long					)

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

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

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

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

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

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

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

														A__						  =				time.time()

														A__						  =				get_cached_data_loader(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     shuffle=UpperCamelCase__							,     device=UpperCamelCase__					)

														A__						  =				get_cached_data_loader(UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__							,     device=UpperCamelCase__					)

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

							else:
														A__						  =				torch.utils.data.DataLoader(
														    dataset=UpperCamelCase__							,     batch_size=UpperCamelCase__							,     shuffle=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)
														A__						  =				torch.utils.data.DataLoader(dataset=UpperCamelCase__							,     batch_size=UpperCamelCase__							,     collate_fn=UpperCamelCase__					)

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

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

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

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

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

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

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


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

				train_discriminator(**(vars(args)))





