
"""simple docstring"""




import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase__							=				logging.getLogger(__name__)



def        _lowerCamelCase ( __a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = np.argmax(__a,							axis=1    )
		return np.sum(outputs == labels    )



def        _lowerCamelCase ( __a    ):
		with open(__a,							encoding='''utf_8'''    ) as f:
				SCREAMING_SNAKE_CASE_						 = csv.reader(__a    )
				SCREAMING_SNAKE_CASE_						 = []
				next(__a    )  # skip the first line
				for line in tqdm(__a    ):
						output.append((''' '''.join(line[1:5]    ), line[5], line[6], int(line[-1]    ) - 1)    )
		return output



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							__a    ):
		SCREAMING_SNAKE_CASE_						 = []
		for dataset in encoded_datasets:
				SCREAMING_SNAKE_CASE_						 = len(__a    )
				SCREAMING_SNAKE_CASE_						 = np.zeros((n_batch, 2, input_len),							dtype=np.intaa    )
				SCREAMING_SNAKE_CASE_						 = np.zeros((n_batch, 2),							dtype=np.intaa    )
				SCREAMING_SNAKE_CASE_						 = np.full((n_batch, 2, input_len),							fill_value=-100,							dtype=np.intaa    )
				SCREAMING_SNAKE_CASE_						 = np.zeros((n_batch,),							dtype=np.intaa    )
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(__a    ):
						SCREAMING_SNAKE_CASE_						 = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						SCREAMING_SNAKE_CASE_						 = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						SCREAMING_SNAKE_CASE_						 = with_conta
						SCREAMING_SNAKE_CASE_						 = with_conta
						SCREAMING_SNAKE_CASE_						 = len(__a    ) - 1
						SCREAMING_SNAKE_CASE_						 = len(__a    ) - 1
						SCREAMING_SNAKE_CASE_						 = with_conta
						SCREAMING_SNAKE_CASE_						 = with_conta
						SCREAMING_SNAKE_CASE_						 = mc_label
				SCREAMING_SNAKE_CASE_						 = (input_ids, mc_token_ids, lm_labels, mc_labels)
				tensor_datasets.append(tuple(torch.tensor(__a    ) for t in all_inputs    )    )
		return tensor_datasets



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = argparse.ArgumentParser()
		parser.add_argument('''--model_name''',							type=__a,							default='''openai-gpt''',							help='''pretrained model name'''    )
		parser.add_argument('''--do_train''',							action='''store_true''',							help='''Whether to run training.'''    )
		parser.add_argument('''--do_eval''',							action='''store_true''',							help='''Whether to run eval on the dev set.'''    )
		parser.add_argument(
		    '''--output_dir''',							default=__a,							type=__a,							required=__a,							help='''The output directory where the model predictions and checkpoints will be written.''',							)
		parser.add_argument('''--train_dataset''',							type=__a,							default=''''''    )
		parser.add_argument('''--eval_dataset''',							type=__a,							default=''''''    )
		parser.add_argument('''--seed''',							type=__a,							default=42    )
		parser.add_argument('''--num_train_epochs''',							type=__a,							default=3    )
		parser.add_argument('''--train_batch_size''',							type=__a,							default=8    )
		parser.add_argument('''--eval_batch_size''',							type=__a,							default=16    )
		parser.add_argument('''--adam_epsilon''',							default=1E-8,							type=__a,							help='''Epsilon for Adam optimizer.'''    )
		parser.add_argument('''--max_grad_norm''',							type=__a,							default=1    )
		parser.add_argument(
		    '''--max_steps''',							default=-1,							type=__a,							help=(
		        '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
		    ),							)
		parser.add_argument(
		    '''--gradient_accumulation_steps''',							type=__a,							default=1,							help='''Number of updates steps to accumulate before                        performing a backward/update pass.''',							)
		parser.add_argument('''--learning_rate''',							type=__a,							default=6.25E-5    )
		parser.add_argument('''--warmup_steps''',							default=0,							type=__a,							help='''Linear warmup over warmup_steps.'''    )
		parser.add_argument('''--lr_schedule''',							type=__a,							default='''warmup_linear'''    )
		parser.add_argument('''--weight_decay''',							type=__a,							default=0.0_1    )
		parser.add_argument('''--lm_coef''',							type=__a,							default=0.9    )
		parser.add_argument('''--n_valid''',							type=__a,							default=374    )

		parser.add_argument('''--server_ip''',							type=__a,							default='''''',							help='''Can be used for distant debugging.'''    )
		parser.add_argument('''--server_port''',							type=__a,							default='''''',							help='''Can be used for distant debugging.'''    )
		SCREAMING_SNAKE_CASE_						 = parser.parse_args()
		print(__a    )

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print('''Waiting for debugger attach'''    )
				ptvsd.enable_attach(address=(args.server_ip, args.server_port),							redirect_output=__a    )
				ptvsd.wait_for_attach()

		random.seed(args.seed    )
		np.random.seed(args.seed    )
		torch.manual_seed(args.seed    )
		torch.cuda.manual_seed_all(args.seed    )

		SCREAMING_SNAKE_CASE_						 = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''    )
		SCREAMING_SNAKE_CASE_						 = torch.cuda.device_count()
		logger.info('''device: {}, n_gpu {}'''.format(__a,							__a    )    )

		if not args.do_train and not args.do_eval:
				raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''    )

		if not os.path.exists(args.output_dir    ):
				os.makedirs(args.output_dir    )

		# Load tokenizer and model
		# This loading functions also add new tokens and embeddings called `special tokens`
		# These new embeddings will be fine-tuned on the RocStories dataset
		SCREAMING_SNAKE_CASE_						 = ['''_start_''', '''_delimiter_''', '''_classify_''']
		SCREAMING_SNAKE_CASE_						 = OpenAIGPTTokenizer.from_pretrained(args.model_name    )
		tokenizer.add_tokens(__a    )
		SCREAMING_SNAKE_CASE_						 = tokenizer.convert_tokens_to_ids(__a    )
		SCREAMING_SNAKE_CASE_						 = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
		model.resize_token_embeddings(len(__a    )    )
		model.to(__a    )

		# Load and encode the datasets
		def tokenize_and_encode(__a    ):
				if isinstance(__a,							__a    ):
						return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__a    )    )
				elif isinstance(__a,							__a    ):
						return obj
				return [tokenize_and_encode(__a    ) for o in obj]

		logger.info('''Encoding dataset...'''    )
		SCREAMING_SNAKE_CASE_						 = load_rocstories_dataset(args.train_dataset    )
		SCREAMING_SNAKE_CASE_						 = load_rocstories_dataset(args.eval_dataset    )
		SCREAMING_SNAKE_CASE_						 = (train_dataset, eval_dataset)
		SCREAMING_SNAKE_CASE_						 = tokenize_and_encode(__a    )

		# Compute the max input length for the Transformer
		SCREAMING_SNAKE_CASE_						 = model.config.n_positions // 2 - 2
		SCREAMING_SNAKE_CASE_						 = max(
		    len(story[:max_length]    ) + max(len(conta[:max_length]    ),							len(conta[:max_length]    )    ) + 3
		    for dataset in encoded_datasets
		    for story, conta, conta, _ in dataset    )
		SCREAMING_SNAKE_CASE_						 = min(__a,							model.config.n_positions    )  # Max size of input for the pre-trained model

		# Prepare inputs tensors and dataloaders
		SCREAMING_SNAKE_CASE_						 = pre_process_datasets(__a,							__a,							__a,							*__a    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = tensor_datasets[0], tensor_datasets[1]

		SCREAMING_SNAKE_CASE_						 = TensorDataset(*__a    )
		SCREAMING_SNAKE_CASE_						 = RandomSampler(__a    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							sampler=__a,							batch_size=args.train_batch_size    )

		SCREAMING_SNAKE_CASE_						 = TensorDataset(*__a    )
		SCREAMING_SNAKE_CASE_						 = SequentialSampler(__a    )
		SCREAMING_SNAKE_CASE_						 = DataLoader(__a,							sampler=__a,							batch_size=args.eval_batch_size    )

		# Prepare optimizer
		if args.do_train:
				if args.max_steps > 0:
						SCREAMING_SNAKE_CASE_						 = args.max_steps
						SCREAMING_SNAKE_CASE_						 = args.max_steps // (len(__a    ) // args.gradient_accumulation_steps) + 1
				else:
						SCREAMING_SNAKE_CASE_						 = len(__a    ) // args.gradient_accumulation_steps * args.num_train_epochs

				SCREAMING_SNAKE_CASE_						 = list(model.named_parameters()    )
				SCREAMING_SNAKE_CASE_						 = ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
				SCREAMING_SNAKE_CASE_						 = [
				    {
				        '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay    )],
				        '''weight_decay''': args.weight_decay,
				    },
				    {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay    )], '''weight_decay''': 0.0},
				]
				SCREAMING_SNAKE_CASE_						 = AdamW(__a,							lr=args.learning_rate,							eps=args.adam_epsilon    )
				SCREAMING_SNAKE_CASE_						 = get_linear_schedule_with_warmup(
				    __a,							num_warmup_steps=args.warmup_steps,							num_training_steps=__a    )

		if args.do_train:
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 0, 0, None
				model.train()
				for _ in trange(int(args.num_train_epochs    ),							desc='''Epoch'''    ):
						SCREAMING_SNAKE_CASE_						 = 0
						SCREAMING_SNAKE_CASE_						 = 0
						SCREAMING_SNAKE_CASE_						 = tqdm(__a,							desc='''Training'''    )
						for step, batch in enumerate(__a    ):
								SCREAMING_SNAKE_CASE_						 = tuple(t.to(__a    ) for t in batch    )
								SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = batch
								SCREAMING_SNAKE_CASE_						 = model(__a,							mc_token_ids=__a,							lm_labels=__a,							mc_labels=__a    )
								SCREAMING_SNAKE_CASE_						 = args.lm_coef * losses[0] + losses[1]
								loss.backward()
								optimizer.step()
								scheduler.step()
								optimizer.zero_grad()
								tr_loss += loss.item()
								SCREAMING_SNAKE_CASE_						 = (
								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
								)
								nb_tr_steps += 1
								SCREAMING_SNAKE_CASE_						 = '''Training loss: {:.2e} lr: {:.2e}'''.format(__a,							scheduler.get_lr()[0]    )

    # Save a trained model
		if args.do_train:
				# Save a trained model, configuration and tokenizer
				SCREAMING_SNAKE_CASE_						 = model.module if hasattr(__a,							'''module'''    ) else model  # Only save the model itself

				# If we save using the predefined names, we can load using `from_pretrained`
				SCREAMING_SNAKE_CASE_						 = os.path.join(args.output_dir,							__a    )
				SCREAMING_SNAKE_CASE_						 = os.path.join(args.output_dir,							__a    )

				torch.save(model_to_save.state_dict(),							__a    )
				model_to_save.config.to_json_file(__a    )
				tokenizer.save_vocabulary(args.output_dir    )

				# Load a trained model and vocabulary that you have fine-tuned
				SCREAMING_SNAKE_CASE_						 = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
				SCREAMING_SNAKE_CASE_						 = OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
				model.to(__a    )

		if args.do_eval:
				model.eval()
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 0, 0
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 0, 0
				for batch in tqdm(__a,							desc='''Evaluating'''    ):
						SCREAMING_SNAKE_CASE_						 = tuple(t.to(__a    ) for t in batch    )
						SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = batch
						with torch.no_grad():
								SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = model(
								    __a,							mc_token_ids=__a,							lm_labels=__a,							mc_labels=__a    )

						SCREAMING_SNAKE_CASE_						 = mc_logits.detach().cpu().numpy()
						SCREAMING_SNAKE_CASE_						 = mc_labels.to('''cpu'''    ).numpy()
						SCREAMING_SNAKE_CASE_						 = accuracy(__a,							__a    )

						eval_loss += mc_loss.mean().item()
						eval_accuracy += tmp_eval_accuracy

						nb_eval_examples += input_ids.size(0    )
						nb_eval_steps += 1

				SCREAMING_SNAKE_CASE_						 = eval_loss / nb_eval_steps
				SCREAMING_SNAKE_CASE_						 = eval_accuracy / nb_eval_examples
				SCREAMING_SNAKE_CASE_						 = tr_loss / nb_tr_steps if args.do_train else None
				SCREAMING_SNAKE_CASE_						 = {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

				SCREAMING_SNAKE_CASE_						 = os.path.join(args.output_dir,							'''eval_results.txt'''    )
				with open(__a,							'''w'''    ) as writer:
						logger.info('''***** Eval results *****'''    )
						for key in sorted(result.keys()    ):
								logger.info('''  %s = %s''',							__a,							str(result[key]    )    )
								writer.write('''%s = %s\n''' % (key, str(result[key]    ))    )


if __name__ == "__main__":
						main()

"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_="cls"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,       bos_token_id=SCREAMING_SNAKE_CASE_       ,       eos_token_id=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = project_dim
									SCREAMING_SNAKE_CASE_						 = pooler_fn
									SCREAMING_SNAKE_CASE_						 = learn_encoder
									SCREAMING_SNAKE_CASE_						 = use_attention_mask







class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      [R'''pooler''', R'''logit_scale''']
							UpperCAmelCase__						     =      [R'''position_ids''', R'''predictions.decoder.bias''']
							UpperCAmelCase__						     =      '''roberta'''
							UpperCAmelCase__						     =      RobertaSeriesConfig

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = XLMRobertaModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
									SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       '''has_pre_transformation'''       ,       SCREAMING_SNAKE_CASE_    )
									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
											SCREAMING_SNAKE_CASE_						 = nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps    )
									self.post_init()







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_						 = self.base_model(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       position_ids=SCREAMING_SNAKE_CASE_       ,       head_mask=SCREAMING_SNAKE_CASE_       ,       inputs_embeds=SCREAMING_SNAKE_CASE_       ,       encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,       encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,       output_attentions=SCREAMING_SNAKE_CASE_       ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )

									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = outputs['''hidden_states'''][-2]
											SCREAMING_SNAKE_CASE_						 = self.pre_LN(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self.transformation_pre(SCREAMING_SNAKE_CASE_    )

											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = self.transformation(outputs.last_hidden_state    )
											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )