


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
)
__snake_case            =   logging.getLogger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					np.argmax(SCREAMING_SNAKE_CASE_       ,  axis=1	)
			return np.sum(outputs == labels	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		Any       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]       ,  SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		Tuple	):





			"""simple docstring"""




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






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					argparse.ArgumentParser()
			parser.add_argument("""--model_name"""       ,  type=SCREAMING_SNAKE_CASE_       ,  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=SCREAMING_SNAKE_CASE_       ,  type=SCREAMING_SNAKE_CASE_       ,  required=SCREAMING_SNAKE_CASE_       ,  help="""The output directory where the model predictions and checkpoints will be written."""       ,  )
			parser.add_argument("""--train_dataset"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=""""""	)
			parser.add_argument("""--eval_dataset"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=""""""	)
			parser.add_argument("""--seed"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=42	)
			parser.add_argument("""--num_train_epochs"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=3	)
			parser.add_argument("""--train_batch_size"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=8	)
			parser.add_argument("""--eval_batch_size"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=16	)
			parser.add_argument("""--adam_epsilon"""       ,  default=1e-8       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Epsilon for Adam optimizer."""	)
			parser.add_argument("""--max_grad_norm"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=1	)
			parser.add_argument(
			    """--max_steps"""       ,  default=-1       ,  type=SCREAMING_SNAKE_CASE_       ,  help=(
			        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
			    )       ,  )
			parser.add_argument(
			    """--gradient_accumulation_steps"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=1       ,  help="""Number of updates steps to accumulate before                        performing a backward/update pass."""       ,  )
			parser.add_argument("""--learning_rate"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=6.25e-5	)
			parser.add_argument("""--warmup_steps"""       ,  default=0       ,  type=SCREAMING_SNAKE_CASE_       ,  help="""Linear warmup over warmup_steps."""	)
			parser.add_argument("""--lr_schedule"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default="""warmup_linear"""	)
			parser.add_argument("""--weight_decay"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=0.01	)
			parser.add_argument("""--lm_coef"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=0.9	)
			parser.add_argument("""--n_valid"""       ,  type=SCREAMING_SNAKE_CASE_       ,  default=374	)

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

			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=SCREAMING_SNAKE_CASE_	)
						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	)

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

			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
			UpperCamelCase         =					["_start_", "_delimiter_", "_classify_"]
			UpperCamelCase         =					OpenAIGPTTokenizer.from_pretrained(args.model_name	)
			tokenizer.add_tokens(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name	)
			model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_	)	)
			model.to(SCREAMING_SNAKE_CASE_	)

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

			logger.info("""Encoding dataset..."""	)
			UpperCamelCase         =					load_rocstories_dataset(args.train_dataset	)
			UpperCamelCase         =					load_rocstories_dataset(args.eval_dataset	)
			UpperCamelCase         =					(train_dataset, eval_dataset)
			UpperCamelCase         =					tokenize_and_encode(SCREAMING_SNAKE_CASE_	)

			# Compute the max input length for the Transformer
			UpperCamelCase         =					model.config.n_positions // 2 - 2
			UpperCamelCase         =					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	)
			UpperCamelCase         =					min(SCREAMING_SNAKE_CASE_       ,  model.config.n_positions	)  # Max size of input for the pre-trained model

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

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

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

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

						UpperCamelCase         =					list(model.named_parameters()	)
						UpperCamelCase         =					["bias", "LayerNorm.bias", "LayerNorm.weight"]
						UpperCamelCase         =					[
						    {
						        "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},
						]
						UpperCamelCase         =					AdamW(SCREAMING_SNAKE_CASE_       ,  lr=args.learning_rate       ,  eps=args.adam_epsilon	)
						UpperCamelCase         =					get_linear_schedule_with_warmup(
						    SCREAMING_SNAKE_CASE_       ,  num_warmup_steps=args.warmup_steps       ,  num_training_steps=SCREAMING_SNAKE_CASE_	)

			if args.do_train:
						UpperCamelCase         =					0, 0, None
						model.train()
						for _ in trange(int(args.num_train_epochs	)       ,  desc="""Epoch"""	):
									UpperCamelCase         =					0
									UpperCamelCase         =					0
									UpperCamelCase         =					tqdm(SCREAMING_SNAKE_CASE_       ,  desc="""Training"""	)
									for step, batch in enumerate(SCREAMING_SNAKE_CASE_	):
												UpperCamelCase         =					tuple(t.to(SCREAMING_SNAKE_CASE_	) for t in batch	)
												UpperCamelCase         =					batch
												UpperCamelCase         =					model(SCREAMING_SNAKE_CASE_       ,  mc_token_ids=SCREAMING_SNAKE_CASE_       ,  lm_labels=SCREAMING_SNAKE_CASE_       ,  mc_labels=SCREAMING_SNAKE_CASE_	)
												UpperCamelCase         =					args.lm_coef * losses[0] + losses[1]
												loss.backward()
												optimizer.step()
												scheduler.step()
												optimizer.zero_grad()
												tr_loss += loss.item()
												UpperCamelCase         =					(
												    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
												)
												nb_tr_steps += 1
												UpperCamelCase         =					"Training loss: {:.2e} lr: {:.2e}".format(SCREAMING_SNAKE_CASE_       ,  scheduler.get_lr()[0]	)

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

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

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

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

			if args.do_eval:
						model.eval()
						UpperCamelCase         =					0, 0
						UpperCamelCase         =					0, 0
						for batch in tqdm(SCREAMING_SNAKE_CASE_       ,  desc="""Evaluating"""	):
									UpperCamelCase         =					tuple(t.to(SCREAMING_SNAKE_CASE_	) for t in batch	)
									UpperCamelCase         =					batch
									with torch.no_grad():
												UpperCamelCase         =					model(
												    SCREAMING_SNAKE_CASE_       ,  mc_token_ids=SCREAMING_SNAKE_CASE_       ,  lm_labels=SCREAMING_SNAKE_CASE_       ,  mc_labels=SCREAMING_SNAKE_CASE_	)

									UpperCamelCase         =					mc_logits.detach().cpu().numpy()
									UpperCamelCase         =					mc_labels.to("""cpu"""	).numpy()
									UpperCamelCase         =					accuracy(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

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

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

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

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


if __name__ == "__main__":
							main()








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




