



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
)
SCREAMING_SNAKE_CASE__       =      logging.getLogger(__name__)


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      str:
			A__       =    np.argmax(__UpperCamelCase ,	axis=1					)
			return np.sum(outputs == labels					)


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


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


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

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

			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=__UpperCamelCase					)
						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					)

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

			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
			A__       =    ['_start_', '_delimiter_', '_classify_']
			A__       =    OpenAIGPTTokenizer.from_pretrained(args.model_name					)
			tokenizer.add_tokens(__UpperCamelCase					)
			A__       =    tokenizer.convert_tokens_to_ids(__UpperCamelCase					)
			A__       =    OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name					)
			model.resize_token_embeddings(len(__UpperCamelCase					)					)
			model.to(__UpperCamelCase					)

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

			logger.info('Encoding dataset...'					)
			A__       =    load_rocstories_dataset(args.train_dataset					)
			A__       =    load_rocstories_dataset(args.eval_dataset					)
			A__       =    (train_dataset, eval_dataset)
			A__       =    tokenize_and_encode(__UpperCamelCase					)

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

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

			A__       =    TensorDataset(*__UpperCamelCase					)
			A__       =    RandomSampler(__UpperCamelCase					)
			A__       =    DataLoader(__UpperCamelCase ,	sampler=__UpperCamelCase ,	batch_size=args.train_batch_size					)

			A__       =    TensorDataset(*__UpperCamelCase					)
			A__       =    SequentialSampler(__UpperCamelCase					)
			A__       =    DataLoader(__UpperCamelCase ,	sampler=__UpperCamelCase ,	batch_size=args.eval_batch_size					)

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

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

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

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

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

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

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

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

									A__       =    mc_logits.detach().cpu().numpy()
									A__       =    mc_labels.to('cpu'					).numpy()
									A__       =    accuracy(__UpperCamelCase ,	__UpperCamelCase					)

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

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

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

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


if __name__ == "__main__":
							main()









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =      {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''XLMRobertaTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      ['''XLMRobertaTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XLMRobertaForCausalLM''',
							    '''XLMRobertaForMaskedLM''',
							    '''XLMRobertaForMultipleChoice''',
							    '''XLMRobertaForQuestionAnswering''',
							    '''XLMRobertaForSequenceClassification''',
							    '''XLMRobertaForTokenClassification''',
							    '''XLMRobertaModel''',
							    '''XLMRobertaPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXLMRobertaForCausalLM''',
							    '''TFXLMRobertaForMaskedLM''',
							    '''TFXLMRobertaForMultipleChoice''',
							    '''TFXLMRobertaForQuestionAnswering''',
							    '''TFXLMRobertaForSequenceClassification''',
							    '''TFXLMRobertaForTokenClassification''',
							    '''TFXLMRobertaModel''',
							    '''TFXLMRobertaPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaxXLMRobertaForMaskedLM''',
							    '''FlaxXLMRobertaForCausalLM''',
							    '''FlaxXLMRobertaForMultipleChoice''',
							    '''FlaxXLMRobertaForQuestionAnswering''',
							    '''FlaxXLMRobertaForSequenceClassification''',
							    '''FlaxXLMRobertaForTokenClassification''',
							    '''FlaxXLMRobertaModel''',
							    '''FlaxXLMRobertaPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_xlm_roberta import (
							    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    XLMRobertaConfig,
							    XLMRobertaOnnxConfig,
							)

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta import XLMRobertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xlm_roberta import (
														    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    XLMRobertaForCausalLM,
														    XLMRobertaForMaskedLM,
														    XLMRobertaForMultipleChoice,
														    XLMRobertaForQuestionAnswering,
														    XLMRobertaForSequenceClassification,
														    XLMRobertaForTokenClassification,
														    XLMRobertaModel,
														    XLMRobertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xlm_roberta import (
														    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXLMRobertaForCausalLM,
														    TFXLMRobertaForMaskedLM,
														    TFXLMRobertaForMultipleChoice,
														    TFXLMRobertaForQuestionAnswering,
														    TFXLMRobertaForSequenceClassification,
														    TFXLMRobertaForTokenClassification,
														    TFXLMRobertaModel,
														    TFXLMRobertaPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xlm_roberta import (
														    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaxXLMRobertaForCausalLM,
														    FlaxXLMRobertaForMaskedLM,
														    FlaxXLMRobertaForMultipleChoice,
														    FlaxXLMRobertaForQuestionAnswering,
														    FlaxXLMRobertaForSequenceClassification,
														    FlaxXLMRobertaForTokenClassification,
														    FlaxXLMRobertaModel,
														    FlaxXLMRobertaPreTrainedModel,
														)

else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




