

'''simple docstring'''


import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import datasets
import numpy as np
import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    PreTrainedTokenizer,
    TFAutoModelForSequenceClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase = None   ,)    ->    int:
 UpperCAmelCase_		:      Union[str, Any]       	=					{}

 if train_file is not None:
  UpperCAmelCase_		:      Dict       	=					[train_file]
 if eval_file is not None:
  UpperCAmelCase_		:      Optional[Any]       	=					[eval_file]
 if test_file is not None:
  UpperCAmelCase_		:      int       	=					[test_file]

 UpperCAmelCase_		:      List[Any]       	=					datasets.load_dataset('csv'   ,data_files=UpperCamelCase  )
 UpperCAmelCase_		:      List[Any]       	=					list(ds[list(files.keys()  )[0]].features.keys()  )
 UpperCAmelCase_		:      Optional[int]       	=					features_name.pop(UpperCamelCase  )
 UpperCAmelCase_		:      Tuple       	=					list(set(ds[list(files.keys()  )[0]][label_name]  )  )
 UpperCAmelCase_		:      Tuple       	=					{label: i for i, label in enumerate(UpperCamelCase  )}
 UpperCAmelCase_		:      str       	=					tokenizer.model_input_names
 UpperCAmelCase_		:      Optional[int]       	=					{}

 if len(UpperCamelCase  ) == 1:
  for k in files.keys():
   UpperCAmelCase_		:      Dict       	=					ds[k].map(
       lambda UpperCamelCase  : tokenizer.batch_encode_plus(
           example[features_name[0]]   ,truncation=UpperCamelCase   ,max_length=UpperCamelCase   ,padding='max_length'  )   ,batched=UpperCamelCase   ,)
 elif len(UpperCamelCase  ) == 2:
  for k in files.keys():
   UpperCAmelCase_		:      Optional[Any]       	=					ds[k].map(
       lambda UpperCamelCase  : tokenizer.batch_encode_plus(
           (example[features_name[0]], example[features_name[1]])   ,truncation=UpperCamelCase   ,max_length=UpperCamelCase   ,padding='max_length'   ,)   ,batched=UpperCamelCase   ,)

 def gen_train():
  for ex in transformed_ds[datasets.Split.TRAIN]:
   UpperCAmelCase_		:      Any       	=					{k: v for k, v in ex.items() if k in input_names}
   UpperCAmelCase_		:      Optional[int]       	=					labelaid[ex[label_name]]
   yield (d, label)

 def gen_val():
  for ex in transformed_ds[datasets.Split.VALIDATION]:
   UpperCAmelCase_		:      Dict       	=					{k: v for k, v in ex.items() if k in input_names}
   UpperCAmelCase_		:      int       	=					labelaid[ex[label_name]]
   yield (d, label)

 def gen_test():
  for ex in transformed_ds[datasets.Split.TEST]:
   UpperCAmelCase_		:      List[str]       	=					{k: v for k, v in ex.items() if k in input_names}
   UpperCAmelCase_		:      Any       	=					labelaid[ex[label_name]]
   yield (d, label)

 UpperCAmelCase_		:      Optional[Any]       	=					(
     tf.data.Dataset.from_generator(
         UpperCamelCase   ,({k: tf.intaa for k in input_names}, tf.intaa)   ,({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))   ,)
     if datasets.Split.TRAIN in transformed_ds
     else None
 )

 if train_ds is not None:
  UpperCAmelCase_		:      Union[str, Any]       	=					train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN]  )  )  )

 UpperCAmelCase_		:      Optional[Any]       	=					(
     tf.data.Dataset.from_generator(
         UpperCamelCase   ,({k: tf.intaa for k in input_names}, tf.intaa)   ,({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))   ,)
     if datasets.Split.VALIDATION in transformed_ds
     else None
 )

 if val_ds is not None:
  UpperCAmelCase_		:      Dict       	=					val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION]  )  )  )

 UpperCAmelCase_		:      Union[str, Any]       	=					(
     tf.data.Dataset.from_generator(
         UpperCamelCase   ,({k: tf.intaa for k in input_names}, tf.intaa)   ,({k: tf.TensorShape([None]  ) for k in input_names}, tf.TensorShape([]  ))   ,)
     if datasets.Split.TEST in transformed_ds
     else None
 )

 if test_ds is not None:
  UpperCAmelCase_		:      int       	=					test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST]  )  )  )

 return train_ds, val_ds, test_ds, labelaid


lowerCAmelCase__										= logging.getLogger(__name__)



@dataclass
class 					lowercase						:
       _lowerCamelCase		:      int=   field(metadata={"help": "Which column contains the label"} )
       _lowerCamelCase		:      str=   field(default=a_,					metadata={"help": "The path of the training file"} )
       _lowerCamelCase		:      Optional[str]=   field(default=a_,					metadata={"help": "The path of the development file"} )
       _lowerCamelCase		:      Optional[str]=   field(default=a_,					metadata={"help": "The path of the test file"} )
       _lowerCamelCase		:      int=   field(
           default=128,					metadata={
               "help": (
                   "The maximum total input sequence length after tokenization. Sequences longer "
                   "than this will be truncated, sequences shorter will be padded."
               )
           },					)
       _lowerCamelCase		:      bool=   field(
           default=a_,					metadata={"help": "Overwrite the cached training and evaluation sets"} )



@dataclass
class 					lowercase						:
       _lowerCamelCase		:      str=   field(
           metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} )
       _lowerCamelCase		:      Optional[str]=   field(
           default=a_,					metadata={"help": "Pretrained config name or path if not the same as model_name"} )
       _lowerCamelCase		:      Optional[str]=   field(
           default=a_,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} )
       _lowerCamelCase		:      bool=   field(default=a_,					metadata={"help": "Set this flag to use fast tokenization."} )
       # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
       # or just modify its tokenizer_config.json.
       _lowerCamelCase		:      Optional[str]=   field(
           default=a_,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},					)







def 						SCREAMING_SNAKE_CASE(  )    ->    str:
 # See all possible arguments in src/transformers/training_args.py
 # or by passing the --help flag to this script.
 # We now keep distinct sets of args, for a cleaner separation of concerns.
 UpperCAmelCase_		:      str       	=					HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments)  )
 UpperCAmelCase_		:      Dict       	=					parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir  )
     and os.listdir(training_args.output_dir  )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
      ' --overwrite_output_dir to overcome.'  )

 # Setup logging
 logging.basicConfig(
     format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'   ,datefmt='%m/%d/%Y %H:%M:%S'   ,level=logging.INFO   ,)
 logger.info(
     f"""n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1  )}, """
     f"""16-bits training: {training_args.fpaa}"""  )
 logger.info(f"""Training/evaluation parameters {training_args}"""  )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 UpperCAmelCase_		:      Optional[int]       	=					AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path   ,cache_dir=model_args.cache_dir   ,)

 UpperCAmelCase_		:      List[str]       	=					get_tfds(
     train_file=data_args.train_file   ,eval_file=data_args.dev_file   ,test_file=data_args.test_file   ,tokenizer=UpperCamelCase   ,label_column_id=data_args.label_column_id   ,max_seq_length=data_args.max_seq_length   ,)

 UpperCAmelCase_		:      Dict       	=					AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path   ,num_labels=len(UpperCamelCase  )   ,labelaid=UpperCamelCase   ,idalabel={id: label for label, id in labelaid.items()}   ,finetuning_task='text-classification'   ,cache_dir=model_args.cache_dir   ,)

 with training_args.strategy.scope():
  UpperCAmelCase_		:      Any       	=					TFAutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path   ,from_pt=bool('.bin' in model_args.model_name_or_path  )   ,config=UpperCamelCase   ,cache_dir=model_args.cache_dir   ,)

 def compute_metrics(UpperCamelCase  ) -> Dict:
  UpperCAmelCase_		:      Any       	=					np.argmax(p.predictions   ,axis=1  )

  return {"acc": (preds == p.label_ids).mean()}

 # Initialize our Trainer
 UpperCAmelCase_		:      str       	=					TFTrainer(
     model=UpperCamelCase   ,args=UpperCamelCase   ,train_dataset=UpperCamelCase   ,eval_dataset=UpperCamelCase   ,compute_metrics=UpperCamelCase   ,)

 # Training
 if training_args.do_train:
  trainer.train()
  trainer.save_model()
  tokenizer.save_pretrained(training_args.output_dir  )

 # Evaluation
 UpperCAmelCase_		:      List[Any]       	=					{}
 if training_args.do_eval:
  logger.info('*** Evaluate ***'  )
  UpperCAmelCase_		:      List[Any]       	=					trainer.evaluate()
  UpperCAmelCase_		:      Dict       	=					os.path.join(training_args.output_dir   ,'eval_results.txt'  )

  with open(UpperCamelCase   ,'w'  ) as writer:
   logger.info('***** Eval results *****'  )

   for key, value in result.items():
    logger.info(f"""  {key} = {value}"""  )
    writer.write(f"""{key} = {value}\n"""  )

   results.update(UpperCamelCase  )

 return results


if __name__ == "__main__":
      main()









'''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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase  )    ->    Union[str, Any]:
	UpperCAmelCase_		:      Any       	=					np.argmax(UpperCamelCase   ,axis=1  )
	return np.sum(outputs == labels  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    List[str]:
	with open(UpperCamelCase   ,encoding='utf_8'  ) as f:
		UpperCAmelCase_		:      int       	=					csv.reader(UpperCamelCase  )
		UpperCAmelCase_		:      Tuple       	=					[]
		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 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase  )    ->    Optional[int]:
	UpperCAmelCase_		:      str       	=					[]
	for dataset in encoded_datasets:
		UpperCAmelCase_		:      str       	=					len(UpperCamelCase  )
		UpperCAmelCase_		:      str       	=					np.zeros((n_batch, 2, input_len)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch, 2)   ,dtype=np.intaa  )
		UpperCAmelCase_		:      Any       	=					np.full((n_batch, 2, input_len)   ,fill_value=-1_0_0   ,dtype=np.intaa  )
		UpperCAmelCase_		:      List[Any]       	=					np.zeros((n_batch,)   ,dtype=np.intaa  )
		for (
		    i,
		    (story, conta, conta, mc_label),
		) in enumerate(UpperCamelCase  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      List[Any]       	=					[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Tuple       	=					with_conta
			UpperCAmelCase_		:      Union[str, Any]       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      int       	=					len(UpperCamelCase  ) - 1
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Any       	=					with_conta
			UpperCAmelCase_		:      Optional[int]       	=					mc_label
		UpperCAmelCase_		:      Tuple       	=					(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 						SCREAMING_SNAKE_CASE(  )    ->    str:
	UpperCAmelCase_		:      Union[str, Any]       	=					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=4_2  )
	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=1_6  )
	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=3_7_4  )

	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.'  )
	UpperCAmelCase_		:      Optional[Any]       	=					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  )

	UpperCAmelCase_		:      Union[str, Any]       	=					torch.device('cuda' if torch.cuda.is_available() else 'cpu'  )
	UpperCAmelCase_		:      Dict       	=					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
	UpperCAmelCase_		:      Optional[int]       	=					['_start_', '_delimiter_', '_classify_']
	UpperCAmelCase_		:      Union[str, Any]       	=					OpenAIGPTTokenizer.from_pretrained(args.model_name  )
	tokenizer.add_tokens(UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					tokenizer.convert_tokens_to_ids(UpperCamelCase  )
	UpperCAmelCase_		:      Union[str, Any]       	=					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...'  )
	UpperCAmelCase_		:      List[str]       	=					load_rocstories_dataset(args.train_dataset  )
	UpperCAmelCase_		:      Optional[Any]       	=					load_rocstories_dataset(args.eval_dataset  )
	UpperCAmelCase_		:      Dict       	=					(train_dataset, eval_dataset)
	UpperCAmelCase_		:      Union[str, Any]       	=					tokenize_and_encode(UpperCamelCase  )

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

	# Prepare inputs tensors and dataloaders
	UpperCAmelCase_		:      Optional[int]       	=					pre_process_datasets(UpperCamelCase   ,UpperCamelCase   ,UpperCamelCase   ,*UpperCamelCase  )
	UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					tensor_datasets[0], tensor_datasets[1]

	UpperCAmelCase_		:      Optional[int]       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					RandomSampler(UpperCamelCase  )
	UpperCAmelCase_		:      int       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.train_batch_size  )

	UpperCAmelCase_		:      int       	=					TensorDataset(*UpperCamelCase  )
	UpperCAmelCase_		:      Optional[int]       	=					SequentialSampler(UpperCamelCase  )
	UpperCAmelCase_		:      Tuple       	=					DataLoader(UpperCamelCase   ,sampler=UpperCamelCase   ,batch_size=args.eval_batch_size  )

	# Prepare optimizer
	if args.do_train:
		if args.max_steps > 0:
			UpperCAmelCase_		:      List[str]       	=					args.max_steps
			UpperCAmelCase_		:      List[str]       	=					args.max_steps // (len(UpperCamelCase  ) // args.gradient_accumulation_steps) + 1
		else:
			UpperCAmelCase_		:      List[str]       	=					len(UpperCamelCase  ) // args.gradient_accumulation_steps * args.num_train_epochs

		UpperCAmelCase_		:      Any       	=					list(model.named_parameters()  )
		UpperCAmelCase_		:      Any       	=					['bias', 'LayerNorm.bias', 'LayerNorm.weight']
		UpperCAmelCase_		:      Dict       	=					[
		    {
		        '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_		:      Any       	=					AdamW(UpperCamelCase   ,lr=args.learning_rate   ,eps=args.adam_epsilon  )
		UpperCAmelCase_		:      List[str]       	=					get_linear_schedule_with_warmup(
		    UpperCamelCase   ,num_warmup_steps=args.warmup_steps   ,num_training_steps=UpperCamelCase  )

	if args.do_train:
		UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      List[Any]       	=					0, 0, None
		model.train()
		for _ in trange(int(args.num_train_epochs  )   ,desc='Epoch'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					0
			UpperCAmelCase_		:      Any       	=					0
			UpperCAmelCase_		:      List[Any]       	=					tqdm(UpperCamelCase   ,desc='Training'  )
			for step, batch in enumerate(UpperCamelCase  ):
				UpperCAmelCase_		:      Optional[Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Tuple       	=					batch
				UpperCAmelCase_		:      Dict       	=					model(UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )
				UpperCAmelCase_		:      int       	=					args.lm_coef * losses[0] + losses[1]
				loss.backward()
				optimizer.step()
				scheduler.step()
				optimizer.zero_grad()
				tr_loss += loss.item()
				UpperCAmelCase_		:      Dict       	=					(
				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
				)
				nb_tr_steps += 1
				UpperCAmelCase_		:      List[Any]       	=					'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
		UpperCAmelCase_		:      int       	=					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`
		UpperCAmelCase_		:      int       	=					os.path.join(args.output_dir   ,UpperCamelCase  )
		UpperCAmelCase_		:      Any       	=					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
		UpperCAmelCase_		:      int       	=					OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir  )
		UpperCAmelCase_		:      Dict       	=					OpenAIGPTTokenizer.from_pretrained(args.output_dir  )
		model.to(UpperCamelCase  )

	if args.do_eval:
		model.eval()
		UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					0, 0
		UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					0, 0
		for batch in tqdm(UpperCamelCase   ,desc='Evaluating'  ):
			UpperCAmelCase_		:      Union[str, Any]       	=					tuple(t.to(UpperCamelCase  ) for t in batch  )
			UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Any       	=					batch
			with torch.no_grad():
				UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_	,    UpperCAmelCase_		:      Union[str, Any]       	=					model(
				    UpperCamelCase   ,mc_token_ids=UpperCamelCase   ,lm_labels=UpperCamelCase   ,mc_labels=UpperCamelCase  )

			UpperCAmelCase_		:      Optional[Any]       	=					mc_logits.detach().cpu().numpy()
			UpperCAmelCase_		:      int       	=					mc_labels.to('cpu'  ).numpy()
			UpperCAmelCase_		:      Optional[int]       	=					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

		UpperCAmelCase_		:      str       	=					eval_loss / nb_eval_steps
		UpperCAmelCase_		:      str       	=					eval_accuracy / nb_eval_examples
		UpperCAmelCase_		:      List[Any]       	=					tr_loss / nb_tr_steps if args.do_train else None
		UpperCAmelCase_		:      Dict       	=					{'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

		UpperCAmelCase_		:      Union[str, Any]       	=					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()






