



import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union

import torch
from filelock import FileLock
from torch.utils.data import Dataset

from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features


lowerCAmelCase__       =  logging.get_logger(__name__)

lowerCAmelCase__       =  list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
lowerCAmelCase__       =  tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)

@dataclass
class       snake_case__:

					"""simple docstring"""






					lowercase_								=		field(
					    default=A_				,			metadata={"""help""": """Model type selected in the list: """ + """, """.join(A_			)}			)
					lowercase_								=		field(
					    default=A_				,			metadata={"""help""": """The input data dir. Should contain the .json files for the SQuAD task."""}			)
					lowercase_								=		field(
					    default=1_2_8				,			metadata={
					        """help""": (
					            """The maximum total input sequence length after tokenization. Sequences longer """
					            """than this will be truncated, sequences shorter will be padded."""
					        )
					    }				,			)
					lowercase_								=		field(
					    default=1_2_8				,			metadata={"""help""": """When splitting up a long document into chunks, how much stride to take between chunks."""}				,			)
					lowercase_								=		field(
					    default=6_4				,			metadata={
					        """help""": (
					            """The maximum number of tokens for the question. Questions longer than this will """
					            """be truncated to this length."""
					        )
					    }				,			)
					lowercase_								=		field(
					    default=3_0				,			metadata={
					        """help""": (
					            """The maximum length of an answer that can be generated. This is needed because the start """
					            """and end predictions are not conditioned on one another."""
					        )
					    }				,			)
					lowercase_								=		field(
					    default=A_				,			metadata={"""help""": """Overwrite the cached training and evaluation sets"""}			)
					lowercase_								=		field(
					    default=A_				,			metadata={"""help""": """If true, the SQuAD examples contain some that do not have an answer."""}			)
					lowercase_								=		field(
					    default=0.0				,			metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}			)
					lowercase_								=		field(
					    default=2_0				,			metadata={"""help""": """If null_score - best_non_null is greater than the threshold predict null."""}			)
					lowercase_								=		field(
					    default=0				,			metadata={
					        """help""": (
					            """language id of input for language-specific xlm models (see"""
					            """ tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)"""
					        )
					    }				,			)
					lowercase_								=		field(default=1				,			metadata={"""help""": """multiple threads for converting example to features"""}			)






class       snake_case__(A_			):

					"""simple docstring"""






					lowercase_								=		"train"
					lowercase_								=		"dev"






class       snake_case__(A_			):

					"""simple docstring"""






					lowercase_								=		42
					lowercase_								=		42
					lowercase_								=		42
					lowercase_								=		42







					def __init__(							self					:	str      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any]      ,   SCREAMING_SNAKE_CASE					:	List[Any]      ,   SCREAMING_SNAKE_CASE					:	List[Any] = None      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any] = Split.train      ,   SCREAMING_SNAKE_CASE					:	Union[str, Any] = False      ,   SCREAMING_SNAKE_CASE					:	List[Any] = None      ,   SCREAMING_SNAKE_CASE					:	Tuple = "pt"      ,   ):
									lowercase__     :    Optional[Any]													=     args
									lowercase__     :    Optional[int]													=     is_language_sensitive
									lowercase__     :    Union[str, Any]													=     SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
									if isinstance(A_      ,   A_     ):
													try:
																	lowercase__     :    Optional[int]													=     Split[mode]
													except KeyError:
																	raise KeyError("mode is not a valid split name"     )
									lowercase__     :    Optional[Any]													=     mode
									# Load data features from cache or dataset file
									lowercase__     :    str													=     "v2" if args.version_2_with_negative else "v1"
									lowercase__     :    Tuple													=     os.path.join(
									    cache_dir if cache_dir is not None else args.data_dir      ,   f"""cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}"""      ,   )

									# Make sure only the first process in distributed training processes the dataset,
									# and the others will use the cache.
									lowercase__     :    Any													=     cached_features_file + ".lock"
									with FileLock(A_     ):
													if os.path.exists(A_     ) and not args.overwrite_cache:
																	lowercase__     :    int													=     time.time()
																	lowercase__     :    List[Any]													=     torch.load(A_     )

																	# Legacy cache files have only features, while new cache files
																	# will have dataset and examples also.
																	lowercase__     :    Any													=     self.old_features["features"]
																	lowercase__     :    str													=     self.old_features.get("dataset"      ,   A_     )
																	lowercase__     :    Union[str, Any]													=     self.old_features.get("examples"      ,   A_     )
																	logger.info(
																	    f"""Loading features from cached file {cached_features_file} [took %.3f s]"""      ,   time.time() - start     )

																	if self.dataset is None or self.examples is None:
																					logger.warning(
																					    f"""Deleting cached file {cached_features_file} will allow dataset and examples to be cached in"""
																					    " future run"     )
													else:
																	if mode == Split.dev:
																					lowercase__     :    Union[str, Any]													=     self.processor.get_dev_examples(args.data_dir     )
																	else:
																					lowercase__     :    Tuple													=     self.processor.get_train_examples(args.data_dir     )

																	lowercase__							,							lowercase__     :    Optional[Any]													=     squad_convert_examples_to_features(
																	    examples=self.examples      ,   tokenizer=A_      ,   max_seq_length=args.max_seq_length      ,   doc_stride=args.doc_stride      ,   max_query_length=args.max_query_length      ,   is_training=mode == Split.train      ,   threads=args.threads      ,   return_dataset=A_      ,   )

																	lowercase__     :    List[Any]													=     time.time()
																	torch.save(
																	    {"features": self.features, "dataset": self.dataset, "examples": self.examples}      ,   A_      ,   )
																	# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
																	logger.info(
																	    f"""Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]"""     )







					def __len__(							self					:	str     ):
									return len(self.features     )







					def __getitem__(							self					:	Tuple      ,   SCREAMING_SNAKE_CASE					:	Optional[int]     ):
									# Convert to Tensors and build dataset
									lowercase__     :    Union[str, Any]													=     self.features[i]

									lowercase__     :    Union[str, Any]													=     torch.tensor(feature.input_ids      ,   dtype=torch.long     )
									lowercase__     :    str													=     torch.tensor(feature.attention_mask      ,   dtype=torch.long     )
									lowercase__     :    Optional[int]													=     torch.tensor(feature.token_type_ids      ,   dtype=torch.long     )
									lowercase__     :    Union[str, Any]													=     torch.tensor(feature.cls_index      ,   dtype=torch.long     )
									lowercase__     :    List[str]													=     torch.tensor(feature.p_mask      ,   dtype=torch.float     )
									lowercase__     :    List[str]													=     torch.tensor(feature.is_impossible      ,   dtype=torch.float     )

									lowercase__     :    List[Any]													=     {
									    "input_ids": input_ids,
									    "attention_mask": attention_mask,
									    "token_type_ids": token_type_ids,
									}

									if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
													del inputs["token_type_ids"]

									if self.args.model_type in ["xlnet", "xlm"]:
													inputs.update({"cls_index": cls_index, "p_mask": p_mask}     )
													if self.args.version_2_with_negative:
																	inputs.update({"is_impossible": is_impossible}     )
													if self.is_language_sensitive:
																	inputs.update({"langs": (torch.ones(input_ids.shape      ,   dtype=torch.intaa     ) * self.args.lang_id)}     )

									if self.mode == Split.train:
													lowercase__     :    Optional[Any]													=     torch.tensor(feature.start_position      ,   dtype=torch.long     )
													lowercase__     :    Tuple													=     torch.tensor(feature.end_position      ,   dtype=torch.long     )
													inputs.update({"start_positions": start_positions, "end_positions": end_positions}     )

									return inputs








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
)
_A		       =						logging.getLogger(__name__)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     __UpperCamelCase    							=np.argmax(SCREAMING_SNAKE_CASE__			,     axis=1				)
     return np.sum(outputs == labels				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[Any]				):
     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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Dict				):
     __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=-1_00			,     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 						_UpperCAmelCase				(			):
     __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=3_74				)

     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__			:					str				):
          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							,      __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							,      __UpperCamelCase							,      __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							,      __UpperCamelCase							,      __UpperCamelCase							,      __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							,      __UpperCamelCase    							=0, 0
          __UpperCamelCase							,      __UpperCamelCase    							=0, 0
          for batch in tqdm(SCREAMING_SNAKE_CASE__			,     desc='Evaluating'				):
               __UpperCamelCase    							=tuple(t.to(SCREAMING_SNAKE_CASE__				) for t in batch				)
               __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=batch
               with torch.no_grad():
                    __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __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()





