


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
)
_UpperCAmelCase			:  Optional[int]     =logging.getLogger(__name__)



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ):
				lowerCAmelCase_ :      Optional[Any]    				=				np.argmax(SCREAMING_SNAKE_CASE_ ,   axis=1 )
				return np.sum(outputs == labels )



def 			lowerCAmelCase (		lowerCAmelCase_ ):

				with open(SCREAMING_SNAKE_CASE_ ,   encoding='''utf_8''' ) as f:
								lowerCAmelCase_ :      int    				=				csv.reader(SCREAMING_SNAKE_CASE_ )
								lowerCAmelCase_ :      List[Any]    				=				[]
								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 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ):
				lowerCAmelCase_ :      List[str]    				=				[]
				for dataset in encoded_datasets:
								lowerCAmelCase_ :      int    				=				len(SCREAMING_SNAKE_CASE_ )
								lowerCAmelCase_ :      str    				=				np.zeros((n_batch, 2, input_len) ,   dtype=np.intaa )
								lowerCAmelCase_ :      Union[str, Any]    				=				np.zeros((n_batch, 2) ,   dtype=np.intaa )
								lowerCAmelCase_ :      Dict    				=				np.full((n_batch, 2, input_len) ,   fill_value=-100 ,   dtype=np.intaa )
								lowerCAmelCase_ :      int    				=				np.zeros((n_batch,) ,   dtype=np.intaa )
								for (
								    i,
								    (story, conta, conta, mc_label),
								) in enumerate(SCREAMING_SNAKE_CASE_ ):
												lowerCAmelCase_ :      List[str]    				=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												lowerCAmelCase_ :      Union[str, Any]    				=				[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
												lowerCAmelCase_ :      str    				=				with_conta
												lowerCAmelCase_ :      int    				=				with_conta
												lowerCAmelCase_ :      Tuple    				=				len(SCREAMING_SNAKE_CASE_ ) - 1
												lowerCAmelCase_ :      List[str]    				=				len(SCREAMING_SNAKE_CASE_ ) - 1
												lowerCAmelCase_ :      List[str]    				=				with_conta
												lowerCAmelCase_ :      Optional[Any]    				=				with_conta
												lowerCAmelCase_ :      Optional[int]    				=				mc_label
								lowerCAmelCase_ :      Optional[Any]    				=				(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 			lowerCAmelCase (		):
				lowerCAmelCase_ :      List[str]    				=				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=6.2_5e-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.''' )
				lowerCAmelCase_ :      Optional[int]    				=				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 )

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

				# Compute the max input length for the Transformer
				lowerCAmelCase_ :      List[str]    				=				model.config.n_positions // 2 - 2
				lowerCAmelCase_ :      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 )
				lowerCAmelCase_ :      str    				=				min(SCREAMING_SNAKE_CASE_ ,   model.config.n_positions )  # Max size of input for the pre-trained model

				# Prepare inputs tensors and dataloaders
				lowerCAmelCase_ :      Union[str, Any]    				=				pre_process_datasets(SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   SCREAMING_SNAKE_CASE_ ,   *SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase_ :      List[Any]    				=				tensor_datasets[0], tensor_datasets[1]

				lowerCAmelCase_ :      Optional[Any]    				=				TensorDataset(*SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase_ :      Any    				=				RandomSampler(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase_ :      str    				=				DataLoader(SCREAMING_SNAKE_CASE_ ,   sampler=SCREAMING_SNAKE_CASE_ ,   batch_size=args.train_batch_size )

				lowerCAmelCase_ :      Any    				=				TensorDataset(*SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase_ :      Tuple    				=				SequentialSampler(SCREAMING_SNAKE_CASE_ )
				lowerCAmelCase_ :      Tuple    				=				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:
												lowerCAmelCase_ :      str    				=				args.max_steps
												lowerCAmelCase_ :      Any    				=				args.max_steps // (len(SCREAMING_SNAKE_CASE_ ) // args.gradient_accumulation_steps) + 1
								else:
												lowerCAmelCase_ :      Any    				=				len(SCREAMING_SNAKE_CASE_ ) // args.gradient_accumulation_steps * args.num_train_epochs

								lowerCAmelCase_ :      Optional[int]    				=				list(model.named_parameters() )
								lowerCAmelCase_ :      Any    				=				["bias", "LayerNorm.bias", "LayerNorm.weight"]
								lowerCAmelCase_ :      Optional[Any]    				=				[
								    {
								        "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},
								]
								lowerCAmelCase_ :      Union[str, Any]    				=				AdamW(SCREAMING_SNAKE_CASE_ ,   lr=args.learning_rate ,   eps=args.adam_epsilon )
								lowerCAmelCase_ :      List[Any]    				=				get_linear_schedule_with_warmup(
								    SCREAMING_SNAKE_CASE_ ,   num_warmup_steps=args.warmup_steps ,   num_training_steps=SCREAMING_SNAKE_CASE_ )

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

				if args.do_eval:
								model.eval()
								lowerCAmelCase_ :      Optional[Any]    				=				0, 0
								lowerCAmelCase_ :      Optional[int]    				=				0, 0
								for batch in tqdm(SCREAMING_SNAKE_CASE_ ,   desc='''Evaluating''' ):
												lowerCAmelCase_ :      Tuple    				=				tuple(t.to(SCREAMING_SNAKE_CASE_ ) for t in batch )
												lowerCAmelCase_ :      Optional[Any]    				=				batch
												with torch.no_grad():
																lowerCAmelCase_ :      Tuple    				=				model(
																    SCREAMING_SNAKE_CASE_ ,   mc_token_ids=SCREAMING_SNAKE_CASE_ ,   lm_labels=SCREAMING_SNAKE_CASE_ ,   mc_labels=SCREAMING_SNAKE_CASE_ )

												lowerCAmelCase_ :      Tuple    				=				mc_logits.detach().cpu().numpy()
												lowerCAmelCase_ :      List[Any]    				=				mc_labels.to('''cpu''' ).numpy()
												lowerCAmelCase_ :      Union[str, Any]    				=				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

								lowerCAmelCase_ :      Optional[Any]    				=				eval_loss / nb_eval_steps
								lowerCAmelCase_ :      List[Any]    				=				eval_accuracy / nb_eval_examples
								lowerCAmelCase_ :      int    				=				tr_loss / nb_tr_steps if args.do_train else None
								lowerCAmelCase_ :      Tuple    				=				{"eval_loss": eval_loss, "eval_accuracy": eval_accuracy, "train_loss": train_loss}

								lowerCAmelCase_ :      Optional[Any]    				=				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 os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_xlnet import XLNetTokenizer
else:
						_UpperCAmelCase			:  Dict     =None


_UpperCAmelCase			:  Tuple     =logging.get_logger(__name__)

_UpperCAmelCase			:  Any     ={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase			:  Any     ={
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase			:  Dict     ={
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

_UpperCAmelCase			:  Tuple     ="""▁"""

# Segments (not really needed)
_UpperCAmelCase			:  str     =0
_UpperCAmelCase			:  List[str]     =1
_UpperCAmelCase			:  int     =2
_UpperCAmelCase			:  Any     =3
_UpperCAmelCase			:  List[Any]     =4







class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  int					=							VOCAB_FILES_NAMES
						SCREAMING_SNAKE_CASE__      :  Dict					=							PRETRAINED_VOCAB_FILES_MAP
						SCREAMING_SNAKE_CASE__      :  Optional[Any]					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						SCREAMING_SNAKE_CASE__      :  Any					=							"""left"""
						SCREAMING_SNAKE_CASE__      :  List[Any]					=							XLNetTokenizer




						def __init__(				self   ,						__lowercase=None   ,						__lowercase=None   ,						__lowercase=False   ,						__lowercase=True   ,						__lowercase=False   ,						__lowercase="<s>"   ,						__lowercase="</s>"   ,						__lowercase="<unk>"   ,						__lowercase="<sep>"   ,						__lowercase="<pad>"   ,						__lowercase="<cls>"   ,						__lowercase="<mask>"   ,						__lowercase=["<eop>", "<eod>"]   ,						**__lowercase   ,						)					->    List[Any]:
										# Mask token behave like a normal word, i.e. include the space before it
										lowerCAmelCase_ :      Any    				=				AddedToken(__lowercase   ,						lstrip=__lowercase   ,						rstrip=__lowercase							) if isinstance(__lowercase   ,						__lowercase							) else mask_token

										super().__init__(
										    vocab_file=__lowercase   ,						tokenizer_file=__lowercase   ,						do_lower_case=__lowercase   ,						remove_space=__lowercase   ,						keep_accents=__lowercase   ,						bos_token=__lowercase   ,						eos_token=__lowercase   ,						unk_token=__lowercase   ,						sep_token=__lowercase   ,						pad_token=__lowercase   ,						cls_token=__lowercase   ,						mask_token=__lowercase   ,						additional_special_tokens=__lowercase   ,						**__lowercase   ,						)

										lowerCAmelCase_ :      List[Any]    				=				3
										lowerCAmelCase_ :      Dict    				=				do_lower_case
										lowerCAmelCase_ :      Dict    				=				remove_space
										lowerCAmelCase_ :      List[str]    				=				keep_accents
										lowerCAmelCase_ :      List[str]    				=				vocab_file
										lowerCAmelCase_ :      str    				=				False if not self.vocab_file else True




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    List[int]:
										lowerCAmelCase_ :      Tuple    				=				[self.sep_token_id]
										lowerCAmelCase_ :      Any    				=				[self.cls_token_id]
										if token_ids_a is None:
														return token_ids_a + sep + cls
										return token_ids_a + sep + token_ids_a + sep + cls




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    List[int]:
										lowerCAmelCase_ :      Optional[Any]    				=				[self.sep_token_id]
										lowerCAmelCase_ :      List[Any]    				=				[2]

										if token_ids_a is None:
														return len(token_ids_a + sep							) * [0] + cls_segment_id
										return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id




						def 						lowercase_ (				self   ,						__lowercase   ,						__lowercase = None							)					->    Tuple[str]:
										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''							)

										if not os.path.isdir(__lowercase							):
														logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
														return
										lowerCAmelCase_ :      str    				=				os.path.join(
										    __lowercase   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

										if os.path.abspath(self.vocab_file							) != os.path.abspath(__lowercase							):
														copyfile(self.vocab_file   ,						__lowercase							)

										return (out_vocab_file,)