

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

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

from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures


_UpperCAmelCase	:     List[Any]		       = logging.get_logger(__name__)







@dataclass
class 				lowercase       :
	__SCREAMING_SNAKE_CASE      :				str     =    field(metadata={'''help''': '''The name of the task to train on: ''' + ''', '''.join(glue_processors.keys()    )}    )
	__SCREAMING_SNAKE_CASE      :				str     =    field(
	    metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}    )
	__SCREAMING_SNAKE_CASE      :				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.'''
	        )
	    }			,						)
	__SCREAMING_SNAKE_CASE      :				bool     =    field(
	    default=lowercase_			,						metadata={'''help''': '''Overwrite the cached training and evaluation sets'''}    )




	def 					a							(      self ):
								snake_case_           =					self.task_name.lower()







class 				lowercase       ( lowercase_    ):
	__SCREAMING_SNAKE_CASE      :				List[str]     =    '''train'''
	__SCREAMING_SNAKE_CASE      :				str     =    '''dev'''
	__SCREAMING_SNAKE_CASE      :				Optional[int]     =    '''test'''







class 				lowercase       ( lowercase_    ):
	__SCREAMING_SNAKE_CASE      :				GlueDataTrainingArguments
	__SCREAMING_SNAKE_CASE      :				str
	__SCREAMING_SNAKE_CASE      :				List[InputFeatures]




	def __init__(      self   ,     snake_case   ,     snake_case   ,     snake_case = None   ,     snake_case = Split.train   ,     snake_case = None   ,     ):
								warnings.warn(
								    'This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets '
								    'library. You can have a look at this example script for pointers: '
								    'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'   ,     snake_case   ,     )
								snake_case_           =					args
								snake_case_           =					glue_processors[args.task_name]()
								snake_case_           =					glue_output_modes[args.task_name]
								if isinstance(snake_case   ,     snake_case ):
															try:
																						snake_case_           =					Split[mode]
															except KeyError:
																						raise KeyError('mode is not a valid split name' )
        # Load data features from cache or dataset file
								snake_case_           =					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}_{args.task_name}'''   ,     )
								snake_case_           =					self.processor.get_labels()
								if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
								    "RobertaTokenizer",
								    "RobertaTokenizerFast",
								    "XLMRobertaTokenizer",
								    "BartTokenizer",
								    "BartTokenizerFast",
								):
															# HACK(label indices are swapped in RoBERTa pretrained model)
															snake_case_       ,					snake_case_           =					label_list[2], label_list[1]
								snake_case_           =					label_list

								# Make sure only the first process in distributed training processes the dataset,
								# and the others will use the cache.
								snake_case_           =					cached_features_file + '.lock'
								with FileLock(snake_case ):
															if os.path.exists(snake_case ) and not args.overwrite_cache:
																						snake_case_           =					time.time()
																						snake_case_           =					torch.load(snake_case )
																						logger.info(
																						    F'''Loading features from cached file {cached_features_file} [took %.3f s]'''   ,     time.time() - start )
															else:
																						logger.info(F'''Creating features from dataset file at {args.data_dir}''' )

																						if mode == Split.dev:
																													snake_case_           =					self.processor.get_dev_examples(args.data_dir )
																						elif mode == Split.test:
																													snake_case_           =					self.processor.get_test_examples(args.data_dir )
																						else:
																													snake_case_           =					self.processor.get_train_examples(args.data_dir )
																						if limit_length is not None:
																													snake_case_           =					examples[:limit_length]
																						snake_case_           =					glue_convert_examples_to_features(
																						    snake_case   ,     snake_case   ,     max_length=args.max_seq_length   ,     label_list=snake_case   ,     output_mode=self.output_mode   ,     )
																						snake_case_           =					time.time()
																						torch.save(self.features   ,     snake_case )
																						# ^ 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 ):
								return len(self.features )




	def __getitem__(      self   ,     snake_case ):
								return self.features[i]




	def 					a							(      self ):
								return self.label_list







import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class 				lowercase       ( lowercase_			,						unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				int     =    PhobertTokenizer
				__SCREAMING_SNAKE_CASE      :				int     =    False




				def 					a							(      self ):
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											snake_case_           =					['T@@', 'i', 'I', 'R@@', 'r', 'e@@']
											snake_case_           =					dict(zip(snake_case   ,     range(len(snake_case ) ) ) )
											snake_case_           =					['#version: 0.2', 'l à</w>']
											snake_case_           =					{'unk_token': '<unk>'}

											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['vocab_file'] )
											snake_case_           =					os.path.join(self.tmpdirname   ,     VOCAB_FILES_NAMES['merges_file'] )

											with open(self.vocab_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		for token in vocab_tokens:
																									fp.write(F'''{token} {vocab_tokens[token]}\n''' )
											with open(self.merges_file   ,     'w'   ,     encoding='utf-8' ) as fp:
																		fp.write('\n'.join(snake_case ) )




				def 					a							(      self   ,     **snake_case ):
											kwargs.update(self.special_tokens_map )
											return PhobertTokenizer.from_pretrained(self.tmpdirname   ,     **snake_case )




				def 					a							(      self   ,     snake_case ):
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'
											return input_text, output_text




				def 					a							(      self ):
											snake_case_           =					PhobertTokenizer(self.vocab_file   ,     self.merges_file   ,     **self.special_tokens_map )
											snake_case_           =					'Tôi là VinAI Research'
											snake_case_           =					'T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'.split()
											snake_case_           =					tokenizer.tokenize(snake_case )
											print(snake_case )
											self.assertListEqual(snake_case   ,     snake_case )

											snake_case_           =					tokens + [tokenizer.unk_token]

											snake_case_           =					[4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case )   ,     snake_case )




