
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


_A						      =				logging.get_logger(__name__)







@dataclass
class   lowerCamelCase	:
     UpperCAmelCase__       :							str     =	field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()						)}						)
     UpperCAmelCase__       :							str     =	field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
     UpperCAmelCase__       :							int     =	field(
         default=1_28							,				metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }							,				)
     UpperCAmelCase__       :							bool     =	field(
         default=lowercase_							,				metadata={"help": "Overwrite the cached training and evaluation sets"}						)





     def 	UpperCAmelCase(self   :					Tuple     )		->		str:
         snake_case				  =      self.task_name.lower()







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







class   lowerCamelCase	(						lowercase_						):
     UpperCAmelCase__       :							GlueDataTrainingArguments
     UpperCAmelCase__       :							str
     UpperCAmelCase__       :							List[InputFeatures]





     def __init__(self   :					List[str]       ,					_A   :					int       ,					_A   :					List[Any]       ,					_A   :					Optional[int] = None       ,					_A   :					Optional[Any] = Split.train       ,					_A   :					Any = None       ,					)		->		List[Any]:
         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"       ,					_A       ,					)
         snake_case				  =      args
         snake_case				  =      glue_processors[args.task_name]()
         snake_case				  =      glue_output_modes[args.task_name]
         if isinstance(_A       ,					_A     ):
             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(_A     ):
             if os.path.exists(_A     ) and not args.overwrite_cache:
                 snake_case				  =      time.time()
                 snake_case				  =      torch.load(_A     )
                 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(
                     _A       ,					_A       ,					max_length=args.max_seq_length       ,					label_list=_A       ,					output_mode=self.output_mode       ,					)
                 snake_case				  =      time.time()
                 torch.save(self.features       ,					_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   :					List[Any]     )		->		Optional[Any]:
         return len(self.features     )





     def __getitem__(self   :					List[Any]       ,					_A   :					List[str]     )		->		int:
         return self.features[i]





     def 	UpperCAmelCase(self   :					List[Any]     )		->		Any:
         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   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							str     =	PhobertTokenizer
     UpperCAmelCase__       :							List[Any]     =	False





     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		List[str]:
         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(_A       ,					range(len(_A     )     )     )     )
         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(_A     )     )





     def 	UpperCAmelCase(self   :					Optional[int]       ,					**_A   :					Optional[int]     )		->		Union[str, Any]:
         kwargs.update(self.special_tokens_map     )
         return PhobertTokenizer.from_pretrained(self.tmpdirname       ,					**_A     )





     def 	UpperCAmelCase(self   :					Tuple       ,					_A   :					Optional[Any]     )		->		str:
         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 	UpperCAmelCase(self   :					Any     )		->		Union[str, Any]:
         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(_A     )
         print(_A     )
         self.assertListEqual(_A       ,					_A     )

         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(_A     )       ,					_A     )

