





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


lowerCAmelCase       		=      logging.get_logger(__name__)
@dataclass
class 		lowerCamelCase  :
	snake_case_      =   field(metadata={"help": "The name of the task to train on: " + ", ".join(glue_processors.keys()       )}       )
	snake_case_      =   field(
	    metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}       )
	snake_case_      =   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."
	        )
	    }      ,   )
	snake_case_      =   field(
	    default=UpperCamelCase__      ,   metadata={"help": "Overwrite the cached training and evaluation sets"}       )



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[Any]						=   self.task_name.lower()






class 		lowerCamelCase  (      UpperCamelCase__       ):
	snake_case_      =   "train"
	snake_case_      =   "dev"
	snake_case_      =   "test"






class 		lowerCamelCase  (      UpperCamelCase__       ):
	snake_case_      =   42
	snake_case_      =   42
	snake_case_      =   42



	def __init__(   self		,      a_		,      a_		,      a_ = None		,      a_ = Split.train		,      a_ = 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"		,      __A		,      )
			lowerCAmelCase     :    Union[str, Any]						=   args
			lowerCAmelCase     :    Dict						=   glue_processors[args.task_name]()
			lowerCAmelCase     :    int						=   glue_output_modes[args.task_name]
			if isinstance(__A		,      __A	):
					try:
							lowerCAmelCase     :    Any						=   Split[mode]
					except KeyError:
							raise KeyError("mode is not a valid split name"	)
        # Load data features from cache or dataset file
			lowerCAmelCase     :    Union[str, Any]						=   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}'''		,      )
			lowerCAmelCase     :    Dict						=   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)
					lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   label_list[2], label_list[1]
			lowerCAmelCase     :    Tuple						=   label_list

			# Make sure only the first process in distributed training processes the dataset,
			# and the others will use the cache.
			lowerCAmelCase     :    Any						=   cached_features_file + ".lock"
			with FileLock(__A	):
					if os.path.exists(__A	) and not args.overwrite_cache:
							lowerCAmelCase     :    List[str]						=   time.time()
							lowerCAmelCase     :    int						=   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:
									lowerCAmelCase     :    Optional[int]						=   self.processor.get_dev_examples(args.data_dir	)
							elif mode == Split.test:
									lowerCAmelCase     :    str						=   self.processor.get_test_examples(args.data_dir	)
							else:
									lowerCAmelCase     :    List[Any]						=   self.processor.get_train_examples(args.data_dir	)
							if limit_length is not None:
									lowerCAmelCase     :    Union[str, Any]						=   examples[:limit_length]
							lowerCAmelCase     :    str						=   glue_convert_examples_to_features(
							    __A		,      __A		,      max_length=args.max_seq_length		,      label_list=__A		,      output_mode=self.output_mode		,      )
							lowerCAmelCase     :    Optional[int]						=   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	):
			return len(self.features	)



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





	def   _lowerCamelCase    (   self	):
			return self.label_list










'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase       		=      logging.get_logger(__name__)

lowerCAmelCase       		=      """▁"""

lowerCAmelCase       		=      {"""vocab_file""": """sentencepiece.bpe.model"""}

lowerCAmelCase       		=      {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

lowerCAmelCase       		=      {
    """xlm-roberta-base""": 5_12,
    """xlm-roberta-large""": 5_12,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_12,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_12,
    """xlm-roberta-large-finetuned-conll03-english""": 5_12,
    """xlm-roberta-large-finetuned-conll03-german""": 5_12,
}
class 		lowerCamelCase  (      _A       ):
	snake_case_      =   VOCAB_FILES_NAMES
	snake_case_      =   PRETRAINED_VOCAB_FILES_MAP
	snake_case_      =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_      =   ["input_ids", "attention_mask"]



	def __init__(   self		,      a_		,      a_="<s>"		,      a_="</s>"		,      a_="</s>"		,      a_="<s>"		,      a_="<unk>"		,      a_="<pad>"		,      a_="<mask>"		,      a_ = None		,      **a_		,      ):
			# Mask token behave like a normal word, i.e. include the space before it
			lowerCAmelCase     :    str						=   AddedToken(a_		,      lstrip=a_		,      rstrip=a_	) if isinstance(a_		,      a_	) else mask_token

			lowerCAmelCase     :    Tuple						=   {} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=a_		,      eos_token=a_		,      unk_token=a_		,      sep_token=a_		,      cls_token=a_		,      pad_token=a_		,      mask_token=a_		,      sp_model_kwargs=self.sp_model_kwargs		,      **a_		,      )

			lowerCAmelCase     :    Optional[int]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
			self.sp_model.Load(str(a_	)	)
			lowerCAmelCase     :    int						=   vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			lowerCAmelCase     :    Any						=   {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			lowerCAmelCase     :    Dict						=   1

			lowerCAmelCase     :    Optional[Any]						=   len(self.sp_model	) + self.fairseq_offset
			lowerCAmelCase     :    List[Any]						=   {v: k for k, v in self.fairseq_tokens_to_ids.items()}



	def __getstate__(   self	):
			lowerCAmelCase     :    Any						=   self.__dict__.copy()
			lowerCAmelCase     :    Optional[int]						=   None
			lowerCAmelCase     :    List[str]						=   self.sp_model.serialized_model_proto()
			return state



	def __setstate__(   self		,      a_	):
			lowerCAmelCase     :    Any						=   d

			# for backward compatibility
			if not hasattr(self		,      "sp_model_kwargs"	):
					lowerCAmelCase     :    Any						=   {}

			lowerCAmelCase     :    Optional[Any]						=   spm.SentencePieceProcessor(**self.sp_model_kwargs	)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowerCAmelCase     :    Optional[Any]						=   [self.cls_token_id]
			lowerCAmelCase     :    List[Any]						=   [self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None		,      a_ = False	):

			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=a_		,      token_ids_a=a_		,      already_has_special_tokens=a_	)

			if token_ids_a is None:
					return [1] + ([0] * len(a_	)) + [1]
			return [1] + ([0] * len(a_	)) + [1, 1] + ([0] * len(a_	)) + [1]



	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			lowerCAmelCase     :    Dict						=   [self.sep_token_id]
			lowerCAmelCase     :    List[Any]						=   [self.cls_token_id]

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



	@property
	def   _lowerCamelCase    (   self	):
			return len(self.sp_model	) + self.fairseq_offset + 1  # Add the <mask> token



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   {self.convert_ids_to_tokens(a_	): i for i in range(self.vocab_size	)}
			vocab.update(self.added_tokens_encoder	)
			return vocab



	def   _lowerCamelCase    (   self		,      a_	):
			return self.sp_model.encode(a_		,      out_type=a_	)



	def   _lowerCamelCase    (   self		,      a_	):
			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			lowerCAmelCase     :    int						=   self.sp_model.PieceToId(a_	)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



	def   _lowerCamelCase    (   self		,      a_	):
			if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset	)



	def   _lowerCamelCase    (   self		,      a_	):
			lowerCAmelCase     :    Dict						=   "".join(a_	).replace(a_		,      " "	).strip()
			return out_string





	def   _lowerCamelCase    (   self		,      a_		,      a_ = None	):
			if not os.path.isdir(a_	):
					logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
					return
			lowerCAmelCase     :    int						=   os.path.join(
			    a_		,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

			if os.path.abspath(self.vocab_file	) != os.path.abspath(a_	) and os.path.isfile(self.vocab_file	):
					copyfile(self.vocab_file		,      a_	)
			elif not os.path.isfile(self.vocab_file	):
					with open(a_		,      "wb"	) as fi:
							lowerCAmelCase     :    Dict						=   self.sp_model.serialized_model_proto()
							fi.write(a_	)

			return (out_vocab_file,)



