"""simple docstring"""
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


lowercase__ 	=     logging.get_logger(__name__)




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



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Dict      							=     self.task_name.lower()







class        __snake_case	(					_lowerCAmelCase    ):
      a__				=		"""train"""
      a__				=		"""dev"""
      a__				=		"""test"""







class        __snake_case	(					_lowerCAmelCase    ):
      a__				=		42
      a__				=		42
      a__				=		42



      def __init__(						self   ,      lowercase   ,      lowercase   ,      lowercase = None   ,      lowercase = Split.train   ,      lowercase = None   ,      )     ->      int:





        '''simple docstring'''





        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'   ,      SCREAMING_SNAKE_CASE_   ,      )
        a__:					List[str]      							=     args
        a__:					Optional[int]      							=     glue_processors[args.task_name]()
        a__:					List[Any]      							=     glue_output_modes[args.task_name]
        if isinstance(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_):
          try:
            a__:					str      							=     Split[mode]
          except KeyError:
            raise KeyError('mode is not a valid split name')
        # Load data features from cache or dataset file
        a__:					Optional[int]      							=     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}'   ,      )
        a__:					List[Any]      							=     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)
          a__:					str      							=     label_list[2], label_list[1]
        a__:					Dict      							=     label_list

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





        '''simple docstring'''





        return len(self.features)



      def __getitem__(						self   ,      lowercase)     ->      InputFeatures:





        '''simple docstring'''





        return self.features[i]



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        return self.label_list



"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

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


if is_sentencepiece_available():
 from .tokenization_mbart import MBartTokenizer
else:
 lowercase__ 	=     None


lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

lowercase__ 	=     {
    'facebook/mbart-large-en-ro': 1024,
    'facebook/mbart-large-cc25': 1024,
}

# fmt: off
lowercase__ 	=     ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		PRETRAINED_VOCAB_FILES_MAP
      a__				=		["""input_ids""", """attention_mask"""]
      a__				=		MBartTokenizer

      a__				=		[]
      a__				=		[]



      def __init__(						self   ,      lowercase=None   ,      lowercase=None   ,      lowercase="<s>"   ,      lowercase="</s>"   ,      lowercase="</s>"   ,      lowercase="<s>"   ,      lowercase="<unk>"   ,      lowercase="<pad>"   ,      lowercase="<mask>"   ,      lowercase=None   ,      lowercase=None   ,      lowercase=None   ,      **lowercase   ,      )     ->      Tuple:





        '''simple docstring'''





        a__:					List[str]      							=     AddedToken(lowercase   ,      lstrip=lowercase   ,      rstrip=lowercase) if isinstance(lowercase   ,      lowercase) else mask_token

        super().__init__(
            vocab_file=lowercase   ,      tokenizer_file=lowercase   ,      bos_token=lowercase   ,      eos_token=lowercase   ,      sep_token=lowercase   ,      cls_token=lowercase   ,      unk_token=lowercase   ,      pad_token=lowercase   ,      mask_token=lowercase   ,      src_lang=lowercase   ,      tgt_lang=lowercase   ,      additional_special_tokens=lowercase   ,      **lowercase   ,      )

        a__:					Tuple      							=     vocab_file
        a__:					Union[str, Any]      							=     False if not self.vocab_file else True

        a__:					Union[str, Any]      							=     FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          _additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in _additional_special_tokens])

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens})
        a__:					int      							=     {
            lang_code: self.convert_tokens_to_ids(lowercase) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        a__:					List[Any]      							=     src_lang if src_lang is not None else 'en_XX'
        a__:					Tuple      							=     self.convert_tokens_to_ids(self._src_lang)
        a__:					str      							=     tgt_lang
        self.set_src_lang_special_tokens(self._src_lang)



      @property
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        return self._src_lang



      @src_lang.setter
      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					Tuple      							=     new_src_lang
        self.set_src_lang_special_tokens(self._src_lang)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        if token_ids_a is None:
          return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      List[int]:





        '''simple docstring'''





        a__:					Any      							=     [self.sep_token_id]
        a__:					List[str]      							=     [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]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      **lowercase)     ->      Union[str, Any]:





        '''simple docstring'''





        if src_lang is None or tgt_lang is None:
          raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model')
        a__:					Union[str, Any]      							=     src_lang
        a__:					Any      							=     self(lowercase   ,      add_special_tokens=lowercase   ,      return_tensors=lowercase   ,      **lowercase)
        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					Any      							=     tgt_lang_id
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = "en_XX"   ,      lowercase = None   ,      lowercase = "ro_RO"   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        a__:					Any      							=     src_lang
        a__:					List[Any]      							=     tgt_lang
        return super().prepare_seqaseq_batch(lowercase   ,      lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        return self.set_src_lang_special_tokens(self.src_lang)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        return self.set_tgt_lang_special_tokens(self.tgt_lang)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					int      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					List[str]      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Dict      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Any      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					int      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      None:





        '''simple docstring'''





        a__:					str      							=     self.convert_tokens_to_ids(lowercase)
        a__:					List[Any]      							=     []
        a__:					Dict      							=     [self.eos_token_id, self.cur_lang_code]

        a__:					Any      							=     self.convert_ids_to_tokens(self.prefix_tokens)
        a__:					Optional[Any]      							=     self.convert_ids_to_tokens(self.suffix_tokens)

        a__:					str      							=     processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str   ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str   ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str   ,      self.prefix_tokens + self.suffix_tokens))   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase = None)     ->      Tuple[str]:





        '''simple docstring'''





        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
        a__:					Any      							=     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,)


