



"""simple docstring"""




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_big_bird import BigBirdTokenizer
else:
 _lowerCAmelCase    : List[Any]							=							None

_lowerCAmelCase    : List[Any]							=							logging.get_logger(__name__)
_lowerCAmelCase    : Optional[Any]							=							{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase    : Any							=							{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

_lowerCAmelCase    : int							=							{
    "google/bigbird-roberta-base": 40_96,
    "google/bigbird-roberta-large": 40_96,
    "google/bigbird-base-trivia-itc": 40_96,
}


_lowerCAmelCase    : List[str]							=							"▁"






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       int								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       List[str]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Dict								=			BigBirdTokenizer
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			["""input_ids""", """attention_mask"""]
      __SCREAMING_SNAKE_CASE						:       List[int]								=			[]



      def __init__(					self					:     Dict					,     A					:     List[str]=None					,     A					:     List[str]=None					,     A					:     Any="<unk>"					,     A					:     Optional[Any]="<s>"					,     A					:     Union[str, Any]="</s>"					,     A					:     int="<pad>"					,     A					:     List[str]="[SEP]"					,     A					:     Dict="[MASK]"					,     A					:     str="[CLS]"					,     **A					:     List[str]					,     ):
           _UpperCAmelCase      :     List[str]        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else bos_token
           _UpperCAmelCase      :     Dict        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else eos_token
           _UpperCAmelCase      :     List[str]        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else unk_token
           _UpperCAmelCase      :     Optional[int]        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else pad_token
           _UpperCAmelCase      :     int        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else cls_token
           _UpperCAmelCase      :     Any        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           _UpperCAmelCase      :     Optional[int]        =   AddedToken(_A					,     lstrip=_A					,     rstrip=_A						) if isinstance(_A					,     _A						) else mask_token

           super().__init__(
               _A					,     tokenizer_file=_A					,     bos_token=_A					,     eos_token=_A					,     unk_token=_A					,     sep_token=_A					,     pad_token=_A					,     cls_token=_A					,     mask_token=_A					,     **_A					,     )

           _UpperCAmelCase      :     List[Any]        =   vocab_file
           _UpperCAmelCase      :     Any        =   False if not self.vocab_file else True



      def 						snake_case_ (					self					:     str					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Optional[int]        =   [self.sep_token_id]
           _UpperCAmelCase      :     int        =   [self.cls_token_id]
           if token_ids_a is None:
                return cls + token_ids_a + sep
           return cls + token_ids_a + sep + token_ids_a + sep



      def 						snake_case_ (					self					:     Tuple					,     A					:     List[int]					,     A					:     Optional[List[int]] = None					,     A					:     bool = False						):

           if already_has_special_tokens:
                if token_ids_a is not None:
                     raise ValueError(
                         "You should not supply a second sequence if the provided sequence of "
                         "ids is already formatted with special tokens for the model."						)
                return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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



      def 						snake_case_ (					self					:     str					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Dict        =   [self.sep_token_id]
           _UpperCAmelCase      :     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						) * [0] + len(token_ids_a + sep						) * [1]



      def 						snake_case_ (					self					:     List[Any]					,     A					:     str					,     A					:     Optional[str] = None						):
           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(_A						):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
                return
           _UpperCAmelCase      :     Optional[Any]        =   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						):
                copyfile(self.vocab_file					,     _A						)

           return (out_vocab_file,)









"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_lowerCAmelCase    : Union[str, Any]							=							get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
 _lowerCAmelCase    : Tuple							=							json.load(f)






@require_torch
class      UpperCAmelCase_  (    unittest.TestCase						):



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           return FSMTTokenizer.from_pretrained(A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     List[Any]        =   FSMTForConditionalGeneration.from_pretrained(A						).to(A						)
           if torch_device == "cuda":
                model.half()
           return model



      @parameterized.expand(
          [
              ["en-ru", 26.0],
              ["ru-en", 22.0],
              ["en-de", 22.0],
              ["de-en", 29.0],
          ]						)
      @slow
      def 						snake_case_ (					self					:     Any					,     A					:     Dict					,     A					:     List[str]						):
           # note: this test is not testing the best performance since it only evals a small batch
           # but it should be enough to detect a regression in the output quality
           _UpperCAmelCase      :     Any        =   f'facebook/wmt19-{pair}'
           _UpperCAmelCase      :     Dict        =   self.get_tokenizer(A						)
           _UpperCAmelCase      :     Optional[int]        =   self.get_model(A						)

           _UpperCAmelCase      :     int        =   bleu_data[pair]["src"]
           _UpperCAmelCase      :     Optional[int]        =   bleu_data[pair]["tgt"]

           _UpperCAmelCase      :     List[str]        =   tokenizer(A					,     return_tensors="pt"					,     truncation=A					,     padding="longest"						).to(A						)
           _UpperCAmelCase      :     List[str]        =   model.generate(
               input_ids=batch.input_ids					,     num_beams=8					,     )
           _UpperCAmelCase      :     Any        =   tokenizer.batch_decode(
               A					,     skip_special_tokens=A					,     clean_up_tokenization_spaces=A						)
           _UpperCAmelCase      :     Any        =   calculate_bleu(A					,     A						)
           print(A						)
           self.assertGreaterEqual(scores["bleu"]					,     A						)
