






"""simple docstring"""







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer


_UpperCamelCase					      =  logging.get_logger(__name__)

_UpperCamelCase					      =  {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_UpperCamelCase					      =  {
    """vocab_file""": {
        """yjernite/retribert-base-uncased""": (
            """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """yjernite/retribert-base-uncased""": (
            """https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

_UpperCamelCase					      =  {
    """yjernite/retribert-base-uncased""": 512,
}


_UpperCamelCase					      =  {
    """yjernite/retribert-base-uncased""": {"""do_lower_case""": True},
}







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		=      PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE		=      RetriBertTokenizer
    SCREAMING_SNAKE_CASE		=      ['''input_ids''', '''attention_mask''']






    def __init__(			self     ,A=None     ,A=None     ,A=True     ,A="[UNK]"     ,A="[SEP]"     ,A="[PAD]"     ,A="[CLS]"     ,A="[MASK]"     ,A=True     ,A=None     ,**A     ,):
          super().__init__(
              A     ,tokenizer_file=A     ,do_lower_case=A     ,unk_token=A     ,sep_token=A     ,pad_token=A     ,cls_token=A     ,mask_token=A     ,tokenize_chinese_chars=A     ,strip_accents=A     ,**A     ,)

          UpperCAmelCase          =	json.loads(self.backend_tokenizer.normalizer.__getstate__() )
          if (
              normalizer_state.get("""lowercase"""     ,A ) != do_lower_case
              or normalizer_state.get("""strip_accents"""     ,A ) != strip_accents
              or normalizer_state.get("""handle_chinese_chars"""     ,A ) != tokenize_chinese_chars
          ):
                UpperCAmelCase          =	getattr(A     ,normalizer_state.pop("""type""" ) )
                UpperCAmelCase          =	do_lower_case
                UpperCAmelCase          =	strip_accents
                UpperCAmelCase          =	tokenize_chinese_chars
                UpperCAmelCase          =	normalizer_class(**A )

          UpperCAmelCase          =	do_lower_case






    def     _UpperCamelCase							(			self     ,A     ,A=None ):
          UpperCAmelCase          =	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
                output += token_ids_a + [self.sep_token_id]

          return output






    def     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	[self.sep_token_id]
          UpperCAmelCase          =	[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     _UpperCamelCase							(			self     ,A     ,A = None ):
          UpperCAmelCase          =	self._tokenizer.model.save(A     ,name=A )
          return tuple(A )











"""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


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	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     _UpperCamelCase							(			self     ,A     ,A ):
          # 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          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

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



