





'''simple docstring'''




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


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

_A:					Optional[Any]							=				{
    """vocab_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-generator""": """https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt""",
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt"""
        ),
    },
    """tokenizer_file""": {
        """google/electra-small-generator""": (
            """https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-generator""": (
            """https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-generator""": (
            """https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json"""
        ),
        """google/electra-small-discriminator""": (
            """https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-base-discriminator""": (
            """https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json"""
        ),
        """google/electra-large-discriminator""": (
            """https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json"""
        ),
    },
}

_A:					List[str]							=				{
    """google/electra-small-generator""": 512,
    """google/electra-base-generator""": 512,
    """google/electra-large-generator""": 512,
    """google/electra-small-discriminator""": 512,
    """google/electra-base-discriminator""": 512,
    """google/electra-large-discriminator""": 512,
}

_A:					List[Any]							=				{
    """google/electra-small-generator""": {"""do_lower_case""": True},
    """google/electra-base-generator""": {"""do_lower_case""": True},
    """google/electra-large-generator""": {"""do_lower_case""": True},
    """google/electra-small-discriminator""": {"""do_lower_case""": True},
    """google/electra-base-discriminator""": {"""do_lower_case""": True},
    """google/electra-large-discriminator""": {"""do_lower_case""": True},
}






class UpperCAmelCase							(		UpperCAmelCase_							):
    _A :			Optional[int]						   =       VOCAB_FILES_NAMES
    _A :			Union[str, Any]						   =       PRETRAINED_VOCAB_FILES_MAP
    _A :			int						   =       PRETRAINED_INIT_CONFIGURATION
    _A :			Any						   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A :			Any						   =       ElectraTokenizer



    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 					__lowerCamelCase						(  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 					__lowerCamelCase						(  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 					__lowerCamelCase						(  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


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






@require_torch
class UpperCAmelCase							(		unittest.TestCase							):



    def 					__lowerCamelCase						(  self     ,			__A					):
     return FSMTTokenizer.from_pretrained(__A					)



    def 					__lowerCamelCase						(  self     ,			__A					):
     __UpperCAmelCase					=   FSMTForConditionalGeneration.from_pretrained(__A					).to(__A					)
     if torch_device == "cuda":
      model.half()
     return model




    @parameterized.expand(
        [
            ['en-ru', 2_6.0],
            ['ru-en', 2_2.0],
            ['en-de', 2_2.0],
            ['de-en', 2_9.0],
        ]					)
    @slow
    def 					__lowerCamelCase						(  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					)





