





'''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


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

_SCREAMING_SNAKE_CASE									=				{
    "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"
        ),
    },
}

_SCREAMING_SNAKE_CASE									=				{
    "google/electra-small-generator": 5_12,
    "google/electra-base-generator": 5_12,
    "google/electra-large-generator": 5_12,
    "google/electra-small-discriminator": 5_12,
    "google/electra-base-discriminator": 5_12,
    "google/electra-large-discriminator": 5_12,
}

_SCREAMING_SNAKE_CASE									=				{
    "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  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Dict								=       VOCAB_FILES_NAMES
 __lowerCamelCase    :				Any								=       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :				List[str]								=       PRETRAINED_INIT_CONFIGURATION
 __lowerCamelCase    :				List[str]								=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :				List[Any]								=       ElectraTokenizer




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

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

     _lowerCAmelCase						      =   do_lower_case




 def 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase=None		)     ->				str:
     _lowerCAmelCase						      =   [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 				_snake_case (						self      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				List[int]:
     _lowerCAmelCase						      =   [self.sep_token_id]
     _lowerCAmelCase						      =   [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      ,  _lowerCAmelCase      ,  _lowerCAmelCase = None		)     ->				Tuple[str]:
     _lowerCAmelCase						      =   self._tokenizer.model.save(_lowerCAmelCase      ,  name=_lowerCAmelCase		)
     return tuple(_lowerCAmelCase		)




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







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

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


