


"""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


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


@require_torch
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[Any]:
          return FSMTTokenizer.from_pretrained(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase							).to(__UpperCAmelCase							)
          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    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Any:
          # 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
          _a    	=				F'facebook/wmt19-{pair}'
          _a    	=				self.get_tokenizer(__UpperCAmelCase							)
          _a    	=				self.get_model(__UpperCAmelCase							)

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

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



"""simple docstring"""






import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__snake_case		=       logging.get_logger(__name__)
__snake_case		=       {'''vocab_file''': '''spiece.model'''}

__snake_case		=       {
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    }
}

__snake_case		=       {
    '''albert-base-v1''': 512,
    '''albert-large-v1''': 512,
    '''albert-xlarge-v1''': 512,
    '''albert-xxlarge-v1''': 512,
    '''albert-base-v2''': 512,
    '''albert-large-v2''': 512,
    '''albert-xlarge-v2''': 512,
    '''albert-xxlarge-v2''': 512,
}

__snake_case		=       '''▁'''


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		VOCAB_FILES_NAMES
     A_       :	int    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	Optional[int]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=False    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="<unk>"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="<pad>"    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[MASK]"    ,				__UpperCAmelCase = None    ,				**__UpperCAmelCase    ,				)       ->							None:
          # Mask token behave like a normal word, i.e. include the space before it and
          # is included in the raw text, there should be a match in a non-normalized sentence.
          _a    	=				(
              AddedToken(__UpperCAmelCase    ,				lstrip=__UpperCAmelCase    ,				rstrip=__UpperCAmelCase    ,				normalized=__UpperCAmelCase							)
              if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase							)
              else mask_token
          )

          _a    	=				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=__UpperCAmelCase    ,				remove_space=__UpperCAmelCase    ,				keep_accents=__UpperCAmelCase    ,				bos_token=__UpperCAmelCase    ,				eos_token=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				sp_model_kwargs=self.sp_model_kwargs    ,				**__UpperCAmelCase    ,				)

          _a    	=				do_lower_case
          _a    	=				remove_space
          _a    	=				keep_accents
          _a    	=				vocab_file

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(__UpperCAmelCase							)


     @property
     def 							_UpperCAmelCase      (      self							)       ->							str:
          return len(self.sp_model							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:
          _a    	=				{self.convert_ids_to_tokens(__UpperCAmelCase							): i for i in range(self.vocab_size							)}
          vocab.update(self.added_tokens_encoder							)
          return vocab


     def __getstate__(      self							)       ->							int:
          _a    	=				self.__dict__.copy()
          _a    	=				None
          return state


     def __setstate__(      self    ,				__UpperCAmelCase							)       ->							Tuple:
          _a    	=				d

          # for backward compatibility
          if not hasattr(self    ,				'''sp_model_kwargs'''							):
               _a    	=				{}

          _a    	=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(self.vocab_file							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          if self.remove_space:
               _a    	=				''' '''.join(inputs.strip().split()							)
          else:
               _a    	=				inputs
          _a    	=				outputs.replace('''``'''    ,				'''"'''							).replace('''\'\''''    ,				'''"'''							)

          if not self.keep_accents:
               _a    	=				unicodedata.normalize('''NFKD'''    ,				__UpperCAmelCase							)
               _a    	=				''''''.join([c for c in outputs if not unicodedata.combining(__UpperCAmelCase							)]							)
          if self.do_lower_case:
               _a    	=				outputs.lower()

          return outputs


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							List[str]:
          _a    	=				self.preprocess_text(__UpperCAmelCase							)
          _a    	=				self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase							)
          _a    	=				[]
          for piece in pieces:
               if len(__UpperCAmelCase							) > 1 and piece[-1] == str(''','''							) and piece[-2].isdigit():
                    _a    	=				self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCAmelCase    ,				''''''							)							)
                    if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                         if len(cur_pieces[0]							) == 1:
                              _a    	=				cur_pieces[1:]
                         else:
                              _a    	=				cur_pieces[0][1:]
                    cur_pieces.append(piece[-1]							)
                    new_pieces.extend(__UpperCAmelCase							)
               else:
                    new_pieces.append(__UpperCAmelCase							)

          return new_pieces


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							str:
          return self.sp_model.PieceToId(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							int:
          return self.sp_model.IdToPiece(__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase							)       ->							Optional[Any]:
          _a    	=				[]
          _a    	=				''''''
          _a    	=				False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    if not prev_is_special:
                         out_string += " "
                    out_string += self.sp_model.decode(__UpperCAmelCase							) + token
                    _a    	=				True
                    _a    	=				[]
               else:
                    current_sub_tokens.append(__UpperCAmelCase							)
                    _a    	=				False
          out_string += self.sp_model.decode(__UpperCAmelCase							)
          return out_string.strip()


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None    ,				__UpperCAmelCase = False							)       ->							List[int]:

          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__UpperCAmelCase    ,				token_ids_a=__UpperCAmelCase    ,				already_has_special_tokens=__UpperCAmelCase							)

          if token_ids_a is not None:
               return [1] + ([0] * len(__UpperCAmelCase							)) + [1] + ([0] * len(__UpperCAmelCase							)) + [1]
          return [1] + ([0] * len(__UpperCAmelCase							)) + [1]


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          if not os.path.isdir(__UpperCAmelCase							):
               logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
               return
          _a    	=				os.path.join(
              __UpperCAmelCase    ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

          if os.path.abspath(self.vocab_file							) != os.path.abspath(__UpperCAmelCase							) and os.path.isfile(self.vocab_file							):
               copyfile(self.vocab_file    ,				__UpperCAmelCase							)
          elif not os.path.isfile(self.vocab_file							):
               with open(__UpperCAmelCase    ,				'''wb'''							) as fi:
                    _a    	=				self.sp_model.serialized_model_proto()
                    fi.write(__UpperCAmelCase							)

          return (out_vocab_file,)