
'''simple docstring'''

import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
__UpperCAmelCase												=   'stas/tiny-wmt19-en-ru'


class 		_a     (							SCREAMING_SNAKE_CASE			,    unittest.TestCase  ):







   """simple docstring"""




   A		= FSMTTokenizer
   A		= False






   def 					__a			(       self      ):
    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		[
        'l',
        'o',
        'w',
        'e',
        'r',
        's',
        't',
        'i',
        'd',
        'n',
        'w</w>',
        'r</w>',
        't</w>',
        'lo',
        'low',
        'er</w>',
        'low</w>',
        'lowest</w>',
        'newer</w>',
        'wider</w>',
        '<unk>',
    ]
    SCREAMING_SNAKE_CASE  :  List[Any]								=		dict(zip(__SCREAMING_SNAKE_CASE    ,range(len(__SCREAMING_SNAKE_CASE      )      )      )      )
    SCREAMING_SNAKE_CASE  :  int								=		['l o 123', 'lo w 1456', 'e r</w> 1789', '']

    SCREAMING_SNAKE_CASE  :  str								=		['en', 'ru']
    SCREAMING_SNAKE_CASE  :  Any								=		{
        'langs': self.langs,
        'src_vocab_size': 10,
        'tgt_vocab_size': 20,
    }

    SCREAMING_SNAKE_CASE  :  Dict								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['src_vocab_file']      )
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['tgt_vocab_file']      )
    SCREAMING_SNAKE_CASE  :  Any								=		os.path.join(self.tmpdirname    ,'tokenizer_config.json'      )
    SCREAMING_SNAKE_CASE  :  int								=		os.path.join(self.tmpdirname    ,VOCAB_FILES_NAMES['merges_file']      )
    with open(self.src_vocab_file    ,'w'      ) as fp:
     fp.write(json.dumps(__SCREAMING_SNAKE_CASE      )      )
    with open(self.tgt_vocab_file    ,'w'      ) as fp:
     fp.write(json.dumps(__SCREAMING_SNAKE_CASE      )      )
    with open(self.merges_file    ,'w'      ) as fp:
     fp.write('\n'.join(__SCREAMING_SNAKE_CASE      )      )
    with open(__SCREAMING_SNAKE_CASE    ,'w'      ) as fp:
     fp.write(json.dumps(__SCREAMING_SNAKE_CASE      )      )






   @cached_property
   def 					__a			(       self      ):
    return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'      )






   @cached_property
   def 					__a			(       self      ):
    return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  List[str]								=		FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE      )
    self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]    ,['en', 'ru']      )
    self.assertEqual(tokenizer.src_vocab_size    ,21      )
    self.assertEqual(tokenizer.tgt_vocab_size    ,21      )






   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		FSMTTokenizer(self.langs    ,self.src_vocab_file    ,self.tgt_vocab_file    ,self.merges_file      )

    SCREAMING_SNAKE_CASE  :  List[str]								=		'lower'
    SCREAMING_SNAKE_CASE  :  str								=		['low', 'er</w>']
    SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE      )
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  int								=		tokens + ['<unk>']
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE      )    ,__SCREAMING_SNAKE_CASE      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.tokenizer_ru_en

    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		tokenizer.encode('sequence builders'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer.encode('multi-sequence build'    ,add_special_tokens=__SCREAMING_SNAKE_CASE      )

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

    assert encoded_sentence == text + [2]
    assert encoded_pair == text + [2] + text_a + [2]






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Any								=		self.tokenizer_en_ru
    SCREAMING_SNAKE_CASE  :  int								=		self.tokenizer_ru_en

    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		[
        [
            'Here\'s a little song I wrote. Don\'t worry, be happy.',
            [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 5, 26, 6445, 7, 2],
        ],
        ['This is it. No more. I\'m done!', [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
    ]

    # if data needs to be recreated or added, run:
    # import torch
    # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
    # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

    for src_text, tgt_input_ids in targets:
     SCREAMING_SNAKE_CASE  :  Any								=		tokenizer_enc.encode(__SCREAMING_SNAKE_CASE    ,return_tensors=__SCREAMING_SNAKE_CASE      )
     self.assertListEqual(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )

     # and decode backward, using the reversed languages model
     SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer_dec.decode(__SCREAMING_SNAKE_CASE    ,skip_special_tokens=__SCREAMING_SNAKE_CASE      )
     self.assertEqual(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   @slow
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'    ,do_lower_case=__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Dict								=		tokenizer.tokenize('USA is United States of America'      )
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		['us', 'a</w>', 'is</w>', 'un', 'i', 'ted</w>', 'st', 'ates</w>', 'of</w>', 'am', 'er', 'ica</w>']
    self.assertListEqual(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )






   @unittest.skip('FSMTConfig.__init__  requires non-optional args'      )
   def 					__a			(       self      ):
    pass






   @unittest.skip('FSMTConfig.__init__  requires non-optional args'      )
   def 					__a			(       self      ):
    pass




'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase												=   {'configuration_sew_d': ['SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWDConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __UpperCAmelCase												=   [
       'SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWDForCTC',
       'SEWDForSequenceClassification',
       'SEWDModel',
       'SEWDPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew_d import SEW_D_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWDConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew_d import (
          SEW_D_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWDForCTC,
          SEWDForSequenceClassification,
          SEWDModel,
          SEWDPreTrainedModel,
      )


else:
   import sys

   __UpperCAmelCase												=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


