


'''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
__lowercase:						Optional[Any]  		=   "stas/tiny-wmt19-en-ru"







class        UpperCAmelCase       (				UpperCamelCase__						,	unittest.TestCase):
 _lowerCamelCase       :    Optional[Any]			    =    FSMTTokenizer
 _lowerCamelCase       :    Optional[int]			    =    False

 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCamelCase__         =  [
        "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>",
    ]
    UpperCamelCase__         =  dict(zip(a_,    range(len(a_    )    )    )    )
    UpperCamelCase__         =  ["l o 123", "lo w 1456", "e r</w> 1789", ""]

    UpperCamelCase__         =  ["en", "ru"]
    UpperCamelCase__         =  {
        "langs": self.langs,
        "src_vocab_size": 10,
        "tgt_vocab_size": 20,
    }

    UpperCamelCase__         =  os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES["src_vocab_file"]    )
    UpperCamelCase__         =  os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES["tgt_vocab_file"]    )
    UpperCamelCase__         =  os.path.join(self.tmpdirname,    "tokenizer_config.json"    )
    UpperCamelCase__         =  os.path.join(self.tmpdirname,    VOCAB_FILES_NAMES["merges_file"]    )
    with open(self.src_vocab_file,    "w"    ) as fp:
       fp.write(json.dumps(a_    )    )
    with open(self.tgt_vocab_file,    "w"    ) as fp:
       fp.write(json.dumps(a_    )    )
    with open(self.merges_file,    "w"    ) as fp:
       fp.write("\n".join(a_    )    )
    with open(a_,    "w"    ) as fp:
       fp.write(json.dumps(a_    )    )

 @cached_property
 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"    )

 @cached_property
 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru"    )

 def 						lowercase_  (       self       :		Dict    ):





    """simple docstring"""



    UpperCamelCase__         =  FSMTTokenizer.from_pretrained(a_    )
    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 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  FSMTTokenizer(self.langs,    self.src_vocab_file,    self.tgt_vocab_file,    self.merges_file    )

    UpperCamelCase__         =  "lower"
    UpperCamelCase__         =  ["low", "er</w>"]
    UpperCamelCase__         =  tokenizer.tokenize(a_    )
    self.assertListEqual(a_,    a_    )

    UpperCamelCase__         =  tokens + ["<unk>"]
    UpperCamelCase__         =  [14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(a_    ),    a_    )

 @slow
 def 						lowercase_  (       self       :		Any    ):





    """simple docstring"""



    UpperCamelCase__         =  self.tokenizer_ru_en

    UpperCamelCase__         =  tokenizer.encode("sequence builders",    add_special_tokens=a_    )
    UpperCamelCase__         =  tokenizer.encode("multi-sequence build",    add_special_tokens=a_    )

    UpperCamelCase__         =  tokenizer.build_inputs_with_special_tokens(a_    )
    UpperCamelCase__         =  tokenizer.build_inputs_with_special_tokens(a_,    a_    )

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

 @slow
 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



    UpperCamelCase__         =  self.tokenizer_en_ru
    UpperCamelCase__         =  self.tokenizer_ru_en

    UpperCamelCase__         =  [
        [
            "Here's a little song I wrote. Don't worry, be happy.",
            [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 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:
       UpperCamelCase__         =  tokenizer_enc.encode(a_,    return_tensors=a_    )
       self.assertListEqual(a_,    a_    )

       # and decode backward, using the reversed languages model
       UpperCamelCase__         =  tokenizer_dec.decode(a_,    skip_special_tokens=a_    )
       self.assertEqual(a_,    a_    )

 @slow
 def 						lowercase_  (       self       :		Optional[Any]    ):





    """simple docstring"""



    UpperCamelCase__         =  FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en",    do_lower_case=a_    )
    UpperCamelCase__         =  tokenizer.tokenize("USA is United States of America"    )
    UpperCamelCase__         =  ["us", "a</w>", "is</w>", "un", "i", "ted</w>", "st", "ates</w>", "of</w>", "am", "er", "ica</w>"]
    self.assertListEqual(a_,    a_    )

 @unittest.skip("FSMTConfig.__init__  requires non-optional args"    )
 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



    pass

 @unittest.skip("FSMTConfig.__init__  requires non-optional args"    )
 def 						lowercase_  (       self       :		List[str]    ):





    """simple docstring"""



    pass





def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     list:
     a      =      []
     a      =      1

     for i in range(len(__UpperCamelCase)):
          if i + 1 < len(__UpperCamelCase) and text[i] == text[i + 1]:
               count += 1
          else:
               encoded.append((text[i], count))
               a      =      1

     return encoded

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase)			->     str:
     return "".join(char * length for char, length in encoded)


if __name__ == "__main__":
   from doctest import testmod

   testmod(name="run_length_encode", verbose=True)
   testmod(name="run_length_decode", verbose=True)

