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__	:       Optional[int]			      =      """stas/tiny-wmt19-en-ru"""







class     a__    (				UpperCAmelCase		,     unittest.TestCase    ):







  """simple docstring"""




  UpperCAmelCase__ :	int         =FSMTTokenizer
  UpperCAmelCase__ :	Union[str, Any]         =False


  def 	_lowercase (     self      :	Optional[Any]						)   ->Union[str, Any]:
        """simple docstring"""







        super().setUp()

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

        SCREAMING_SNAKE_CASE	: Any					       =				["""en""", """ru"""]
        SCREAMING_SNAKE_CASE	: Dict					       =				{
            """langs""": self.langs,
            """src_vocab_size""": 1_0,
            """tgt_vocab_size""": 2_0,
        }

        SCREAMING_SNAKE_CASE	: Tuple					       =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["""src_vocab_file"""]						)
        SCREAMING_SNAKE_CASE	: int					       =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["""tgt_vocab_file"""]						)
        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				os.path.join(self.tmpdirname			,			"""tokenizer_config.json"""						)
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES["""merges_file"""]						)
        with open(self.src_vocab_file			,			"""w"""						) as fp:
              fp.write(json.dumps(UpperCAmelCase__						)						)
        with open(self.tgt_vocab_file			,			"""w"""						) as fp:
              fp.write(json.dumps(UpperCAmelCase__						)						)
        with open(self.merges_file			,			"""w"""						) as fp:
              fp.write("""\n""".join(UpperCAmelCase__						)						)
        with open(UpperCAmelCase__			,			"""w"""						) as fp:
              fp.write(json.dumps(UpperCAmelCase__						)						)


  @cached_property
  def 	_lowercase (     self      :	Dict						)   ->Tuple:
        """simple docstring"""







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


  @cached_property
  def 	_lowercase (     self      :	Optional[int]						)   ->Optional[Any]:
        """simple docstring"""







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


  def 	_lowercase (     self      :	Optional[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				FSMTTokenizer.from_pretrained(UpperCAmelCase__						)
        self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]			,			["""en""", """ru"""]						)
        self.assertEqual(tokenizer.src_vocab_size			,			2_1						)
        self.assertEqual(tokenizer.tgt_vocab_size			,			2_1						)


  def 	_lowercase (     self      :	str						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: str					       =				FSMTTokenizer(self.langs			,			self.src_vocab_file			,			self.tgt_vocab_file			,			self.merges_file						)

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				"""lower"""
        SCREAMING_SNAKE_CASE	: Any					       =				["""low""", """er</w>"""]
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				tokenizer.tokenize(UpperCAmelCase__						)
        self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__						)

        SCREAMING_SNAKE_CASE	: int					       =				tokens + ["""<unk>"""]
        SCREAMING_SNAKE_CASE	: List[str]					       =				[1_4, 1_5, 2_0]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__						)			,			UpperCAmelCase__						)


  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: Optional[Any]					       =				self.tokenizer_ru_en

        SCREAMING_SNAKE_CASE	: Union[str, Any]					       =				tokenizer.encode("""sequence builders"""			,			add_special_tokens=UpperCAmelCase__						)
        SCREAMING_SNAKE_CASE	: List[str]					       =				tokenizer.encode("""multi-sequence build"""			,			add_special_tokens=UpperCAmelCase__						)

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

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


  @slow
  def 	_lowercase (     self      :	List[Any]						)   ->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE	: List[Any]					       =				self.tokenizer_en_ru
        SCREAMING_SNAKE_CASE	: Optional[int]					       =				self.tokenizer_ru_en

        SCREAMING_SNAKE_CASE	: int					       =				[
            [
                """Here's a little song I wrote. Don't worry, be happy.""",
                [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
            ],
            ["""This is it. No more. I'm done!""", [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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	: Tuple					       =				tokenizer_enc.encode(UpperCAmelCase__			,			return_tensors=UpperCAmelCase__						)
              self.assertListEqual(UpperCAmelCase__			,			UpperCAmelCase__						)

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


  @slow
  def 	_lowercase (     self      :	List[Any]						)   ->str:
        """simple docstring"""







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


  @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""						)
  def 	_lowercase (     self      :	Optional[Any]						)   ->str:
        """simple docstring"""







        pass




  @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""						)
  def 	_lowercase (     self      :	Optional[int]						)   ->Dict:
        """simple docstring"""







        pass







import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

UpperCAmelCase__	:       Dict			      =      [(1,)]






def    __lowercase (       )       ->  Tuple:
      SCREAMING_SNAKE_CASE	: Optional[Any]					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"






def    __lowercase (       )       ->  Optional[Any]:
      SCREAMING_SNAKE_CASE	: Dict					       =				bool(os.getenv("""RUN_SLOW"""  ,      0     )     )
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"







@require_torch
class     a__    (				unittest.TestCase    ):







  """simple docstring"""




  @slow
  @require_torch_gpu
  def 	_lowercase (     self      :	Any						)   ->Optional[Any]:
        """simple docstring"""







        check_slow_torch_cuda()


  @require_torch_gpu
  @slow
  def 	_lowercase (     self      :	Optional[Any]						)   ->Any:
        """simple docstring"""







        check_slow_torch_cuda()




  @parameterized.expand(UpperCAmelCase__						)
  @slow
  def 	_lowercase (     self      :	Optional[int]			,			UpperCAmelCase__      :	Dict=None						)   ->Optional[Any]:
        """simple docstring"""







        check_slow()






@slow
@require_torch_gpu
def    __lowercase (       )       ->  Optional[int]:
      check_slow_torch_cuda()






@require_torch_gpu
@slow
def    __lowercase (       )       ->  int:
      check_slow_torch_cuda()






@slow
@pytest.mark.parametrize("""param"""  ,      [1]     )
def    __lowercase (       _A     )       ->  Optional[int]:
      check_slow()







@pytest.mark.parametrize("""param"""  ,      [1]     )
@slow
def    __lowercase (       _A     )       ->  str:
      check_slow()




