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




class 						__snake_case						(			__lowerCAmelCase			,	unittest.TestCase		):





      '''simple docstring'''






      _snake_case     		=							FSMTTokenizer
      _snake_case     		=							False



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Dict:


          """simple docstring"""


          super().setUp()

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

          _lowerCamelCase  :		int			    =    ["""en""", """ru"""]
          _lowerCamelCase  :		Optional[Any]			    =    {
              """langs""": self.langs,
              """src_vocab_size""": 10,
              """tgt_vocab_size""": 20,
          }

          _lowerCamelCase  :		int			    =    os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES["""src_vocab_file"""])
          _lowerCamelCase  :		int			    =    os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES["""tgt_vocab_file"""])
          _lowerCamelCase  :		List[str]			    =    os.path.join(self.tmpdirname     ,	"""tokenizer_config.json""")
          _lowerCamelCase  :		List[Any]			    =    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 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->int:


          """simple docstring"""


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



      @cached_property
      def 						_SCREAMING_SNAKE_CASE     (       self  :					List[Any])					->Dict:


          """simple docstring"""


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



      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    FSMTTokenizer.from_pretrained(_UpperCamelCase)
          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 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		List[Any]			    =    FSMTTokenizer(self.langs     ,	self.src_vocab_file     ,	self.tgt_vocab_file     ,	self.merges_file)

          _lowerCamelCase  :		List[str]			    =    """lower"""
          _lowerCamelCase  :		int			    =    ["""low""", """er</w>"""]
          _lowerCamelCase  :		List[str]			    =    tokenizer.tokenize(_UpperCamelCase)
          self.assertListEqual(_UpperCamelCase     ,	_UpperCamelCase)

          _lowerCamelCase  :		List[str]			    =    tokens + ["""<unk>"""]
          _lowerCamelCase  :		List[str]			    =    [14, 15, 20]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase)     ,	_UpperCamelCase)



      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->List[str]:


          """simple docstring"""


          _lowerCamelCase  :		int			    =    self.tokenizer_ru_en

          _lowerCamelCase  :		int			    =    tokenizer.encode("""sequence builders"""     ,	add_special_tokens=_UpperCamelCase)
          _lowerCamelCase  :		int			    =    tokenizer.encode("""multi-sequence build"""     ,	add_special_tokens=_UpperCamelCase)

          _lowerCamelCase  :		int			    =    tokenizer.build_inputs_with_special_tokens(_UpperCamelCase)
          _lowerCamelCase  :		Union[str, Any]			    =    tokenizer.build_inputs_with_special_tokens(_UpperCamelCase     ,	_UpperCamelCase)

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



      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Tuple)					->Tuple:


          """simple docstring"""


          _lowerCamelCase  :		Any			    =    self.tokenizer_en_ru
          _lowerCamelCase  :		Dict			    =    self.tokenizer_ru_en

          _lowerCamelCase  :		Optional[int]			    =    [
              [
                  """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:
              _lowerCamelCase  :		Optional[Any]			    =    tokenizer_enc.encode(_UpperCamelCase     ,	return_tensors=_UpperCamelCase)
              self.assertListEqual(_UpperCamelCase     ,	_UpperCamelCase)

              # and decode backward, using the reversed languages model
              _lowerCamelCase  :		int			    =    tokenizer_dec.decode(_UpperCamelCase     ,	skip_special_tokens=_UpperCamelCase)
              self.assertEqual(_UpperCamelCase     ,	_UpperCamelCase)



      @slow
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase  :		Tuple			    =    FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""     ,	do_lower_case=_UpperCamelCase)
          _lowerCamelCase  :		List[Any]			    =    tokenizer.tokenize("""USA is United States of America""")
          _lowerCamelCase  :		List[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 						_SCREAMING_SNAKE_CASE     (       self  :					Dict)					->Tuple:


          """simple docstring"""


          pass




      @unittest.skip("""FSMTConfig.__init__  requires non-optional args""")
      def 						_SCREAMING_SNAKE_CASE     (       self  :					str)					->Dict:


          """simple docstring"""


          pass





lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




