



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


class       __lowerCamelCase	(   lowerCAmelCase_   ,							unittest.TestCase    ):
   __UpperCamelCase									=   FSMTTokenizer
   __UpperCamelCase									=   False







   def 							A__						(self				):




      '''simple docstring'''




      super().setUp()

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

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

      _lowerCAmelCase					     =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["""src_vocab_file"""]				)
      _lowerCAmelCase					     =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["""tgt_vocab_file"""]				)
      _lowerCAmelCase					     =		os.path.join(self.tmpdirname       ,	"""tokenizer_config.json"""				)
      _lowerCAmelCase					     =		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				):




      '''simple docstring'''




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







   @cached_property
   def 							A__						(self				):




      '''simple docstring'''




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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		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				):




      '''simple docstring'''




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

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

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







   @slow
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.tokenizer_ru_en

      _lowerCAmelCase					     =		tokenizer.encode("""sequence builders"""       ,	add_special_tokens=__SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase					     =		tokenizer.encode("""multi-sequence build"""       ,	add_special_tokens=__SCREAMING_SNAKE_CASE				)

      _lowerCAmelCase					     =		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase					     =		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				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.tokenizer_en_ru
      _lowerCAmelCase					     =		self.tokenizer_ru_en

      _lowerCAmelCase					     =		[
          [
              """Here's a little song I wrote. Don't worry, be happy.""",
              [2_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
          ],
          ["""This is it. No more. I'm done!""", [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 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					     =		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
         _lowerCAmelCase					     =		tokenizer_dec.decode(__SCREAMING_SNAKE_CASE       ,	skip_special_tokens=__SCREAMING_SNAKE_CASE				)
         self.assertEqual(__SCREAMING_SNAKE_CASE       ,	__SCREAMING_SNAKE_CASE				)







   @slow
   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""       ,	do_lower_case=__SCREAMING_SNAKE_CASE				)
      _lowerCAmelCase					     =		tokenizer.tokenize("""USA is United States of America"""				)
      _lowerCAmelCase					     =		["""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				):




      '''simple docstring'''




      pass







   @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""				)
   def 							A__						(self				):




      '''simple docstring'''




      pass




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  float   )							->	int:






   """simple docstring"""


   return int(snake_case_   ) if x - int(snake_case_   ) <= 0 else int(snake_case_   ) + 1


if __name__ == "__main__":
  import doctest

  doctest.testmod()