



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 	__snake_case (				SCREAMING_SNAKE_CASE     ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   FSMTTokenizer
							UpperCamelCase__			: List[str]             =   False


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

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

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

											a__			 =						os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES["""src_vocab_file"""]						)
											a__			 =						os.path.join(self.tmpdirname					,					VOCAB_FILES_NAMES["""tgt_vocab_file"""]						)
											a__			 =						os.path.join(self.tmpdirname					,					"""tokenizer_config.json"""						)
											a__			 =						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 _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						):
											a__			 =						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 _a (							self						):
											a__			 =						FSMTTokenizer(self.langs					,					self.src_vocab_file					,					self.tgt_vocab_file					,					self.merges_file						)

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

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


							@slow
							def _a (							self						):
											a__			 =						self.tokenizer_ru_en

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

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

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


							@slow
							def _a (							self						):
											a__			 =						self.tokenizer_en_ru
											a__			 =						self.tokenizer_ru_en

											a__			 =						[
											    [
											        """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:
															a__			 =						tokenizer_enc.encode(a_					,					return_tensors=a_						)
															self.assertListEqual(a_					,					a_						)

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


							@slow
							def _a (							self						):
											a__			 =						FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""					,					do_lower_case=a_						)
											a__			 =						tokenizer.tokenize("""USA is United States of America"""						)
											a__			 =						["""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 _a (							self						):
											pass







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






def      A_ (		__a       :			int = 1_000						):

				"""simple docstring"""
				return sum(i for i in range(__a						) if i % 3 == 0 or i % 5 == 0						)


if __name__ == "__main__":
	print(f"""{solution() = }""")

