



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
snake_case     :    int						=    'stas/tiny-wmt19-en-ru'




class 				lowerCAmelCase__	(						UpperCamelCase       ,				unittest.TestCase    ):
					__A  :     Optional[int]     =    FSMTTokenizer
					__A  :     Dict     =    False







					def 			_lowercase      (							self   :  List[Any]):
											super().setUp()

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

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

											A__						:				List[Any]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["src_vocab_file"])
											A__						:				Union[str, Any]         =			os.path.join(self.tmpdirname      ,		VOCAB_FILES_NAMES["tgt_vocab_file"])
											A__						:				List[Any]         =			os.path.join(self.tmpdirname      ,		"tokenizer_config.json")
											A__						:				Dict         =			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   :  List[Any]):
											return FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en")







					@cached_property
					def 			_lowercase      (							self   :  Tuple):
											return FSMTTokenizer.from_pretrained("facebook/wmt19-en-ru")







					def 			_lowercase      (							self   :  str):
											A__						:				Tuple         =			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   :  Union[str, Any]):
											A__						:				List[str]         =			FSMTTokenizer(self.langs      ,		self.src_vocab_file      ,		self.tgt_vocab_file      ,		self.merges_file)

											A__						:				str         =			"lower"
											A__						:				Optional[Any]         =			["low", "er</w>"]
											A__						:				Any         =			tokenizer.tokenize(_A)
											self.assertListEqual(_A      ,		_A)

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







					@slow
					def 			_lowercase      (							self   :  Optional[int]):
											A__						:				Union[str, Any]         =			self.tokenizer_ru_en

											A__						:				int         =			tokenizer.encode("sequence builders"      ,		add_special_tokens=_A)
											A__						:				Optional[int]         =			tokenizer.encode("multi-sequence build"      ,		add_special_tokens=_A)

											A__						:				Optional[Any]         =			tokenizer.build_inputs_with_special_tokens(_A)
											A__						:				int         =			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   :  Any):
											A__						:				Optional[int]         =			self.tokenizer_en_ru
											A__						:				int         =			self.tokenizer_ru_en

											A__						:				List[str]         =			[
											    [
											        "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:
																	A__						:				Dict         =			tokenizer_enc.encode(_A      ,		return_tensors=_A)
																	self.assertListEqual(_A      ,		_A)

																	# and decode backward, using the reversed languages model
																	A__						:				Tuple         =			tokenizer_dec.decode(_A      ,		skip_special_tokens=_A)
																	self.assertEqual(_A      ,		_A)







					@slow
					def 			_lowercase      (							self   :  List[Any]):
											A__						:				Union[str, Any]         =			FSMTTokenizer.from_pretrained("facebook/wmt19-ru-en"      ,		do_lower_case=_A)
											A__						:				str         =			tokenizer.tokenize("USA is United States of America")
											A__						:				List[str]         =			["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   :  Union[str, Any]):
											pass







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




from itertools import compress, repeat
from math import ceil, sqrt
def 						snake_case__							(       __lowercase							)		->		list[int]:







						"""simple docstring"""

						if num <= 2:
												return []
						if num == 3:
												return [2]

						# Odd sieve for numbers in range [3, num - 1]
						A__						:				Dict         =			bytearray(b"\x01"							) * ((num >> 1) - 1)

						for i in range(3				,  int(sqrt(__lowercase							)							) + 1				,  2							):
												if sieve[(i >> 1) - 1]:
																		A__						:				int         =			i**2
																		A__						:				List[str]         =			repeat(
																		    0				,  ceil((num - i_squared) / (i << 1)							)							)

						return [2] + list(compress(range(3				,  __lowercase				,  2							)				,  __lowercase							)							)


if __name__ == "__main__":
		import doctest

		doctest.testmod()