




'''simple docstring'''




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__ 							=    """stas/tiny-wmt19-en-ru"""






class      UpperCamelCase_  (a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       FSMTTokenizer
						_lowerCAmelCase         =       False







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											super().setUp()

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

											A_			:  List[Any]			=				['''en''', '''ru''']
											A_			:  List[Any]			=				{
											    '''langs''': self.langs,
											    '''src_vocab_size''': 10,
											    '''tgt_vocab_size''': 20,
											}

											A_			:  Any			=				os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''src_vocab_file''']     )
											A_			:  List[Any]			=				os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''tgt_vocab_file''']     )
											A_			:  Union[str, Any]			=				os.path.join(self.tmpdirname				,			'''tokenizer_config.json'''     )
											A_			:  Any			=				os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES['''merges_file''']     )
											with open(self.src_vocab_file				,			'''w'''     ) as fp:
																fp.write(json.dumps(_lowerCamelCase     )     )
											with open(self.tgt_vocab_file				,			'''w'''     ) as fp:
																fp.write(json.dumps(_lowerCamelCase     )     )
											with open(self.merges_file				,			'''w'''     ) as fp:
																fp.write('''\n'''.join(_lowerCamelCase     )     )
											with open(_lowerCamelCase				,			'''w'''     ) as fp:
																fp.write(json.dumps(_lowerCamelCase     )     )







						@cached_property
						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







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







						@cached_property
						def    _a     (  self   :							int     ):





											"""simple docstring"""







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







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											A_			:  Any			=				FSMTTokenizer.from_pretrained(_lowerCamelCase     )
											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   :							int     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				FSMTTokenizer(self.langs				,			self.src_vocab_file				,			self.tgt_vocab_file				,			self.merges_file     )

											A_			:  str			=				'''lower'''
											A_			:  List[Any]			=				['''low''', '''er</w>''']
											A_			:  List[Any]			=				tokenizer.tokenize(_lowerCamelCase     )
											self.assertListEqual(_lowerCamelCase				,			_lowerCamelCase     )

											A_			:  Union[str, Any]			=				tokens + ['''<unk>''']
											A_			:  Union[str, Any]			=				[14, 15, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase     )				,			_lowerCamelCase     )







						@slow
						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				self.tokenizer_ru_en

											A_			:  int			=				tokenizer.encode('''sequence builders'''				,			add_special_tokens=_lowerCamelCase     )
											A_			:  List[str]			=				tokenizer.encode('''multi-sequence build'''				,			add_special_tokens=_lowerCamelCase     )

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

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







						@slow
						def    _a     (  self   :							Dict     ):





											"""simple docstring"""







											A_			:  List[Any]			=				self.tokenizer_en_ru
											A_			:  Dict			=				self.tokenizer_ru_en

											A_			:  List[Any]			=				[
											    [
											        '''Here\'s a little song I wrote. Don\'t worry, be happy.''',
											        [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 13160, 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_			:  List[Any]			=				tokenizer_enc.encode(_lowerCamelCase				,			return_tensors=_lowerCamelCase     )
																self.assertListEqual(_lowerCamelCase				,			_lowerCamelCase     )

																# and decode backward, using the reversed languages model
																A_			:  Union[str, Any]			=				tokenizer_dec.decode(_lowerCamelCase				,			skip_special_tokens=_lowerCamelCase     )
																self.assertEqual(_lowerCamelCase				,			_lowerCamelCase     )







						@slow
						def    _a     (  self   :							Dict     ):





											"""simple docstring"""







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







						@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''     )
						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											pass







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





											"""simple docstring"""







											pass









'''simple docstring'''




from itertools import permutations




def     snake_case__     (			lowerCamelCase__					: int = 5      ) -> int:
					if gon_side < 3 or gon_side > 5:
										raise ValueError('''gon_side must be in the range [3, 5]'''      )

					# Since it's 16, we know 10 is on the outer ring
					# Put the big numbers at the end so that they are never the first number
					A_			:  int			=				list(range(gon_side + 1      ,		0      ,		-1      )      )
					A_			:  List[Any]			=				list(range(gon_side + 2      ,		gon_side * 2 + 1      )      )

					for perm in permutations(small_numbers + big_numbers      ):
										A_			:  Optional[int]			=				generate_gon_ring(lowerCamelCase__      ,		list(lowerCamelCase__      )      )
										if is_magic_gon(lowerCamelCase__      ):
															return int(''''''.join(str(lowerCamelCase__      ) for n in numbers      )      )

					A_			:  Optional[Any]			=				f'Magic {gon_side}-gon ring is impossible'
					raise ValueError(lowerCamelCase__      )




def     snake_case__     (			lowerCamelCase__					: int      ,		lowerCamelCase__					: list[int]      ) -> list[int]:
					A_			:  List[Any]			=				[0] * (gon_side * 3)
					A_			:  Optional[Any]			=				perm[0:3]
					perm.append(perm[1]      )

					A_			:  Optional[Any]			=				1 if gon_side < 5 else 2

					for i in range(1      ,		len(lowerCamelCase__      ) // 3 + magic_number      ):
										A_			:  List[str]			=				perm[2 * i + 1]
										A_			:  str			=				result[3 * i - 1]
										A_			:  Union[str, Any]			=				perm[2 * i + 2]

					return result




def     snake_case__     (			lowerCamelCase__					: list[int]      ) -> bool:
					if len(lowerCamelCase__      ) % 3 != 0:
										raise ValueError('''a gon ring should have a length that is a multiple of 3'''      )

					if min(numbers[::3]      ) != numbers[0]:
										return False

					A_			:  Union[str, Any]			=				sum(numbers[:3]      )

					return all(sum(numbers[i : i + 3]      ) == total for i in range(3      ,		len(lowerCamelCase__      )      ,		3      )      )


if __name__ == "__main__":
							print(solution())



