






"""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
__a							:						List[str]   							=			'stas/tiny-wmt19-en-ru'







class 							_SCREAMING_SNAKE_CASE    (	__snake_case							,			unittest.TestCase				):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE     =FSMTTokenizer
							_SCREAMING_SNAKE_CASE     =False




							def 			lowercase		( self:  Dict     ):

												'''simple docstring'''

												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 			lowercase		( self:  Tuple     ):

												'''simple docstring'''

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




							@cached_property
							def 			lowercase		( self:  str     ):

												'''simple docstring'''

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




							def 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												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 			lowercase		( self:  Union[str, Any]     ):

												'''simple docstring'''

												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 			lowercase		( self:  int     ):

												'''simple docstring'''

												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 			lowercase		( self:  str     ):

												'''simple docstring'''

												a__ 							=	self.tokenizer_en_ru
												a__ 							=	self.tokenizer_ru_en

												a__ 							=	[
												    [
												        '''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__ 							=	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 			lowercase		( self:  Optional[int]     ):

												'''simple docstring'''

												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 			lowercase		( self:  List[Any]     ):

												'''simple docstring'''

												pass





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

												'''simple docstring'''

												pass










"""simple docstring"""







from __future__ import annotations





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):

					# Base Case
					if counter == 0 or capacity == 0:
										return 0

					# If weight of the nth item is more than Knapsack of capacity,
					#   then this item cannot be included in the optimal solution,
					# else return the maximum of two cases:
					#   (1) nth item included
					#   (2) not included
					if weights[counter - 1] > capacity:
										return knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
					else:
										a__ 							=	capacity - weights[counter - 1]
										a__ 							=	values[counter - 1] + knapsack(
										    lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										a__ 							=	knapsack(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  counter - 1)
										return max(lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	import doctest

	doctest.testmod()


