






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
lowerCAmelCase_     =			'stas/tiny-wmt19-en-ru'
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = FSMTTokenizer
		snake_case			 = False
		def    A(	self		:							Any		)     -> Optional[Any]:


						'''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(_SCREAMING_SNAKE_CASE    ,range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
						A									=   ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

						A									=   ['en', 'ru']
						A									=   {
						    'langs': self.langs,
						    'src_vocab_size': 1_0,
						    'tgt_vocab_size': 2_0,
						}

						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(_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		:							Tuple		)     -> Dict:


						'''simple docstring'''
						return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'		)
		@cached_property
		def    A(	self		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'		)
		def    A(	self		:							Tuple		)     -> int:


						'''simple docstring'''
						A									=   FSMTTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE		)
						self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]    ,['en', 'ru']		)
						self.assertEqual(tokenizer.src_vocab_size    ,2_1		)
						self.assertEqual(tokenizer.tgt_vocab_size    ,2_1		)
		def    A(	self		:							Optional[int]		)     -> List[str]:


						'''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(_SCREAMING_SNAKE_CASE		)
						self.assertListEqual(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)

						A									=   tokens + ['<unk>']
						A									=   [1_4, 1_5, 2_0]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)    ,_SCREAMING_SNAKE_CASE		)
		@slow
		def    A(	self		:							List[Any]		)     -> str:


						'''simple docstring'''
						A									=   self.tokenizer_ru_en

						A									=   tokenizer.encode('sequence builders'    ,add_special_tokens=_SCREAMING_SNAKE_CASE		)
						A									=   tokenizer.encode('multi-sequence build'    ,add_special_tokens=_SCREAMING_SNAKE_CASE		)

						A									=   tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
						A									=   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		:							List[Any]		)     -> Dict:


						'''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.',
						        [2_4_7_0, 3_9, 1_1, 2_3_4_9, 7_2_2_2, 7_0, 5_9_7_9, 7, 8_4_5_0, 1_0_5_0, 1_3_1_6_0, 5, 2_6, 6_4_4_5, 7, 2],
						    ],
						    ['This is it. No more. I\'m done!', [1_3_2, 2_1, 3_7, 7, 1_4_3_4, 8_6, 7, 7_0, 6_4_7_6, 1_3_0_5, 4_2_7, 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(_SCREAMING_SNAKE_CASE    ,return_tensors=_SCREAMING_SNAKE_CASE		)
										self.assertListEqual(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)

										# and decode backward, using the reversed languages model
										A									=   tokenizer_dec.decode(_SCREAMING_SNAKE_CASE    ,skip_special_tokens=_SCREAMING_SNAKE_CASE		)
										self.assertEqual(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
		@slow
		def    A(	self		:							Optional[int]		)     -> Optional[int]:


						'''simple docstring'''
						A									=   FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'    ,do_lower_case=_SCREAMING_SNAKE_CASE		)
						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(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		)
		@unittest.skip('FSMTConfig.__init__  requires non-optional args'		)
		def    A(	self		:							Optional[int]		)     -> List[str]:


						'''simple docstring'''
						pass







		@unittest.skip('FSMTConfig.__init__  requires non-optional args'		)
		def    A(	self		:							Union[str, Any]		)     -> str:


						'''simple docstring'''
						pass













import numpy as np







def   snake_case		(				UpperCAmelCase						:							Any ):
				A									=   np.exp(UpperCAmelCase )

				# Add up the all the exponentials
				A									=   np.sum(UpperCAmelCase )

				# Divide every exponent by the sum of all exponents
				A									=   exponent_vector / sum_of_exponents

				return softmax_vector


if __name__ == "__main__":
					print(softmax((0,)))





