



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
__magic_name__       =     '''stas/tiny-wmt19-en-ru'''


class 	a__       (       a__  ,			unittest.TestCase       ):


					"""simple docstring"""



					A__  :				Dict					 =				FSMTTokenizer
					A__  :				Optional[Any]					 =				False

					def 				__UpperCAmelCase		(		self     :Optional[int]    ):
										super().setUp()

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

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

										lowercase						   =       os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['src_vocab_file']    )
										lowercase						   =       os.path.join(self.tmpdirname      ,  VOCAB_FILES_NAMES['tgt_vocab_file']    )
										lowercase						   =       os.path.join(self.tmpdirname      ,  'tokenizer_config.json'    )
										lowercase						   =       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 				__UpperCAmelCase		(		self     :Optional[int]    ):
										return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'    )

					@cached_property
					def 				__UpperCAmelCase		(		self     :int    ):
										return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'    )

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

										lowercase						   =       '''lower'''
										lowercase						   =       ['''low''', '''er</w>''']
										lowercase						   =       tokenizer.tokenize(_lowerCamelCase    )
										self.assertListEqual(_lowerCamelCase      ,  _lowerCamelCase    )

										lowercase						   =       tokens + ['''<unk>''']
										lowercase						   =       [14, 15, 20]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase    )      ,  _lowerCamelCase    )

					@slow
					def 				__UpperCAmelCase		(		self     :Any    ):
										lowercase						   =       self.tokenizer_ru_en

										lowercase						   =       tokenizer.encode('sequence builders'      ,  add_special_tokens=_lowerCamelCase    )
										lowercase						   =       tokenizer.encode('multi-sequence build'      ,  add_special_tokens=_lowerCamelCase    )

										lowercase						   =       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase    )
										lowercase						   =       tokenizer.build_inputs_with_special_tokens(_lowerCamelCase      ,  _lowerCamelCase    )

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

					@slow
					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       self.tokenizer_en_ru
										lowercase						   =       self.tokenizer_ru_en

										lowercase						   =       [
										    [
										        '''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:
															lowercase						   =       tokenizer_enc.encode(_lowerCamelCase      ,  return_tensors=_lowerCamelCase    )
															self.assertListEqual(_lowerCamelCase      ,  _lowerCamelCase    )

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

					@slow
					def 				__UpperCAmelCase		(		self     :Dict    ):
										lowercase						   =       FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'      ,  do_lower_case=_lowerCamelCase    )
										lowercase						   =       tokenizer.tokenize('USA is United States of America'    )
										lowercase						   =       ['''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 				__UpperCAmelCase		(		self     :List[str]    ):
										pass



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












'''simple docstring'''




import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


snake_case__ 							=    TypeVar("""T""", bound="""TaskTemplate""")






@dataclass(frozen=a__  )
class      UpperCamelCase_  (abc.ABC  ):







						"""simple docstring"""


						_lowerCAmelCase         =       42
						_lowerCAmelCase         =       42
						_lowerCAmelCase         =       42







						def    _a     (  self   :							T				,			_lowerCamelCase   :							Features     ):





											"""simple docstring"""







											return copy.deepcopy(self     )







						@property
						def    _a     (  self   :							List[str]     ):





											"""simple docstring"""







											return Features(**self.input_schema				,			**self.label_schema     )







						@property
						@abc.abstractmethod
						def    _a     (  self   :							int     ):





											"""simple docstring"""







											raise NotImplementedError







						@classmethod
						def    _a     (  cls   :							Type[T]				,			_lowerCamelCase   :							dict     ):





											"""simple docstring"""







											A_			:  Dict			=				{f.name for f in dataclasses.fields(cls     )}
											return cls(**{k: v for k, v in template_dict.items() if k in field_names}     )



