



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






class   UpperCAmelCase__		(   UpperCamelCase ,unittest.TestCase  ):
		lowerCAmelCase_							:						List[str]							=  FSMTTokenizer
		lowerCAmelCase_							:						Dict							=  False



		def        A_		(			self	:     str					)      ->    str:






									'''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        A_		(			self	:     Optional[Any]					)      ->    Any:






									'''simple docstring'''

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



		@cached_property
		def        A_		(			self	:     Dict					)      ->    List[str]:






									'''simple docstring'''

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



		def        A_		(			self	:     List[str]					)      ->    Optional[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        A_		(			self	:     Tuple					)      ->    Optional[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        A_		(			self	:     List[Any]					)      ->    Optional[Any]:






									'''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        A_		(			self	:     List[str]					)      ->    Any:






									'''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_470, 39, 11, 2_349, 7_222, 70, 5_979, 7, 8_450, 1_050, 13_160, 5, 26, 6_445, 7, 2],
									    ],
									    ['''This is it. No more. I\'m done!''', [132, 21, 37, 7, 1_434, 86, 7, 70, 6_476, 1_305, 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        A_		(			self	:     int					)      ->    List[Any]:






									'''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        A_		(			self	:     List[Any]					)      ->    List[str]:






									'''simple docstring'''

									pass



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






									'''simple docstring'''

									pass







import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


a					:				Optional[Any]       =      logging.get_logger(__name__)

class   lowerCamelCase						(	SCREAMING_SNAKE_CASE  ):
       _UpperCamelCase     		=   """hybrid-clip"""
       _UpperCamelCase     		=   True

       def __init__(      self						,    a__=5_12						,    **a__       ):






        '''simple docstring'''






        super().__init__(**a__       )

        if "text_config" not in kwargs:
         raise ValueError('''`text_config` can not be `None`.'''       )

        if "vision_config" not in kwargs:
         raise ValueError('''`vision_config` can not be `None`.'''       )

        snake_case :			int							=   kwargs.pop('''text_config'''       )
        snake_case :			Optional[int]							=   kwargs.pop('''vision_config'''       )

        snake_case :			Optional[Any]							=   text_config.pop('''model_type'''       )
        snake_case :			str							=   vision_config.pop('''model_type'''       )

        from transformers import AutoConfig

        snake_case :			List[Any]							=   AutoConfig.for_model(a__						,    **a__       )

        if vision_model_type == "clip":
         snake_case :			Dict							=   AutoConfig.for_model(a__						,    **a__       ).vision_config
        elif vision_model_type == "clip_vision_model":
         from transformers import CLIPVisionConfig

         snake_case :			str							=   CLIPVisionConfig(**a__       )
        else:
         snake_case :			Any							=   AutoConfig.for_model(a__						,    **a__       )

        snake_case :			Optional[Any]							=   projection_dim
        snake_case :			int							=   1.0

       @classmethod
       def 					UpperCAmelCase					(      cls						,    a__						,    a__						,    **a__       ):






        '''simple docstring'''






        return cls(text_config=text_config.to_dict()						,    vision_config=vision_config.to_dict()						,    **a__       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Dict							=   copy.deepcopy(self.__dict__       )
        snake_case :			Dict							=   self.text_config.to_dict()
        snake_case :			int							=   self.vision_config.to_dict()
        snake_case :			List[Any]							=   self.__class__.model_type
        return output






