


"""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
UpperCAmelCase											=	'''stas/tiny-wmt19-en-ru'''
class     __magic_name__						(	__UpperCAmelCase	,  unittest.TestCase   ):
       __A	:					int					     =      FSMTTokenizer
       __A	:					List[Any]					     =      False






       def        __snake_case  (      self     :			int ):

              '''simple docstring'''






              super().setUp()

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

              lowercase						:Dict											= ['en', 'ru']
              lowercase						:Union[str, Any]											= {
                  'langs': self.langs,
                  'src_vocab_size': 1_0,
                  'tgt_vocab_size': 2_0,
              }

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






       @cached_property
       def        __snake_case  (      self     :			List[str] ):

              '''simple docstring'''






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






       @cached_property
       def        __snake_case  (      self     :			Any ):

              '''simple docstring'''






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






       def        __snake_case  (      self     :			Dict ):

              '''simple docstring'''






              lowercase						:Dict											= FSMTTokenizer.from_pretrained(__lowercase )
              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        __snake_case  (      self     :			int ):

              '''simple docstring'''






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

              lowercase						:int											= 'lower'
              lowercase						:List[Any]											= ['low', 'er</w>']
              lowercase						:int											= tokenizer.tokenize(__lowercase )
              self.assertListEqual(__lowercase     ,   __lowercase )

              lowercase						:Optional[int]											= tokens + ['<unk>']
              lowercase						:str											= [1_4, 1_5, 2_0]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase )     ,   __lowercase )






       @slow
       def        __snake_case  (      self     :			Dict ):

              '''simple docstring'''






              lowercase						:List[Any]											= self.tokenizer_ru_en

              lowercase						:Optional[int]											= tokenizer.encode('''sequence builders'''     ,   add_special_tokens=__lowercase )
              lowercase						:int											= tokenizer.encode('''multi-sequence build'''     ,   add_special_tokens=__lowercase )

              lowercase						:Optional[Any]											= tokenizer.build_inputs_with_special_tokens(__lowercase )
              lowercase						:int											= tokenizer.build_inputs_with_special_tokens(__lowercase     ,   __lowercase )

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






       @slow
       def        __snake_case  (      self     :			List[str] ):

              '''simple docstring'''






              lowercase						:Any											= self.tokenizer_en_ru
              lowercase						:Optional[Any]											= self.tokenizer_ru_en

              lowercase						:str											= [
                  [
                      '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:
                     lowercase						:Optional[Any]											= tokenizer_enc.encode(__lowercase     ,   return_tensors=__lowercase )
                     self.assertListEqual(__lowercase     ,   __lowercase )

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






       @slow
       def        __snake_case  (      self     :			Union[str, Any] ):

              '''simple docstring'''






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






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

              '''simple docstring'''






              pass







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

              '''simple docstring'''






              pass







from typing import Optional

from ..utils.logging import get_logger
from .audio_classificiation import AudioClassification
from .automatic_speech_recognition import AutomaticSpeechRecognition
from .base import TaskTemplate
from .image_classification import ImageClassification
from .language_modeling import LanguageModeling
from .question_answering import QuestionAnsweringExtractive
from .summarization import Summarization
from .text_classification import TextClassification


UpperCamelCase__              =						[
    'AutomaticSpeechRecognition',
    'AudioClassification',
    'ImageClassification',
    'LanguageModeling',
    'QuestionAnsweringExtractive',
    'Summarization',
    'TaskTemplate',
    'TextClassification',
]

UpperCamelCase__              =						get_logger(__name__)


UpperCamelCase__              =						{
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}






def 					snake_case					(							A			)							->							Optional[TaskTemplate]:




	'''simple docstring'''


	lowerCamelCase_							:		Any							   =							task_template_dict.get('task'			)
	if task_name is None:
		logger.warning(f"Couldn't find template for task '{task_name}'. Available templates: {list(A			)}"			)
		return None
	lowerCamelCase_							:		Any							   =							NAMEaTEMPLATE.get(A			)
	return template.from_dict(A			)
