






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        snake_case_				(							lowerCAmelCase__   ,      unittest.TestCase   ):







   """simple docstring"""



   __lowerCAmelCase  =    FSMTTokenizer
   __lowerCAmelCase  =    False

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Any:


       """simple docstring"""



       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(_A ,							range(len(_A						)						)						)						)
       _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(_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       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		int:


       """simple docstring"""



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

   @cached_property
   def       SCREAMING_SNAKE_CASE__					(     self:						Dict						)	->		Optional[int]:


       """simple docstring"""



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

   def       SCREAMING_SNAKE_CASE__					(     self:						List[Any]						)	->		int:


       """simple docstring"""



       _lowercase     			=						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       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						FSMTTokenizer(self.langs ,							self.src_vocab_file ,							self.tgt_vocab_file ,							self.merges_file						)

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

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

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Optional[Any]:


       """simple docstring"""



       _lowercase     			=						self.tokenizer_ru_en

       _lowercase     			=						tokenizer.encode('sequence builders' ,							add_special_tokens=_A						)
       _lowercase     			=						tokenizer.encode('multi-sequence build' ,							add_special_tokens=_A						)

       _lowercase     			=						tokenizer.build_inputs_with_special_tokens(_A						)
       _lowercase     			=						tokenizer.build_inputs_with_special_tokens(_A ,							_A						)

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

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		int:


       """simple docstring"""



       _lowercase     			=						self.tokenizer_en_ru
       _lowercase     			=						self.tokenizer_ru_en

       _lowercase     			=						[
           [
               '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:
           _lowercase     			=						tokenizer_enc.encode(_A ,							return_tensors=_A						)
           self.assertListEqual(_A ,							_A						)

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

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						List[Any]						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en' ,							do_lower_case=_A						)
       _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(_A ,							_A						)

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


       """simple docstring"""



       pass

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


       """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__	:   int								=     [
    "AutomaticSpeechRecognition",
    "AudioClassification",
    "ImageClassification",
    "LanguageModeling",
    "QuestionAnsweringExtractive",
    "Summarization",
    "TaskTemplate",
    "TextClassification",
]

UpperCamelCase__	:   Any								=     get_logger(__name__)


UpperCamelCase__	:   Union[str, Any]								=     {
    AutomaticSpeechRecognition.task: AutomaticSpeechRecognition,
    AudioClassification.task: AudioClassification,
    ImageClassification.task: ImageClassification,
    LanguageModeling.task: LanguageModeling,
    QuestionAnsweringExtractive.task: QuestionAnsweringExtractive,
    Summarization.task: Summarization,
    TextClassification.task: TextClassification,
}


def        _UpperCAmelCase							(	_SCREAMING_SNAKE_CASE       :		dict							):





    """simple docstring"""


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


