






"""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__       :			int=						"""stas/tiny-wmt19-en-ru"""
class  __lowerCamelCase     (	_a				,						unittest.TestCase	):
   a   :					Optional[Any]			    =FSMTTokenizer
   a   :					Tuple			    =False

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       super().setUp()

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

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

       UpperCamelCase__								=     os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['src_vocab_file']   )
       UpperCamelCase__								=     os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['tgt_vocab_file']   )
       UpperCamelCase__								=     os.path.join(self.tmpdirname			,					'tokenizer_config.json'   )
       UpperCamelCase__								=     os.path.join(self.tmpdirname			,					VOCAB_FILES_NAMES['merges_file']   )
       with open(self.src_vocab_file			,					'w'   ) as fp:
           fp.write(json.dumps(snake_case_   )   )
       with open(self.tgt_vocab_file			,					'w'   ) as fp:
           fp.write(json.dumps(snake_case_   )   )
       with open(self.merges_file			,					'w'   ) as fp:
           fp.write('\n'.join(snake_case_   )   )
       with open(snake_case_			,					'w'   ) as fp:
           fp.write(json.dumps(snake_case_   )   )

   @cached_property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       return FSMTTokenizer.from_pretrained('facebook/wmt19-ru-en'   )

   @cached_property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       return FSMTTokenizer.from_pretrained('facebook/wmt19-en-ru'   )

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Optional[Any]:
       UpperCamelCase__								=     FSMTTokenizer.from_pretrained(snake_case_   )
       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   )		->							str:
       UpperCamelCase__								=     FSMTTokenizer(self.langs			,					self.src_vocab_file			,					self.tgt_vocab_file			,					self.merges_file   )

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

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

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       UpperCamelCase__								=     self.tokenizer_ru_en

       UpperCamelCase__								=     tokenizer.encode('sequence builders'			,					add_special_tokens=snake_case_   )
       UpperCamelCase__								=     tokenizer.encode('multi-sequence build'			,					add_special_tokens=snake_case_   )

       UpperCamelCase__								=     tokenizer.build_inputs_with_special_tokens(snake_case_   )
       UpperCamelCase__								=     tokenizer.build_inputs_with_special_tokens(snake_case_			,					snake_case_   )

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

   @slow
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__								=     self.tokenizer_en_ru
       UpperCamelCase__								=     self.tokenizer_ru_en

       UpperCamelCase__								=     [
           [
               '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:
           UpperCamelCase__								=     tokenizer_enc.encode(snake_case_			,					return_tensors=snake_case_   )
           self.assertListEqual(snake_case_			,					snake_case_   )

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

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

   @unittest.skip('FSMTConfig.__init__  requires non-optional args'   )
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       pass







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












"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__       :			Union[str, Any]=						logging.get_logger(__name__)

A__       :			Dict=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}
class  __lowerCamelCase     (	_a	):
   a   :					Optional[int]			    ="""rembert"""

   def __init__(       self			,					snake_case_=25_0300			,					snake_case_=1152			,					snake_case_=32			,					snake_case_=18			,					snake_case_=256			,					snake_case_=1664			,					snake_case_=4608			,					snake_case_="gelu"			,					snake_case_=0.0			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=512			,					snake_case_=2			,					snake_case_=0.02			,					snake_case_=1E-12			,					snake_case_=True			,					snake_case_=0			,					snake_case_=312			,					snake_case_=313			,					**snake_case_			,					)		->							str:
       super().__init__(pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_			,					**snake_case_   )

       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     input_embedding_size
       UpperCamelCase__								=     output_embedding_size
       UpperCamelCase__								=     max_position_embeddings
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     hidden_dropout_prob
       UpperCamelCase__								=     attention_probs_dropout_prob
       UpperCamelCase__								=     classifier_dropout_prob
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     type_vocab_size
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     use_cache
       UpperCamelCase__								=     False







class  __lowerCamelCase     (	_a	):

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
           UpperCamelCase__								=     {0: 'batch', 1: 'choice', 2: 'sequence'}
       else:
           UpperCamelCase__								=     {0: 'batch', 1: 'sequence'}
       return OrderedDict(
           [
               ('input_ids', dynamic_axis),
               ('attention_mask', dynamic_axis),
               ('token_type_ids', dynamic_axis),
           ]   )







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							float:
       return 1E-4




