




"""simple docstring"""



import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
      from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
      SCREAMING_SNAKE_CASE    =	get_tests_dir("fixtures/test_sentencepiece.model")


if is_torch_available():
      from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

SCREAMING_SNAKE_CASE    =	12_8022
SCREAMING_SNAKE_CASE    =	12_8028




@require_sentencepiece
class      UpperCAmelCase_  (						lowercase__,       unittest.TestCase							):
    lowercase__						 =				MaMaaaTokenizer
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				True




    def 							__magic_name__							(   self						:	str				)				->							Tuple:




     '''simple docstring'''

     super().setUp()

     A__	=    ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
     A__	=    dict(zip(_a     ,			range(len(_a				)				)				)				)
     A__	=    Path(self.tmpdirname				)
     save_json(_a     ,			save_dir / VOCAB_FILES_NAMES["vocab_file"]				)
     if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
      copyfile(_a     ,			save_dir / VOCAB_FILES_NAMES["spm_file"]				)

     A__	=    MaMaaaTokenizer.from_pretrained(self.tmpdirname				)
     tokenizer.save_pretrained(self.tmpdirname				)




    def 							__magic_name__							(   self						:	Any     ,			**snake_case_						:	Optional[int]				)				->							Dict:




     '''simple docstring'''

     return MaMaaaTokenizer.from_pretrained(self.tmpdirname     ,			**_a				)




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	int				)				->							Tuple:




     '''simple docstring'''

     return (
         "This is a test",
         "This is a test",
     )




    def 							__magic_name__							(   self						:	Dict				)				->							List[Any]:




     '''simple docstring'''

     A__	=    '</s>'
     A__	=    0

     self.assertEqual(self.get_tokenizer()._convert_token_to_id(_a				)     ,			_a				)
     self.assertEqual(self.get_tokenizer()._convert_id_to_token(_a				)     ,			_a				)




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.get_tokenizer()
     A__	=    list(tokenizer.get_vocab().keys()				)

     self.assertEqual(vocab_keys[0]     ,			"</s>"				)
     self.assertEqual(vocab_keys[1]     ,			"<unk>"				)
     self.assertEqual(vocab_keys[-1]     ,			"<s>"				)
     self.assertEqual(len(_a				)     ,			tokenizer.vocab_size + len(tokenizer.get_added_vocab()				)				)




    @unittest.skip("Skip this test while all models are still to be uploaded."				)
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Optional[int]				)				->							Optional[Any]:




     '''simple docstring'''

     A__	=    self.get_tokenizer()

     A__	=    tokenizer.tokenize("This is a test"				)
     self.assertListEqual(_a     ,			["▁This", "▁is", "▁a", "▁t", "est"]				)

     self.assertListEqual(
         tokenizer.convert_tokens_to_ids(_a				)     ,			[2, 3, 4, 5, 6]     ,			)

     A__	=    tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]				)
     self.assertListEqual(_a     ,			["▁This", "▁is", "▁a", "▁t", "est"]				)

     A__	=    tokenizer.convert_tokens_to_string(_a				)
     self.assertEqual(_a     ,			"This is a test"				)




    @slow
    def 							__magic_name__							(   self						:	Tuple				)				->							Any:




     '''simple docstring'''

     A__	=    {'input_ids': [[128_022, 110_108, 397, 11, 38_272, 2_247, 124_811, 285, 18_105, 1_586, 207, 7, 39_534, 4_428, 397, 1_019, 18_105, 1_586, 207, 7, 41_337, 16_786, 241, 7, 20_214, 17, 125_690, 10_398, 7, 44_378, 58_069, 68_342, 7_798, 7_343, 11, 299, 33_310, 4, 158, 37_350, 94_077, 4_569, 299, 33_310, 90, 4, 52_840, 290, 4, 31_270, 112, 299, 682, 4, 52_840, 39_953, 14_079, 193, 52_519, 90_894, 17_894, 120_697, 11, 40_445, 551, 17, 1_019, 52_519, 90_894, 17_756, 963, 11, 40_445, 480, 17, 9_792, 1_120, 5_173, 1_393, 6_240, 16_786, 241, 120_996, 28, 1_245, 1_393, 118_240, 11_123, 1_019, 93_612, 2_691, 10_618, 98_058, 120_409, 1_928, 279, 4, 40_683, 367, 178, 207, 1_019, 103, 103_121, 506, 65_296, 5, 2], [128_022, 21_217, 367, 117, 125_450, 128, 719, 7, 7_308, 40, 93_612, 12_669, 1_116, 16_704, 71, 17_785, 3_699, 15_592, 35, 144, 9_584, 241, 11_943, 713, 950, 799, 2_247, 88_427, 150, 149, 118_813, 120_706, 1_019, 106_906, 81_518, 28, 1_224, 22_799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128_022, 1_658, 123_311, 5_155, 5_578, 4_722, 279, 14_947, 2_366, 1_120, 1_197, 14, 1_348, 9_232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
     # fmt: on

     self.tokenizer_integration_test_util(
         expected_encoding=_a     ,			model_name="facebook/m2m100_418M"     ,			revision="c168bae485c864188cf9aa0e4108b0b6934dc91e"     ,			)






@require_torch
@require_sentencepiece
@require_tokenizers
class      UpperCAmelCase_  (						unittest.TestCase							):
    lowercase__						 =				'''facebook/m2m100_418M'''
    lowercase__						 =				[
        '''In my opinion, there are two levels of response from the French government.''',
        '''NSA Affair Emphasizes Complete Lack of Debate on Intelligence''',
    ]
    lowercase__						 =				[
        '''Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.''',
        '''L\'affaire NSA souligne l\'absence totale de débat sur le renseignement''',
    ]

    # fmt: off
    lowercase__						 =				[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]




    @classmethod
    def 							__magic_name__							(   cls						:	Dict				)				->							str:




     '''simple docstring'''

     A__	=    MaMaaaTokenizer.from_pretrained(
         cls.checkpoint_name     ,			src_lang="en"     ,			tgt_lang="fr"				)
     A__	=    1
     return cls




    def 							__magic_name__							(   self						:	int				)				->							Optional[Any]:




     '''simple docstring'''

     self.assertEqual(self.tokenizer.get_lang_id("ar"				)     ,			128_006				)
     self.assertEqual(self.tokenizer.get_lang_id("en"				)     ,			128_022				)
     self.assertEqual(self.tokenizer.get_lang_id("ro"				)     ,			128_076				)
     self.assertEqual(self.tokenizer.get_lang_id("mr"				)     ,			128_063				)




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




     '''simple docstring'''

     A__	=    self.tokenizer.get_vocab()
     self.assertEqual(len(_a				)     ,			self.tokenizer.vocab_size				)
     self.assertEqual(vocab["<unk>"]     ,			3				)
     self.assertIn(self.tokenizer.get_lang_token("en"				)     ,			_a				)




    def 							__magic_name__							(   self						:	str				)				->							Dict:




     '''simple docstring'''

     A__	=    'en'
     A__	=    self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
     self.assertListEqual(self.expected_src_tokens     ,			_a				)




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




     '''simple docstring'''

     self.assertIn(_a     ,			self.tokenizer.all_special_ids				)
     # fmt: off
     A__	=    [FR_CODE, 5_364, 82, 8_642, 4, 294, 47, 8, 14_028, 136, 3_286, 9_706, 6, 90_797, 6, 144_012, 162, 88_128, 30_061, 5, 2]
     # fmt: on
     A__	=    self.tokenizer.decode(_a     ,			skip_special_tokens=_a				)
     A__	=    self.tokenizer.decode(generated_ids[1:]     ,			skip_special_tokens=_a				)
     self.assertEqual(_a     ,			_a				)
     self.assertNotIn(self.tokenizer.eos_token     ,			_a				)




    def 							__magic_name__							(   self						:	Any				)				->							int:




     '''simple docstring'''

     A__	=    tempfile.mkdtemp()
     A__	=    self.tokenizer.lang_token_to_id
     self.tokenizer.save_pretrained(_a				)
     A__	=    MaMaaaTokenizer.from_pretrained(_a				)
     self.assertDictEqual(new_tok.lang_token_to_id     ,			_a				)




    @require_torch
    def 							__magic_name__							(   self						:	Optional[int]				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    'en'
     A__	=    'fr'

     A__	=    self.tokenizer(self.src_text     ,			text_target=self.tgt_text     ,			padding=_a     ,			return_tensors="pt"				)

     A__	=    shift_tokens_right(
         batch["labels"]     ,			self.tokenizer.pad_token_id     ,			self.tokenizer.eos_token_id				)

     for k in batch:
      A__	=    batch[k].tolist()
     # batch = {k: v.tolist() for k,v in batch.items()}
     # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
     # batch.decoder_inputs_ids[0][0] ==
     assert batch.input_ids[1][0] == EN_CODE
     assert batch.input_ids[1][-1] == 2
     assert batch.labels[1][0] == FR_CODE
     assert batch.labels[1][-1] == 2
     assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]




    @require_torch
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Tuple:




     '''simple docstring'''

     A__	=    'mr'
     self.assertListEqual(self.tokenizer.prefix_tokens     ,			[self.tokenizer.get_lang_id("mr"				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id]				)

     A__	=    'zh'
     self.assertListEqual(self.tokenizer.prefix_tokens     ,			[self.tokenizer.get_lang_id("zh"				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id]				)




    @require_torch
    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    'mr'
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,			[self.tokenizer.get_lang_id("mr"				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id]				)
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,			[self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)

     A__	=    'zh'
     self.tokenizer._switch_to_target_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,			[self.tokenizer.get_lang_id("zh"				)]				)
     self.assertListEqual(self.tokenizer.suffix_tokens     ,			[self.tokenizer.eos_token_id]				)
     self.tokenizer._switch_to_input_mode()
     self.assertListEqual(self.tokenizer.prefix_tokens     ,			[self.tokenizer.get_lang_id(self.tokenizer.src_lang				)]				)




    @require_torch
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    self.tokenizer._build_translation_inputs("A test"     ,			return_tensors="pt"     ,			src_lang="en"     ,			tgt_lang="ar"				)

     self.assertEqual(
         nested_simplify(_a				)     ,			{
             # en_XX, A, test, EOS
             "input_ids": [[128_022, 58, 4_183, 2]],
             "attention_mask": [[1, 1, 1, 1]],
             # ar_AR
             "forced_bos_token_id": 128_006,
         }     ,			)











"""simple docstring"""



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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





