


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A			:						List[Any]        =  get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
@require_tokenizers
class 				A       (			UpperCAmelCase__					,    unittest.TestCase				):
					'''simple docstring'''






					A__     =					XLMRobertaTokenizer
					A__     =					XLMRobertaTokenizerFast
					A__     =					True
					A__     =					True

					def     lowerCamelCase__	(self       :		List[str]						)		->  List[Any]:




							"""simple docstring"""


							super().setUp()

							# We have a SentencePiece fixture for testing
							lowercase__	     =						XLMRobertaTokenizer(_UpperCAmelCase ,   keep_accents=_UpperCAmelCase						)
							tokenizer.save_pretrained(self.tmpdirname						)

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




							"""simple docstring"""


							lowercase__	     =						"""<pad>"""
							lowercase__	     =						1

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

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




							"""simple docstring"""


							lowercase__	     =						list(self.get_tokenizer().get_vocab().keys()						)

							self.assertEqual(vocab_keys[0] ,   """<s>"""						)
							self.assertEqual(vocab_keys[1] ,   """<pad>"""						)
							self.assertEqual(vocab_keys[-1] ,   """<mask>"""						)
							self.assertEqual(len(_UpperCAmelCase						) ,   1002						)

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




							"""simple docstring"""


							self.assertEqual(self.get_tokenizer().vocab_size ,   1002						)

					def     lowerCamelCase__	(self       :		Any						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						XLMRobertaTokenizer(_UpperCAmelCase ,   keep_accents=_UpperCAmelCase						)

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

							self.assertListEqual(
							    tokenizer.convert_tokens_to_ids(_UpperCAmelCase						) ,   [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,   )

							lowercase__	     =						tokenizer.tokenize("""I was born in 92000, and this is falsé."""						)
							self.assertListEqual(
							    _UpperCAmelCase ,   [
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """9""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """é""",
							        """.""",
							    ] ,   )
							lowercase__	     =						tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
							self.assertListEqual(
							    _UpperCAmelCase ,   [
							        value + tokenizer.fairseq_offset
							        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
							        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
							    ] ,   )

							lowercase__	     =						tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
							self.assertListEqual(
							    _UpperCAmelCase ,   [
							        SPIECE_UNDERLINE + """I""",
							        SPIECE_UNDERLINE + """was""",
							        SPIECE_UNDERLINE + """b""",
							        """or""",
							        """n""",
							        SPIECE_UNDERLINE + """in""",
							        SPIECE_UNDERLINE + """""",
							        """<unk>""",
							        """2""",
							        """0""",
							        """0""",
							        """0""",
							        """,""",
							        SPIECE_UNDERLINE + """and""",
							        SPIECE_UNDERLINE + """this""",
							        SPIECE_UNDERLINE + """is""",
							        SPIECE_UNDERLINE + """f""",
							        """al""",
							        """s""",
							        """<unk>""",
							        """.""",
							    ] ,   )

					def     lowerCamelCase__	(self       :		Optional[Any]						)		->  str:




							"""simple docstring"""


							if not self.test_slow_tokenizer:
									# as we don't have a slow version, we can't compare the outputs between slow and fast versions
									return

							lowercase__	     =						(self.rust_tokenizer_class, """hf-internal-testing/tiny-xlm-roberta""", {})
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
											lowercase__	     =						self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase ,   **_UpperCAmelCase						)
											lowercase__	     =						self.tokenizer_class.from_pretrained(_UpperCAmelCase ,   **_UpperCAmelCase						)

											lowercase__	     =						tempfile.mkdtemp()

											lowercase__	     =						tokenizer_r.save_pretrained(_UpperCAmelCase						)
											lowercase__	     =						tokenizer_p.save_pretrained(_UpperCAmelCase						)

											# Checks it save with the same files + the tokenizer.json file for the fast one
											self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files						)						)
											lowercase__	     =						tuple(f for f in tokenizer_r_files if """tokenizer.json""" not in f						)
											self.assertSequenceEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

											# Checks everything loads correctly in the same way
											lowercase__	     =						tokenizer_r.from_pretrained(_UpperCAmelCase						)
											lowercase__	     =						tokenizer_p.from_pretrained(_UpperCAmelCase						)

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_UpperCAmelCase ,   _UpperCAmelCase						)						)
													# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
													# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

											shutil.rmtree(_UpperCAmelCase						)

											# Save tokenizer rust, legacy_format=True
											lowercase__	     =						tempfile.mkdtemp()

											lowercase__	     =						tokenizer_r.save_pretrained(_UpperCAmelCase ,   legacy_format=_UpperCAmelCase						)
											lowercase__	     =						tokenizer_p.save_pretrained(_UpperCAmelCase						)

											# Checks it save with the same files
											self.assertSequenceEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

											# Checks everything loads correctly in the same way
											lowercase__	     =						tokenizer_r.from_pretrained(_UpperCAmelCase						)
											lowercase__	     =						tokenizer_p.from_pretrained(_UpperCAmelCase						)

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_UpperCAmelCase ,   _UpperCAmelCase						)						)

											shutil.rmtree(_UpperCAmelCase						)

											# Save tokenizer rust, legacy_format=False
											lowercase__	     =						tempfile.mkdtemp()

											lowercase__	     =						tokenizer_r.save_pretrained(_UpperCAmelCase ,   legacy_format=_UpperCAmelCase						)
											lowercase__	     =						tokenizer_p.save_pretrained(_UpperCAmelCase						)

											# Checks it saved the tokenizer.json file
											self.assertTrue(any("""tokenizer.json""" in f for f in tokenizer_r_files						)						)

											# Checks everything loads correctly in the same way
											lowercase__	     =						tokenizer_r.from_pretrained(_UpperCAmelCase						)
											lowercase__	     =						tokenizer_p.from_pretrained(_UpperCAmelCase						)

											# Check special tokens are set accordingly on Rust and Python
											for key in tokenizer_pp.special_tokens_map:
													self.assertTrue(hasattr(_UpperCAmelCase ,   _UpperCAmelCase						)						)

											shutil.rmtree(_UpperCAmelCase						)

					@cached_property
					def     lowerCamelCase__	(self       :		str						)		->  str:




							"""simple docstring"""


							return XLMRobertaTokenizer.from_pretrained("""xlm-roberta-base"""						)

					def     lowerCamelCase__	(self       :		int						)		->  List[str]:




							"""simple docstring"""


							with tempfile.NamedTemporaryFile() as f:
									shutil.copyfile(_UpperCAmelCase ,   f.name						)
									lowercase__	     =						XLMRobertaTokenizer(f.name ,   keep_accents=_UpperCAmelCase						)
									lowercase__	     =						pickle.dumps(_UpperCAmelCase						)
							pickle.loads(_UpperCAmelCase						)

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




							"""simple docstring"""


							if not self.test_rust_tokenizer:
									return

							lowercase__	     =						self.get_tokenizer()
							lowercase__	     =						self.get_rust_tokenizer()

							lowercase__	     =						"""I was born in 92000, and this is falsé."""

							lowercase__	     =						tokenizer.tokenize(_UpperCAmelCase						)
							lowercase__	     =						rust_tokenizer.tokenize(_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

							lowercase__	     =						tokenizer.encode(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						)
							lowercase__	     =						rust_tokenizer.encode(_UpperCAmelCase ,   add_special_tokens=_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

							lowercase__	     =						self.get_rust_tokenizer()
							lowercase__	     =						tokenizer.encode(_UpperCAmelCase						)
							lowercase__	     =						rust_tokenizer.encode(_UpperCAmelCase						)
							self.assertListEqual(_UpperCAmelCase ,   _UpperCAmelCase						)

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




							"""simple docstring"""


							lowercase__	     =						"""Hello World!"""
							lowercase__	     =						[0, 3_5378, 6661, 38, 2]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(_UpperCAmelCase ,   self.big_tokenizer.encode(_UpperCAmelCase						)						)

					@slow
					def     lowerCamelCase__	(self       :		str						)		->  Any:




							"""simple docstring"""


							lowercase__	     =						(
							    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
							    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
							)
							lowercase__	     =						[
							    0,
							    3293,
							    83,
							    10,
							    4552,
							    4989,
							    7986,
							    678,
							    10,
							    5915,
							    111,
							    17_9459,
							    12_4850,
							    4,
							    6044,
							    237,
							    12,
							    6,
							    5,
							    6,
							    4,
							    6780,
							    705,
							    15,
							    1388,
							    44,
							    378,
							    1_0114,
							    711,
							    152,
							    20,
							    6,
							    5,
							    2_2376,
							    642,
							    1221,
							    1_5190,
							    3_4153,
							    450,
							    5608,
							    959,
							    1119,
							    5_7702,
							    136,
							    186,
							    47,
							    1098,
							    2_9367,
							    47,
							    # 4426, # What fairseq tokenizes from "<unk>": "_<"
							    # 3678, # What fairseq tokenizes from "<unk>": "unk"
							    # 2740, # What fairseq tokenizes from "<unk>": ">"
							    3,  # What we tokenize from "<unk>": "<unk>"
							    6,  # Residue from the tokenization: an extra sentencepiece underline
							    4,
							    6044,
							    237,
							    6284,
							    5_0901,
							    528,
							    31,
							    90,
							    34,
							    927,
							    2,
							]
							# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
							# xlmr.eval()
							# xlmr.encode(symbols)

							self.assertListEqual(_UpperCAmelCase ,   self.big_tokenizer.encode(_UpperCAmelCase						)						)


					@slow
					def     lowerCamelCase__	(self       :		str						)		->  List[str]:




							"""simple docstring"""


							lowercase__	     =						{"""input_ids""": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 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], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 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]], """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, 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], [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]]}  # noqa: E501
							# fmt: on

							self.tokenizer_integration_test_util(
							    expected_encoding=_UpperCAmelCase ,   model_name="""xlm-roberta-base""" ,   revision="""d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3""" ,   )





from collections import OrderedDict
from typing import Mapping

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


A			:						List[str]        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''ibert'''

					def __init__(self       :		int ,   _UpperCAmelCase       :		Union[str, Any]=3_0522 ,   _UpperCAmelCase       :		Any=768 ,   _UpperCAmelCase       :		Optional[Any]=12 ,   _UpperCAmelCase       :		Union[str, Any]=12 ,   _UpperCAmelCase       :		List[Any]=3072 ,   _UpperCAmelCase       :		List[Any]="gelu" ,   _UpperCAmelCase       :		List[str]=0.1 ,   _UpperCAmelCase       :		Union[str, Any]=0.1 ,   _UpperCAmelCase       :		Optional[int]=512 ,   _UpperCAmelCase       :		str=2 ,   _UpperCAmelCase       :		Union[str, Any]=0.02 ,   _UpperCAmelCase       :		Dict=1E-1_2 ,   _UpperCAmelCase       :		int=1 ,   _UpperCAmelCase       :		str=0 ,   _UpperCAmelCase       :		Tuple=2 ,   _UpperCAmelCase       :		int="absolute" ,   _UpperCAmelCase       :		Any=False ,   _UpperCAmelCase       :		List[Any]="none" ,   **_UpperCAmelCase       :		List[Any] ,   )		->  Any:




							"""simple docstring"""


							super().__init__(pad_token_id=_UpperCAmelCase ,   bos_token_id=_UpperCAmelCase ,   eos_token_id=_UpperCAmelCase ,   **_UpperCAmelCase						)

							lowercase__	     =						vocab_size
							lowercase__	     =						hidden_size
							lowercase__	     =						num_hidden_layers
							lowercase__	     =						num_attention_heads
							lowercase__	     =						hidden_act
							lowercase__	     =						intermediate_size
							lowercase__	     =						hidden_dropout_prob
							lowercase__	     =						attention_probs_dropout_prob
							lowercase__	     =						max_position_embeddings
							lowercase__	     =						type_vocab_size
							lowercase__	     =						initializer_range
							lowercase__	     =						layer_norm_eps
							lowercase__	     =						position_embedding_type
							lowercase__	     =						quant_mode
							lowercase__	     =						force_dequant






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							if self.task == "multiple-choice":
									lowercase__	     =						{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									lowercase__	     =						{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							    ]						)

