



'''simple docstring'''

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


__lowerCAmelCase						      =       get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
@require_tokenizers
class      __magic_name__						(   _UpperCamelCase      ,						unittest.TestCase       ):
							lowerCAmelCase	:   List[str]           = XLMRobertaTokenizer
							lowerCAmelCase	:   Optional[int]           = XLMRobertaTokenizerFast
							lowerCAmelCase	:   Union[str, Any]           = True
							lowerCAmelCase	:   int           = True







							def 							__lowercase      (							self :	int		):
									super().setUp()

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







							def 							__lowercase      (							self :	Any		):
									_a					:				Any				=							'<pad>'
									_a					:				Optional[int]				=							1

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







							def 							__lowercase      (							self :	List[str]		):
									_a					:				List[Any]				=							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 							__lowercase      (							self :	Tuple		):
									self.assertEqual(self.get_tokenizer().vocab_size     ,1002		)







							def 							__lowercase      (							self :	List[Any]		):
									_a					:				int				=							XLMRobertaTokenizer(_UpperCAmelCase     ,keep_accents=_UpperCAmelCase		)

									_a					:				Any				=							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]]     ,)

									_a					:				Dict				=							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',
									        '.',
									    ]     ,)
									_a					:				Optional[Any]				=							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 ^
									    ]     ,)

									_a					:				Any				=							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 							__lowercase      (							self :	Dict		):
									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

									_a					:				List[str]				=							(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})"""		):
													_a					:				int				=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase     ,**_UpperCAmelCase		)
													_a					:				Union[str, Any]				=							self.tokenizer_class.from_pretrained(_UpperCAmelCase     ,**_UpperCAmelCase		)

													_a					:				Tuple				=							tempfile.mkdtemp()

													_a					:				Optional[int]				=							tokenizer_r.save_pretrained(_UpperCAmelCase		)
													_a					:				Dict				=							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		)		)
													_a					:				Optional[int]				=							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
													_a					:				Any				=							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a					:				Optional[Any]				=							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
													_a					:				Any				=							tempfile.mkdtemp()

													_a					:				Dict				=							tokenizer_r.save_pretrained(_UpperCAmelCase     ,legacy_format=_UpperCAmelCase		)
													_a					:				Optional[Any]				=							tokenizer_p.save_pretrained(_UpperCAmelCase		)

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

													# Checks everything loads correctly in the same way
													_a					:				str				=							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a					:				str				=							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
													_a					:				Tuple				=							tempfile.mkdtemp()

													_a					:				str				=							tokenizer_r.save_pretrained(_UpperCAmelCase     ,legacy_format=_UpperCAmelCase		)
													_a					:				Tuple				=							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
													_a					:				List[str]				=							tokenizer_r.from_pretrained(_UpperCAmelCase		)
													_a					:				Optional[int]				=							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 							__lowercase      (							self :	int		):
									return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base'		)







							def 							__lowercase      (							self :	Dict		):
									with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(_UpperCAmelCase     ,f.name		)
											_a					:				Any				=							XLMRobertaTokenizer(f.name     ,keep_accents=_UpperCAmelCase		)
											_a					:				Tuple				=							pickle.dumps(_UpperCAmelCase		)
									pickle.loads(_UpperCAmelCase		)







							def 							__lowercase      (							self :	Any		):
									if not self.test_rust_tokenizer:
											return

									_a					:				Tuple				=							self.get_tokenizer()
									_a					:				Any				=							self.get_rust_tokenizer()

									_a					:				List[Any]				=							'I was born in 92000, and this is falsé.'

									_a					:				Dict				=							tokenizer.tokenize(_UpperCAmelCase		)
									_a					:				Union[str, Any]				=							rust_tokenizer.tokenize(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)

									_a					:				Any				=							tokenizer.encode(_UpperCAmelCase     ,add_special_tokens=_UpperCAmelCase		)
									_a					:				Dict				=							rust_tokenizer.encode(_UpperCAmelCase     ,add_special_tokens=_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)

									_a					:				Optional[int]				=							self.get_rust_tokenizer()
									_a					:				str				=							tokenizer.encode(_UpperCAmelCase		)
									_a					:				Dict				=							rust_tokenizer.encode(_UpperCAmelCase		)
									self.assertListEqual(_UpperCAmelCase     ,_UpperCAmelCase		)







							@slow
							def 							__lowercase      (							self :	Union[str, Any]		):
									_a					:				str				=							'Hello World!'
									_a					:				Optional[Any]				=							[0, 35378, 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 							__lowercase      (							self :	Optional[Any]		):
									_a					:				Union[str, Any]				=							(
									    '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'
									)
									_a					:				str				=							[
									    0,
									    3293,
									    83,
									    10,
									    4552,
									    4989,
									    7986,
									    678,
									    10,
									    5915,
									    111,
									    179459,
									    124850,
									    4,
									    6044,
									    237,
									    12,
									    6,
									    5,
									    6,
									    4,
									    6780,
									    705,
									    15,
									    1388,
									    44,
									    378,
									    10114,
									    711,
									    152,
									    20,
									    6,
									    5,
									    22376,
									    642,
									    1221,
									    15190,
									    34153,
									    450,
									    5608,
									    959,
									    1119,
									    57702,
									    136,
									    186,
									    47,
									    1098,
									    29367,
									    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,
									    50901,
									    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 							__lowercase      (							self :	Union[str, Any]		):
									# fmt: off
									_a					:				Tuple				=							{'input_ids': [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 5428, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 12399, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 122009, 115774, 23, 805, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 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, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 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'     ,)









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase						      =       {
    '''configuration_data2vec_audio''': ['''DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Data2VecAudioConfig'''],
    '''configuration_data2vec_text''': [
        '''DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecTextConfig''',
        '''Data2VecTextOnnxConfig''',
    ],
    '''configuration_data2vec_vision''': [
        '''DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Data2VecVisionConfig''',
        '''Data2VecVisionOnnxConfig''',
    ],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecAudioForAudioFrameClassification''',
							    '''Data2VecAudioForCTC''',
							    '''Data2VecAudioForSequenceClassification''',
							    '''Data2VecAudioForXVector''',
							    '''Data2VecAudioModel''',
							    '''Data2VecAudioPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecTextForCausalLM''',
							    '''Data2VecTextForMaskedLM''',
							    '''Data2VecTextForMultipleChoice''',
							    '''Data2VecTextForQuestionAnswering''',
							    '''Data2VecTextForSequenceClassification''',
							    '''Data2VecTextForTokenClassification''',
							    '''Data2VecTextModel''',
							    '''Data2VecTextPreTrainedModel''',
							]
							__lowerCAmelCase						      =       [
							    '''DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''Data2VecVisionForImageClassification''',
							    '''Data2VecVisionForMaskedImageModeling''',
							    '''Data2VecVisionForSemanticSegmentation''',
							    '''Data2VecVisionModel''',
							    '''Data2VecVisionPreTrainedModel''',
							]

if is_tf_available():
							__lowerCAmelCase						      =       [
							    '''TFData2VecVisionForImageClassification''',
							    '''TFData2VecVisionForSemanticSegmentation''',
							    '''TFData2VecVisionModel''',
							    '''TFData2VecVisionPreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
							from .configuration_dataavec_text import (
							    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecTextConfig,
							    DataaVecTextOnnxConfig,
							)
							from .configuration_dataavec_vision import (
							    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DataaVecVisionConfig,
							    DataaVecVisionOnnxConfig,
							)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_dataavec_audio import (
														    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecAudioForAudioFrameClassification,
														    DataaVecAudioForCTC,
														    DataaVecAudioForSequenceClassification,
														    DataaVecAudioForXVector,
														    DataaVecAudioModel,
														    DataaVecAudioPreTrainedModel,
														)
														from .modeling_dataavec_text import (
														    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecTextForCausalLM,
														    DataaVecTextForMaskedLM,
														    DataaVecTextForMultipleChoice,
														    DataaVecTextForQuestionAnswering,
														    DataaVecTextForSequenceClassification,
														    DataaVecTextForTokenClassification,
														    DataaVecTextModel,
														    DataaVecTextPreTrainedModel,
														)
														from .modeling_dataavec_vision import (
														    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DataaVecVisionForImageClassification,
														    DataaVecVisionForMaskedImageModeling,
														    DataaVecVisionForSemanticSegmentation,
														    DataaVecVisionModel,
														    DataaVecVisionPreTrainedModel,
														)
							if is_tf_available():
														from .modeling_tf_dataavec_vision import (
														    TFDataaVecVisionForImageClassification,
														    TFDataaVecVisionForSemanticSegmentation,
														    TFDataaVecVisionModel,
														    TFDataaVecVisionPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase						      =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




