






'''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


_UpperCamelCase						:						List[str]  =					get_tests_dir('fixtures/test_sentencepiece.model')


@require_sentencepiece
@require_tokenizers
class  snake_case__      (					UpperCamelCase     ,				unittest.TestCase):
					a_        =					XLMRobertaTokenizer
					a_        =					XLMRobertaTokenizerFast
					a_        =					True
					a_        =					True




					def 		A      (  self  :		int      )    ->							Tuple:
								super().setUp()

								# We have a SentencePiece fixture for testing
								UpperCAmelCase_      :       Union[str, Any]          =       XLMRobertaTokenizer(snake_case__				, keep_accents=snake_case__      )
								tokenizer.save_pretrained(self.tmpdirname      )




					def 		A      (  self  :		Optional[Any]      )    ->							List[str]:
								UpperCAmelCase_      :       str          =       "<pad>"
								UpperCAmelCase_      :       Optional[Any]          =       1

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




					def 		A      (  self  :		Optional[int]      )    ->							Dict:
								UpperCAmelCase_      :       str          =       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(snake_case__      )				, 10_02      )




					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								self.assertEqual(self.get_tokenizer().vocab_size				, 10_02      )




					def 		A      (  self  :		List[str]      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       XLMRobertaTokenizer(snake_case__				, keep_accents=snake_case__      )

								UpperCAmelCase_      :       List[Any]          =       tokenizer.tokenize('''This is a test'''      )
								self.assertListEqual(snake_case__				, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(snake_case__      )				, [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]				, )

								UpperCAmelCase_      :       Optional[Any]          =       tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
								self.assertListEqual(
								    snake_case__				, [
								        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''',
								        '''.''',
								    ]				, )
								UpperCAmelCase_      :       Any          =       tokenizer.convert_tokens_to_ids(snake_case__      )
								self.assertListEqual(
								    snake_case__				, [
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								        #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
								    ]				, )

								UpperCAmelCase_      :       List[str]          =       tokenizer.convert_ids_to_tokens(snake_case__      )
								self.assertListEqual(
								    snake_case__				, [
								        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 		A      (  self  :		Any      )    ->							str:
								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

								UpperCAmelCase_      :       int          =       (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})"      ):
														UpperCAmelCase_      :       Any          =       self.rust_tokenizer_class.from_pretrained(snake_case__				, **snake_case__      )
														UpperCAmelCase_      :       List[str]          =       self.tokenizer_class.from_pretrained(snake_case__				, **snake_case__      )

														UpperCAmelCase_      :       Tuple          =       tempfile.mkdtemp()

														UpperCAmelCase_      :       Dict          =       tokenizer_r.save_pretrained(snake_case__      )
														UpperCAmelCase_      :       str          =       tokenizer_p.save_pretrained(snake_case__      )

														# 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      )      )
														UpperCAmelCase_      :       int          =       tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
														self.assertSequenceEqual(snake_case__				, snake_case__      )

														# Checks everything loads correctly in the same way
														UpperCAmelCase_      :       Tuple          =       tokenizer_r.from_pretrained(snake_case__      )
														UpperCAmelCase_      :       str          =       tokenizer_p.from_pretrained(snake_case__      )

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

														shutil.rmtree(snake_case__      )

														# Save tokenizer rust, legacy_format=True
														UpperCAmelCase_      :       Tuple          =       tempfile.mkdtemp()

														UpperCAmelCase_      :       Any          =       tokenizer_r.save_pretrained(snake_case__				, legacy_format=snake_case__      )
														UpperCAmelCase_      :       Any          =       tokenizer_p.save_pretrained(snake_case__      )

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

														# Checks everything loads correctly in the same way
														UpperCAmelCase_      :       Optional[Any]          =       tokenizer_r.from_pretrained(snake_case__      )
														UpperCAmelCase_      :       Tuple          =       tokenizer_p.from_pretrained(snake_case__      )

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

														shutil.rmtree(snake_case__      )

														# Save tokenizer rust, legacy_format=False
														UpperCAmelCase_      :       Tuple          =       tempfile.mkdtemp()

														UpperCAmelCase_      :       Dict          =       tokenizer_r.save_pretrained(snake_case__				, legacy_format=snake_case__      )
														UpperCAmelCase_      :       Tuple          =       tokenizer_p.save_pretrained(snake_case__      )

														# 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
														UpperCAmelCase_      :       Optional[int]          =       tokenizer_r.from_pretrained(snake_case__      )
														UpperCAmelCase_      :       Any          =       tokenizer_p.from_pretrained(snake_case__      )

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

														shutil.rmtree(snake_case__      )




					@cached_property
					def 		A      (  self  :		str      )    ->							Union[str, Any]:
								return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base'''      )




					def 		A      (  self  :		Optional[int]      )    ->							int:
								with tempfile.NamedTemporaryFile() as f:
											shutil.copyfile(snake_case__				, f.name      )
											UpperCAmelCase_      :       Tuple          =       XLMRobertaTokenizer(f.name				, keep_accents=snake_case__      )
											UpperCAmelCase_      :       Tuple          =       pickle.dumps(snake_case__      )
								pickle.loads(snake_case__      )




					def 		A      (  self  :		Optional[Any]      )    ->							List[Any]:
								if not self.test_rust_tokenizer:
											return

								UpperCAmelCase_      :       Dict          =       self.get_tokenizer()
								UpperCAmelCase_      :       List[Any]          =       self.get_rust_tokenizer()

								UpperCAmelCase_      :       Optional[int]          =       "I was born in 92000, and this is falsé."

								UpperCAmelCase_      :       Any          =       tokenizer.tokenize(snake_case__      )
								UpperCAmelCase_      :       Any          =       rust_tokenizer.tokenize(snake_case__      )
								self.assertListEqual(snake_case__				, snake_case__      )

								UpperCAmelCase_      :       Union[str, Any]          =       tokenizer.encode(snake_case__				, add_special_tokens=snake_case__      )
								UpperCAmelCase_      :       Union[str, Any]          =       rust_tokenizer.encode(snake_case__				, add_special_tokens=snake_case__      )
								self.assertListEqual(snake_case__				, snake_case__      )

								UpperCAmelCase_      :       List[Any]          =       self.get_rust_tokenizer()
								UpperCAmelCase_      :       Union[str, Any]          =       tokenizer.encode(snake_case__      )
								UpperCAmelCase_      :       Dict          =       rust_tokenizer.encode(snake_case__      )
								self.assertListEqual(snake_case__				, snake_case__      )




					@slow
					def 		A      (  self  :		str      )    ->							Optional[int]:
								UpperCAmelCase_      :       int          =       "Hello World!"
								UpperCAmelCase_      :       int          =       [0, 3_53_78, 66_61, 38, 2]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

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




					@slow
					def 		A      (  self  :		List[Any]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       str          =       (
								    "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"
								)
								UpperCAmelCase_      :       Dict          =       [
								    0,
								    32_93,
								    83,
								    10,
								    45_52,
								    49_89,
								    79_86,
								    6_78,
								    10,
								    59_15,
								    1_11,
								    17_94_59,
								    12_48_50,
								    4,
								    60_44,
								    2_37,
								    12,
								    6,
								    5,
								    6,
								    4,
								    67_80,
								    7_05,
								    15,
								    13_88,
								    44,
								    3_78,
								    1_01_14,
								    7_11,
								    1_52,
								    20,
								    6,
								    5,
								    2_23_76,
								    6_42,
								    12_21,
								    1_51_90,
								    3_41_53,
								    4_50,
								    56_08,
								    9_59,
								    11_19,
								    5_77_02,
								    1_36,
								    1_86,
								    47,
								    10_98,
								    2_93_67,
								    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,
								    60_44,
								    2_37,
								    62_84,
								    5_09_01,
								    5_28,
								    31,
								    90,
								    34,
								    9_27,
								    2,
								]
								# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')  # xlmr.large has same tokenizer
								# xlmr.eval()
								# xlmr.encode(symbols)

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





					@slow
					def 		A      (  self  :		Tuple      )    ->							int:
								UpperCAmelCase_      :       List[str]          =       {"input_ids": [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=snake_case__				, model_name='''xlm-roberta-base'''				, revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3'''				, )










"""simple docstring"""



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


_lowerCAmelCase	:	Any				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Union[str, Any]				=      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''megatron-bert'''
   def __init__(		self      :	Tuple							,			snake_case__      :	List[Any]=2_9_0_5_6							,			snake_case__      :	Any=1_0_2_4							,			snake_case__      :	Tuple=2_4							,			snake_case__      :	Optional[Any]=1_6							,			snake_case__      :	List[str]=4_0_9_6							,			snake_case__      :	Union[str, Any]="gelu"							,			snake_case__      :	int=0.1							,			snake_case__      :	Dict=0.1							,			snake_case__      :	str=5_1_2							,			snake_case__      :	Optional[Any]=2							,			snake_case__      :	Any=0.02							,			snake_case__      :	Optional[Any]=1e-12							,			snake_case__      :	str=0							,			snake_case__      :	Optional[Any]="absolute"							,			snake_case__      :	Optional[Any]=True							,			**snake_case__      :	Any							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    Union[str, Any]							=							vocab_size
        UpperCAmelCase__						:    Union[str, Any]							=							hidden_size
        UpperCAmelCase__						:    Optional[Any]							=							num_hidden_layers
        UpperCAmelCase__						:    Tuple							=							num_attention_heads
        UpperCAmelCase__						:    List[Any]							=							hidden_act
        UpperCAmelCase__						:    Optional[int]							=							intermediate_size
        UpperCAmelCase__						:    Dict							=							hidden_dropout_prob
        UpperCAmelCase__						:    str							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    List[str]							=							type_vocab_size
        UpperCAmelCase__						:    Tuple							=							initializer_range
        UpperCAmelCase__						:    str							=							layer_norm_eps
        UpperCAmelCase__						:    List[Any]							=							position_embedding_type
        UpperCAmelCase__						:    Dict							=							use_cache


