


'''simple docstring'''




import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase  :		Union[str, Any]		     =	get_tests_dir('fixtures/test_sentencepiece.model')
_lowerCAmelCase  :		Tuple		     =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

_lowerCAmelCase  :		Tuple		     =	'pt' if is_torch_available() else 'tf'



@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					Any			      = CamembertTokenizer
	_lowerCamelCase   :					Optional[int]			      = CamembertTokenizerFast
	_lowerCamelCase   :					Optional[Any]			      = True
	_lowerCamelCase   :					List[Any]			      = True



	def 				lowercase     (					self    ):
		super().setUp()

		# We have a SentencePiece fixture for testing
		lowerCAmelCase					:    Tuple      							= CamembertTokenizer(snake_case__    )
		tokenizer.save_pretrained(self.tmpdirname    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= '<pad>'
		lowerCAmelCase					:    Optional[int]      							= 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 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= list(self.get_tokenizer().get_vocab().keys()    )

		self.assertEqual(vocab_keys[0] ,					'<s>NOTUSED'    )
		self.assertEqual(vocab_keys[1] ,					'<pad>'    )
		self.assertEqual(vocab_keys[-1] ,					'<mask>'    )
		self.assertEqual(len(snake_case__    ) ,					1004    )



	def 				lowercase     (					self    ):
		self.assertEqual(self.get_tokenizer().vocab_size ,					1005    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= CamembertTokenizer(snake_case__    )
		tokenizer.save_pretrained(self.tmpdirname    )
		lowerCAmelCase					:    Optional[int]      							= CamembertTokenizerFast.from_pretrained(self.tmpdirname    )

		lowerCAmelCase					:    Union[str, Any]      							= 'I was born in 92000, and this is falsé.'

		lowerCAmelCase					:    Optional[int]      							= tokenizer.encode(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= rust_tokenizer.encode(snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )

		lowerCAmelCase					:    List[str]      							= tokenizer.encode(snake_case__ ,					add_special_tokens=snake_case__    )
		lowerCAmelCase					:    str      							= rust_tokenizer.encode(snake_case__ ,					add_special_tokens=snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )

		# <unk> tokens are not the same for `rust` than for `slow`.
		# Because spm gives back raw token instead of `unk` in EncodeAsPieces
		# tokens = tokenizer.tokenize(sequence)
		lowerCAmelCase					:    List[Any]      							= tokenizer.convert_ids_to_tokens(snake_case__    )
		lowerCAmelCase					:    Any      							= rust_tokenizer.tokenize(snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )



	def 				lowercase     (					self    ):
		if not self.test_rust_tokenizer:
			return

		lowerCAmelCase					:    str      							= self.get_tokenizer()
		lowerCAmelCase					:    str      							= self.get_rust_tokenizer()

		lowerCAmelCase					:    str      							= 'I was born in 92000, and this is falsé.'

		lowerCAmelCase					:    List[str]      							= tokenizer.tokenize(snake_case__    )
		lowerCAmelCase					:    int      							= rust_tokenizer.tokenize(snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )

		lowerCAmelCase					:    Optional[int]      							= tokenizer.encode(snake_case__ ,					add_special_tokens=snake_case__    )
		lowerCAmelCase					:    Tuple      							= rust_tokenizer.encode(snake_case__ ,					add_special_tokens=snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )

		lowerCAmelCase					:    Dict      							= self.get_rust_tokenizer()
		lowerCAmelCase					:    Any      							= tokenizer.encode(snake_case__    )
		lowerCAmelCase					:    Dict      							= rust_tokenizer.encode(snake_case__    )
		self.assertListEqual(snake_case__ ,					snake_case__    )



	@slow
	def 				lowercase     (					self    ):
		# fmt: off
		lowerCAmelCase					:    List[Any]      							= {'input_ids': [[5, 54, 7196, 297, 30, 23, 776, 18, 11, 3215, 3705, 8252, 22, 3164, 1181, 2116, 29, 16, 813, 25, 791, 3314, 20, 3446, 38, 2_7575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9088, 20, 1517, 8, 2_2804, 1_8818, 10, 38, 629, 607, 607, 142, 19, 7196, 867, 56, 1_0326, 24, 2267, 20, 416, 5072, 1_5612, 233, 734, 7, 2399, 27, 16, 3015, 1649, 7, 24, 20, 4338, 2399, 27, 13, 3400, 14, 13, 6189, 8, 930, 9, 6]], '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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
		# fmt: on

		# camembert is a french model. So we also use french texts.
		lowerCAmelCase					:    Dict      							= [
		    'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
		    'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
		    'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
		    'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
		    'telles que la traduction et la synthèse de texte.',
		]

		self.tokenizer_integration_test_util(
		    expected_encoding=snake_case__ ,					model_name='camembert-base' ,					revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' ,					sequences=snake_case__ ,					)




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase  :		Any		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'xlm-roberta-base': 'https://huggingface.co/xlm-roberta-base/resolve/main/config.json',
    'xlm-roberta-large': 'https://huggingface.co/xlm-roberta-large/resolve/main/config.json',
    'xlm-roberta-large-finetuned-conll02-dutch': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll02-spanish': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-english': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'
    ),
    'xlm-roberta-large-finetuned-conll03-german': (
        'https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'
    ),
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					List[str]			      = """xlm-roberta"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

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



class  lowerCAmelCase				(				a							):



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