"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
    __UpperCamelCase										=     None


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase										=     {
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

__UpperCamelCase										=     {
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_						      = PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_						      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_						      = ["input_ids", "attention_mask"]
       SCREAMING_SNAKE_CASE_						      = XLMRobertaTokenizer







       def __init__(					self,				lowerCAmelCase__=None,				lowerCAmelCase__=None,				lowerCAmelCase__="<s>",				lowerCAmelCase__="</s>",				lowerCAmelCase__="</s>",				lowerCAmelCase__="<s>",				lowerCAmelCase__="<unk>",				lowerCAmelCase__="<pad>",				lowerCAmelCase__="<mask>",				**lowerCAmelCase__,				) -> Union[str, Any]:
            # Mask token behave like a normal word, i.e. include the space before it
            snake_case_   			=   AddedToken(lowerCAmelCase__,				lstrip=lowerCAmelCase__,				rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__,				lowerCAmelCase__) else mask_token

            super().__init__(
                lowerCAmelCase__,				tokenizer_file=lowerCAmelCase__,				bos_token=lowerCAmelCase__,				eos_token=lowerCAmelCase__,				sep_token=lowerCAmelCase__,				cls_token=lowerCAmelCase__,				unk_token=lowerCAmelCase__,				pad_token=lowerCAmelCase__,				mask_token=lowerCAmelCase__,				**lowerCAmelCase__,				)

            snake_case_   			=   vocab_file
            snake_case_   			=   False if not self.vocab_file else True







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__ = None) -> List[int]:

            if token_ids_a is None:
                 return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
            snake_case_   			=   [self.cls_token_id]
            snake_case_   			=   [self.sep_token_id]
            return cls + token_ids_a + sep + sep + token_ids_a + sep







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__ = None) -> List[int]:
            snake_case_   			=   [self.sep_token_id]
            snake_case_   			=   [self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]







       def 						a_		(					self,				lowerCAmelCase__,				lowerCAmelCase__ = None) -> Tuple[str]:
            if not self.can_save_slow_tokenizer:
                 raise ValueError(
                     'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                     'tokenizer.')

            if not os.path.isdir(lowerCAmelCase__):
                 logger.error(f'Vocabulary path ({save_directory}) should be a directory.')
                 return
            snake_case_   			=   os.path.join(
                lowerCAmelCase__,				(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

            if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):
                 copyfile(self.vocab_file,				lowerCAmelCase__)

            return (out_vocab_file,)


"""simple docstring"""


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


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

