


"""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:
 snake_case__		:  Optional[int]     					=					None


snake_case__		:  Tuple     					=					logging.get_logger(__name__)

snake_case__		:  Union[str, Any]     					=					{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

snake_case__		:  str     					=					{
    '''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'''
        ),
    },
}

snake_case__		:  str     					=					{
    '''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 		snake_case_(							a__      ):
     __UpperCamelCase	=       VOCAB_FILES_NAMES
     __UpperCamelCase	=       PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase	=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase	=       ['''input_ids''', '''attention_mask''']
     __UpperCamelCase	=       XLMRobertaTokenizer






     def __init__(						self	:       Tuple       ,					UpperCamelCase_	:       Optional[Any]=None       ,					UpperCamelCase_	:       List[Any]=None       ,					UpperCamelCase_	:       Optional[Any]="<s>"       ,					UpperCamelCase_	:       Tuple="</s>"       ,					UpperCamelCase_	:       List[Any]="</s>"       ,					UpperCamelCase_	:       Optional[int]="<s>"       ,					UpperCamelCase_	:       str="<unk>"       ,					UpperCamelCase_	:       List[str]="<pad>"       ,					UpperCamelCase_	:       int="<mask>"       ,					**UpperCamelCase_	:       Optional[Any]       ,					):
           lowerCAmelCase       :						Optional[int]							= AddedToken(__lowercase       ,					lstrip=__lowercase       ,					rstrip=__lowercase					) if isinstance(__lowercase       ,					__lowercase					) else mask_token

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

           lowerCAmelCase       :						str							= vocab_file
           lowerCAmelCase       :						str							= False if not self.vocab_file else True






     def   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       int       ,					UpperCamelCase_	:       Union[str, Any] = None					):




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






     def   lowerCamelCase__  (						self	:       str       ,					UpperCamelCase_	:       List[str]       ,					UpperCamelCase_	:       Optional[Any] = None					):
           lowerCAmelCase       :						str							= [self.sep_token_id]
           lowerCAmelCase       :						Dict							= [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   lowerCamelCase__  (						self	:       Dict       ,					UpperCamelCase_	:       str       ,					UpperCamelCase_	:       Any = None					):




           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(__lowercase					):
                 logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''					)
                 return
           lowerCAmelCase       :						List[Any]							= os.path.join(
               __lowercase       ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

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

           return (out_vocab_file,)








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase__						=		{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		['LayoutLMTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'LayoutLMForMaskedLM',
							    'LayoutLMForSequenceClassification',
							    'LayoutLMForTokenClassification',
							    'LayoutLMForQuestionAnswering',
							    'LayoutLMModel',
							    'LayoutLMPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFLayoutLMForMaskedLM',
							    'TFLayoutLMForSequenceClassification',
							    'TFLayoutLMForTokenClassification',
							    'TFLayoutLMForQuestionAnswering',
							    'TFLayoutLMMainLayer',
							    'TFLayoutLMModel',
							    'TFLayoutLMPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
							from .tokenization_layoutlm import LayoutLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_layoutlm import (
														    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LayoutLMForMaskedLM,
														    LayoutLMForQuestionAnswering,
														    LayoutLMForSequenceClassification,
														    LayoutLMForTokenClassification,
														    LayoutLMModel,
														    LayoutLMPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_layoutlm import (
														    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLayoutLMForMaskedLM,
														    TFLayoutLMForQuestionAnswering,
														    TFLayoutLMForSequenceClassification,
														    TFLayoutLMForTokenClassification,
														    TFLayoutLMMainLayer,
														    TFLayoutLMModel,
														    TFLayoutLMPreTrainedModel,
														)

else:
							import sys

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