

'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


_lowerCamelCase      					=     logging.get_logger(__name__)

_lowerCamelCase      					=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase      					=     {
    '''vocab_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/vocab.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/vocab.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/vocab.json''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/merges.txt''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/merges.txt''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/merges.txt''',
        '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt''',
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/tokenizer.json''',
        '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/tokenizer.json''',
        '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json''',
        '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json''',
        '''roberta-base-openai-detector''': (
            '''https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'''
        ),
        '''roberta-large-openai-detector''': (
            '''https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'''
        ),
    },
}

_lowerCamelCase      					=     {
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}
class 					_snake_case		(_lowercase):
			__A  :	Optional[int]        =VOCAB_FILES_NAMES
			__A  :	Any        =PRETRAINED_VOCAB_FILES_MAP
			__A  :	List[Any]        =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__A  :	int        =["input_ids", "attention_mask"]
			__A  :	Dict        =RobertaTokenizer






			def __init__(  self			,_snake_case=None			,_snake_case=None			,_snake_case=None			,_snake_case="replace"			,_snake_case="<s>"			,_snake_case="</s>"			,_snake_case="</s>"			,_snake_case="<s>"			,_snake_case="<unk>"			,_snake_case="<pad>"			,_snake_case="<mask>"			,_snake_case=False			,_snake_case=True			,**_snake_case			,):
				super().__init__(
				    _snake_case			,_snake_case			,tokenizer_file=_snake_case			,errors=_snake_case			,bos_token=_snake_case			,eos_token=_snake_case			,sep_token=_snake_case			,cls_token=_snake_case			,unk_token=_snake_case			,pad_token=_snake_case			,mask_token=_snake_case			,add_prefix_space=_snake_case			,trim_offsets=_snake_case			,**_snake_case			,)

				UpperCAmelCase_     :      int						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
				if pre_tok_state.get("add_prefix_space"			,_snake_case     ) != add_prefix_space:
					UpperCAmelCase_     :      Optional[int]						=  getattr(_snake_case			,pre_tok_state.pop("type"     )     )
					UpperCAmelCase_     :      Dict						=  add_prefix_space
					UpperCAmelCase_     :      Tuple						=  pre_tok_class(**_snake_case     )

				UpperCAmelCase_     :      str						=  add_prefix_space

				UpperCAmelCase_     :      List[str]						=  "post_processor"
				UpperCAmelCase_     :      Optional[Any]						=  getattr(self.backend_tokenizer			,_snake_case			,_snake_case     )
				if tokenizer_component_instance:
					UpperCAmelCase_     :      str						=  json.loads(tokenizer_component_instance.__getstate__()     )

					# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
					if "sep" in state:
						UpperCAmelCase_     :      Dict						=  tuple(state["sep"]     )
					if "cls" in state:
						UpperCAmelCase_     :      Any						=  tuple(state["cls"]     )

					UpperCAmelCase_     :      Optional[Any]						=  False

					if state.get("add_prefix_space"			,_snake_case     ) != add_prefix_space:
						UpperCAmelCase_     :      int						=  add_prefix_space
						UpperCAmelCase_     :      Tuple						=  True

					if state.get("trim_offsets"			,_snake_case     ) != trim_offsets:
						UpperCAmelCase_     :      Optional[int]						=  trim_offsets
						UpperCAmelCase_     :      int						=  True

					if changes_to_apply:
						UpperCAmelCase_     :      Union[str, Any]						=  getattr(_snake_case			,state.pop("type"     )     )
						UpperCAmelCase_     :      str						=  component_class(**_snake_case     )
						setattr(self.backend_tokenizer			,_snake_case			,_snake_case     )






			@property
			def 		UpperCamelCase__							(  self     ):
				if self._mask_token is None:
					if self.verbose:
						logger.error("Using mask_token, but it is not set yet."     )
					return None
				return str(self._mask_token     )






			@mask_token.setter
			def 		UpperCamelCase__							(  self			,_snake_case     ):
				UpperCAmelCase_     :      Optional[Any]						=  AddedToken(_snake_case			,lstrip=_snake_case			,rstrip=_snake_case     ) if isinstance(_snake_case			,_snake_case     ) else value
				UpperCAmelCase_     :      Tuple						=  value






			def 		UpperCamelCase__							(  self			,*_snake_case			,**_snake_case     ):
				UpperCAmelCase_     :      List[Any]						=  kwargs.get("is_split_into_words"			,_snake_case     )
				assert self.add_prefix_space or not is_split_into_words, (
				    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._batch_encode_plus(*_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,*_snake_case			,**_snake_case     ):
				UpperCAmelCase_     :      List[Any]						=  kwargs.get("is_split_into_words"			,_snake_case     )

				assert self.add_prefix_space or not is_split_into_words, (
				    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._encode_plus(*_snake_case			,**_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      Tuple						=  self._tokenizer.model.save(_snake_case			,name=_snake_case     )
				return tuple(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None     ):
				UpperCAmelCase_     :      str						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
				if token_ids_a is None:
					return output

				return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      List[str]						=  [self.sep_token_id]
				UpperCAmelCase_     :      Optional[int]						=  [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]






"""simple docstring"""





from typing import TYPE_CHECKING

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


UpperCamelCase_       :    Any						      =			{'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    str						      =			[
		    '''VAN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''VanForImageClassification''',
		    '''VanModel''',
		    '''VanPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_van import (
				    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    VanForImageClassification,
				    VanModel,
				    VanPreTrainedModel,
				)

else:
		import sys

		UpperCamelCase_       :    Optional[Any]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




