






'''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   :					Any   							=					logging.get_logger(__name__)

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

__lowerCamelCase   :					int   							=					{
    '''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   :					Optional[int]   							=					{
    '''roberta-base''': 512,
    '''roberta-large''': 512,
    '''roberta-large-mnli''': 512,
    '''distilroberta-base''': 512,
    '''roberta-base-openai-detector''': 512,
    '''roberta-large-openai-detector''': 512,
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	VOCAB_FILES_NAMES
							a__   		=	PRETRAINED_VOCAB_FILES_MAP
							a__   		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__   		=	['''input_ids''', '''attention_mask''']
							a__   		=	RobertaTokenizer

							def __init__(		self			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]=None       ,							lowerCAmelCase__			:Union[str, Any]=None       ,							lowerCAmelCase__			:Optional[int]=None       ,							lowerCAmelCase__			:Optional[Any]="replace"       ,							lowerCAmelCase__			:Dict="<s>"       ,							lowerCAmelCase__			:Optional[int]="</s>"       ,							lowerCAmelCase__			:str="</s>"       ,							lowerCAmelCase__			:Tuple="<s>"       ,							lowerCAmelCase__			:Tuple="<unk>"       ,							lowerCAmelCase__			:Dict="<pad>"       ,							lowerCAmelCase__			:Union[str, Any]="<mask>"       ,							lowerCAmelCase__			:str=False       ,							lowerCAmelCase__			:str=True       ,							**lowerCAmelCase__			:str       ,							)				->  Dict:


										'''simple docstring'''




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

										snake_case_				:					Union[str, Any]   =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get("add_prefix_space"       ,							lowerCAmelCase__				) != add_prefix_space:
													snake_case_				:					Any   =       getattr(lowerCAmelCase__       ,							pre_tok_state.pop("type"				)				)
													snake_case_				:					List[str]   =       add_prefix_space
													snake_case_				:					Optional[Any]   =       pre_tok_class(**lowerCAmelCase__				)

										snake_case_				:					Tuple   =       add_prefix_space

										snake_case_				:					List[Any]   =       "post_processor"
										snake_case_				:					List[str]   =       getattr(self.backend_tokenizer       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)
										if tokenizer_component_instance:
													snake_case_				:					Optional[int]   =       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:
																snake_case_				:					Optional[int]   =       tuple(state["sep"]				)
													if "cls" in state:
																snake_case_				:					List[str]   =       tuple(state["cls"]				)

													snake_case_				:					Any   =       False

													if state.get("add_prefix_space"       ,							lowerCAmelCase__				) != add_prefix_space:
																snake_case_				:					List[Any]   =       add_prefix_space
																snake_case_				:					Optional[int]   =       True

													if state.get("trim_offsets"       ,							lowerCAmelCase__				) != trim_offsets:
																snake_case_				:					Tuple   =       trim_offsets
																snake_case_				:					Optional[Any]   =       True

													if changes_to_apply:
																snake_case_				:					int   =       getattr(lowerCAmelCase__       ,							state.pop("type"				)				)
																snake_case_				:					str   =       component_class(**lowerCAmelCase__				)
																setattr(self.backend_tokenizer       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)

							@property
							def 							_A	(		self			:List[Any]				)				->  str:


										'''simple docstring'''




										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 							_A	(		self			:str       ,							lowerCAmelCase__			:str				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					List[Any]   =       AddedToken(lowerCAmelCase__       ,							lstrip=lowerCAmelCase__       ,							rstrip=lowerCAmelCase__				) if isinstance(lowerCAmelCase__       ,							lowerCAmelCase__				) else value
										snake_case_				:					Union[str, Any]   =       value

							def 							_A	(		self			:Optional[Any]       ,							*lowerCAmelCase__			:str       ,							**lowerCAmelCase__			:List[str]				)				->  BatchEncoding:


										'''simple docstring'''




										snake_case_				:					Dict   =       kwargs.get("is_split_into_words"       ,							lowerCAmelCase__				)
										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(*lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:int       ,							*lowerCAmelCase__			:str       ,							**lowerCAmelCase__			:Any				)				->  BatchEncoding:


										'''simple docstring'''




										snake_case_				:					Any   =       kwargs.get("is_split_into_words"       ,							lowerCAmelCase__				)

										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(*lowerCAmelCase__       ,							**lowerCAmelCase__				)

							def 							_A	(		self			:Any       ,							lowerCAmelCase__			:str       ,							lowerCAmelCase__			:Optional[str] = None				)				->  Tuple[str]:


										'''simple docstring'''




										snake_case_				:					Dict   =       self._tokenizer.model.save(lowerCAmelCase__       ,							name=lowerCAmelCase__				)
										return tuple(lowerCAmelCase__				)

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


										'''simple docstring'''




										snake_case_				:					Any   =       [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 							_A	(		self			:str       ,							lowerCAmelCase__			:List[int]       ,							lowerCAmelCase__			:Optional[List[int]] = None				)				->  List[int]:


										'''simple docstring'''




										snake_case_				:					Union[str, Any]   =       [self.sep_token_id]
										snake_case_				:					str   =       [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 ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__lowerCamelCase   :					Dict   							=					{
    '''configuration_gpt_neox_japanese''': ['''GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXJapaneseConfig'''],
    '''tokenization_gpt_neox_japanese''': ['''GPTNeoXJapaneseTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase   :					int   							=					[
		    '''GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GPTNeoXJapaneseForCausalLM''',
		    '''GPTNeoXJapaneseLayer''',
		    '''GPTNeoXJapaneseModel''',
		    '''GPTNeoXJapanesePreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
		from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_gpt_neox_japanese import (
				    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GPTNeoXJapaneseForCausalLM,
				    GPTNeoXJapaneseLayer,
				    GPTNeoXJapaneseModel,
				    GPTNeoXJapanesePreTrainedModel,
				)


else:
		import sys

		__lowerCamelCase   :					Optional[int]   							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




