
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


SCREAMING_SNAKE_CASE							:Any										=    logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE							:Optional[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'
        ),
    },
}

SCREAMING_SNAKE_CASE							:List[str]										=    {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					["input_ids", "attention_mask"]
	snake_case_    =					RobertaTokenizer






	def __init__(	self :			Tuple							,A :			int=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			Tuple="replace"							,A :			str="<s>"							,A :			str="</s>"							,A :			Union[str, Any]="</s>"							,A :			int="<s>"							,A :			str="<unk>"							,A :			int="<pad>"							,A :			Union[str, Any]="<mask>"							,A :			List[Any]=False							,A :			Tuple=True							,**A :			Dict							,):
					super().__init__(
					    A							,A							,tokenizer_file=A							,errors=A							,bos_token=A							,eos_token=A							,sep_token=A							,cls_token=A							,unk_token=A							,pad_token=A							,mask_token=A							,add_prefix_space=A							,trim_offsets=A							,**A							,)

					__A             =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
					if pre_tok_state.get("add_prefix_space"							,A			) != add_prefix_space:
									__A             =			getattr(A							,pre_tok_state.pop("type"			)			)
									__A             =			add_prefix_space
									__A             =			pre_tok_class(**A			)

					__A             =			add_prefix_space

					__A             =			"post_processor"
					__A             =			getattr(self.backend_tokenizer							,A							,A			)
					if tokenizer_component_instance:
									__A             =			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:
													__A             =			tuple(state["sep"]			)
									if "cls" in state:
													__A             =			tuple(state["cls"]			)

									__A             =			False

									if state.get("add_prefix_space"							,A			) != add_prefix_space:
													__A             =			add_prefix_space
													__A             =			True

									if state.get("trim_offsets"							,A			) != trim_offsets:
													__A             =			trim_offsets
													__A             =			True

									if changes_to_apply:
													__A             =			getattr(A							,state.pop("type"			)			)
													__A             =			component_class(**A			)
													setattr(self.backend_tokenizer							,A							,A			)






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					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 :			Tuple							,A :			List[Any]			):
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else value
					__A             =			value






	def     UpperCamelCase_  (	self :			Dict							,*A :			List[str]							,**A :			List[str]			):
					__A             =			kwargs.get("is_split_into_words"							,A			)
					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(*A							,**A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,*A :			str							,**A :			int			):
					__A             =			kwargs.get("is_split_into_words"							,A			)

					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(*A							,**A			)






	def     UpperCamelCase_  (	self :			str							,A :			str							,A :			Optional[str] = None			):
					__A             =			self._tokenizer.model.save(A							,name=A			)
					return tuple(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple							,A :			Tuple=None			):
					__A             =			[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 :			Any							,A :			List[int]							,A :			Optional[List[int]] = None			):
					__A             =			[self.sep_token_id]
					__A             =			[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]


from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:int										=    {'configuration_encoder_decoder': ['EncoderDecoderConfig']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Dict										=    ['EncoderDecoderModel']

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:int										=    ['TFEncoderDecoderModel']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:Optional[int]										=    ['FlaxEncoderDecoderModel']

if TYPE_CHECKING:
					from .configuration_encoder_decoder import EncoderDecoderConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encoder_decoder import EncoderDecoderModel

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
					import sys

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