
'''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


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowercase__						       =      {
    "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"
        ),
    },
}

lowercase__						       =      {
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

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



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






				"""simple docstring"""







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

				snake_case  :       List[Any]     			=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
				if pre_tok_state.get('''add_prefix_space'''  ,	UpperCamelCase__      ) != add_prefix_space:
						snake_case  :       int     			=						getattr(UpperCamelCase__  ,	pre_tok_state.pop('''type'''      )      )
						snake_case  :       int     			=						add_prefix_space
						snake_case  :       List[Any]     			=						pre_tok_class(**UpperCamelCase__      )

				snake_case  :       Dict     			=						add_prefix_space

				snake_case  :       List[Any]     			=						'''post_processor'''
				snake_case  :       Any     			=						getattr(self.backend_tokenizer  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				if tokenizer_component_instance:
						snake_case  :       Tuple     			=						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  :       Tuple     			=						tuple(state['''sep''']      )
						if "cls" in state:
								snake_case  :       Optional[Any]     			=						tuple(state['''cls''']      )

						snake_case  :       Any     			=						False

						if state.get('''add_prefix_space'''  ,	UpperCamelCase__      ) != add_prefix_space:
								snake_case  :       Dict     			=						add_prefix_space
								snake_case  :       Optional[int]     			=						True

						if state.get('''trim_offsets'''  ,	UpperCamelCase__      ) != trim_offsets:
								snake_case  :       Optional[int]     			=						trim_offsets
								snake_case  :       Optional[Any]     			=						True

						if changes_to_apply:
								snake_case  :       List[Any]     			=						getattr(UpperCamelCase__  ,	state.pop('''type'''      )      )
								snake_case  :       List[str]     			=						component_class(**UpperCamelCase__      )
								setattr(self.backend_tokenizer  ,	UpperCamelCase__  ,	UpperCamelCase__      )



		@property
		def       lowerCAmelCase				(      self				:			Union[str, 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       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Union[str, Any]      )						->			List[str]:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						AddedToken(UpperCamelCase__  ,	lstrip=UpperCamelCase__  ,	rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__  ,	UpperCamelCase__      ) else value
				snake_case  :       Tuple     			=						value



		def       lowerCAmelCase				(      self				:			Any  ,	*UpperCamelCase__				:			Any  ,	**UpperCamelCase__				:			Dict      )						->			BatchEncoding:






				"""simple docstring"""







				snake_case  :       Any     			=						kwargs.get('''is_split_into_words'''  ,	UpperCamelCase__      )
				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(*UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	*UpperCamelCase__				:			List[Any]  ,	**UpperCamelCase__				:			Union[str, Any]      )						->			BatchEncoding:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						kwargs.get('''is_split_into_words'''  ,	UpperCamelCase__      )

				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(*UpperCamelCase__  ,	**UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[str] = None      )						->			Tuple[str]:






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self._tokenizer.model.save(UpperCamelCase__  ,	name=UpperCamelCase__      )
				return tuple(UpperCamelCase__      )



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Tuple  ,	UpperCamelCase__				:			List[Any]=None      )						->			Any:






				"""simple docstring"""







				snake_case  :       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       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None      )						->			List[int]:






				"""simple docstring"""







				snake_case  :       List[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 ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowercase__						       =      {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["EncoderDecoderModel"]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["TFEncoderDecoderModel"]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["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

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


