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


__UpperCamelCase		:       Optional[Any]									=     logging.get_logger(__name__)

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

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

__UpperCamelCase		:       Union[str, Any]									=     {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		VOCAB_FILES_NAMES
						UpperCamelCase_     =		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_     =		["""input_ids""", """attention_mask"""]
						UpperCamelCase_     =		RobertaTokenizer


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

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

												SCREAMING_SNAKE_CASE							:    str							=							add_prefix_space

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							'''post_processor'''
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							getattr(self.backend_tokenizer    ,		UpperCamelCase__    ,		UpperCamelCase__						)
												if tokenizer_component_instance:
																		SCREAMING_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:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							tuple(state['''sep''']						)
																		if "cls" in state:
																								SCREAMING_SNAKE_CASE							:    str							=							tuple(state['''cls''']						)

																		SCREAMING_SNAKE_CASE							:    List[str]							=							False

																		if state.get('''add_prefix_space'''    ,		UpperCamelCase__						) != add_prefix_space:
																								SCREAMING_SNAKE_CASE							:    Tuple							=							add_prefix_space
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							True

																		if state.get('''trim_offsets'''    ,		UpperCamelCase__						) != trim_offsets:
																								SCREAMING_SNAKE_CASE							:    int							=							trim_offsets
																								SCREAMING_SNAKE_CASE							:    Any							=							True

																		if changes_to_apply:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							getattr(UpperCamelCase__    ,		state.pop('''type'''						)						)
																								SCREAMING_SNAKE_CASE							:    Dict							=							component_class(**UpperCamelCase__						)
																								setattr(self.backend_tokenizer    ,		UpperCamelCase__    ,		UpperCamelCase__						)


						@property
						def        __A       (       self	:				List[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	:				Dict    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Any							=							AddedToken(UpperCamelCase__    ,		lstrip=UpperCamelCase__    ,		rstrip=UpperCamelCase__						) if isinstance(UpperCamelCase__    ,		UpperCamelCase__						) else value
												SCREAMING_SNAKE_CASE							:    List[Any]							=							value


						def        __A       (       self	:				Tuple    ,		*UpperCamelCase__	:				Optional[Any]    ,		**UpperCamelCase__	:				Any						):





												'''simple docstring'''





												SCREAMING_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        __A       (       self	:				List[str]    ,		*UpperCamelCase__	:				str    ,		**UpperCamelCase__	:				Union[str, Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							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        __A       (       self	:				Optional[Any]    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				Optional[str] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							self._tokenizer.model.save(UpperCamelCase__    ,		name=UpperCamelCase__						)
												return tuple(UpperCamelCase__						)


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				str=None						):





												'''simple docstring'''





												SCREAMING_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	:				List[str]    ,		UpperCamelCase__	:				List[int]    ,		UpperCamelCase__	:				Optional[List[int]] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[self.sep_token_id]
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[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_torch_available, is_vision_available


__UpperCamelCase		:       int									=     {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       int									=     ['YolosFeatureExtractor']
					__UpperCamelCase		:       Union[str, Any]									=     ['YolosImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__UpperCamelCase		:       str									=     [
					    'YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'YolosForObjectDetection',
					    'YolosModel',
					    'YolosPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_yolos import YolosFeatureExtractor
										from .image_processing_yolos import YolosImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_yolos import (
										    YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    YolosForObjectDetection,
										    YolosModel,
										    YolosPreTrainedModel,
										)


else:
					import sys

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





