



"""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


A		=						logging.get_logger(__name__)

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

A		=						{
    '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'
        ),
    },
}

A		=						{
    '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       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     VOCAB_FILES_NAMES
			__magic_name__     					=     PRETRAINED_VOCAB_FILES_MAP
			__magic_name__     					=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__magic_name__     					=     ["""input_ids""", """attention_mask"""]
			__magic_name__     					=     RobertaTokenizer



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

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

									snake_case   :			Any						    = add_prefix_space

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

															snake_case   :			List[str]						    = False

															if state.get("add_prefix_space"   ,	_lowercase		) != add_prefix_space:
																					snake_case   :			Union[str, Any]						    = add_prefix_space
																					snake_case   :			Union[str, Any]						    = True

															if state.get("trim_offsets"   ,	_lowercase		) != trim_offsets:
																					snake_case   :			Dict						    = trim_offsets
																					snake_case   :			Dict						    = True

															if changes_to_apply:
																					snake_case   :			List[str]						    = getattr(_lowercase   ,	state.pop("type"		)		)
																					snake_case   :			Optional[int]						    = component_class(**_lowercase		)
																					setattr(self.backend_tokenizer   ,	_lowercase   ,	_lowercase		)



			@property
			def 				__lowercase					(			self							:				List[Any]		)							->					str:
									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 				__lowercase					(			self							:				Any   ,	_lowercase							:				Optional[int]		)							->					Dict:
									snake_case   :			List[str]						    = AddedToken(_lowercase   ,	lstrip=_lowercase   ,	rstrip=_lowercase		) if isinstance(_lowercase   ,	_lowercase		) else value
									snake_case   :			Union[str, Any]						    = value



			def 				__lowercase					(			self							:				Optional[Any]   ,	*_lowercase							:				Union[str, Any]   ,	**_lowercase							:				Dict		)							->					BatchEncoding:
									snake_case   :			List[Any]						    = kwargs.get("is_split_into_words"   ,	_lowercase		)
									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(*_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				List[Any]   ,	*_lowercase							:				str   ,	**_lowercase							:				List[str]		)							->					BatchEncoding:
									snake_case   :			List[Any]						    = kwargs.get("is_split_into_words"   ,	_lowercase		)

									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(*_lowercase   ,	**_lowercase		)



			def 				__lowercase					(			self							:				int   ,	_lowercase							:				str   ,	_lowercase							:				Optional[str] = None		)							->					Tuple[str]:
									snake_case   :			str						    = self._tokenizer.model.save(_lowercase   ,	name=_lowercase		)
									return tuple(_lowercase		)



			def 				__lowercase					(			self							:				Optional[Any]   ,	_lowercase							:				List[Any]   ,	_lowercase							:				int=None		)							->					Optional[int]:
									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 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				List[int]   ,	_lowercase							:				Optional[List[int]] = None		)							->					List[int]:
									snake_case   :			List[str]						    = [self.sep_token_id]
									snake_case   :			Optional[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]








"""simple docstring"""







from typing import TYPE_CHECKING

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


A		=						{'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						['ViTFeatureExtractor']
			A		=						['ViTImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ViTForImageClassification',
			    'ViTForMaskedImageModeling',
			    'ViTModel',
			    'ViTPreTrainedModel',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'TFViTForImageClassification',
			    'TFViTModel',
			    'TFViTPreTrainedModel',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			A		=						[
			    'FlaxViTForImageClassification',
			    'FlaxViTModel',
			    'FlaxViTPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_vit import ViTFeatureExtractor
						from .image_processing_vit import ViTImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit import (
						    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTForImageClassification,
						    ViTForMaskedImageModeling,
						    ViTModel,
						    ViTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
			import sys

			A		=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



