

"""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": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}





class    snake_case     (    __snake_case  ):
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_       :				Optional[Any]       				=  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_       :				Any       				=  RobertaTokenizer

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



						'''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__					,  )

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

						__lowerCAmelCase:	Optional[Any]   				=      add_prefix_space

						__lowerCAmelCase:	List[Any]   				=      "post_processor"
						__lowerCAmelCase:	List[Any]   				=      getattr(self.backend_tokenizer					,  UpperCamelCase__					,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:	str   				=      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:
										__lowerCAmelCase:	Dict   				=      tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:	List[str]   				=      tuple(state["cls"])

								__lowerCAmelCase:	List[Any]   				=      False

								if state.get("add_prefix_space"					,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:	List[str]   				=      add_prefix_space
										__lowerCAmelCase:	str   				=      True

								if state.get("trim_offsets"					,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:	Union[str, Any]   				=      trim_offsets
										__lowerCAmelCase:	str   				=      True

								if changes_to_apply:
										__lowerCAmelCase:	str   				=      getattr(UpperCamelCase__					,  state.pop("type"))
										__lowerCAmelCase:	Union[str, Any]   				=      component_class(**UpperCamelCase__)
										setattr(self.backend_tokenizer					,  UpperCamelCase__					,  UpperCamelCase__)

				@property
				def 							lowercase_					(     self							:    int)->       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 							lowercase_					(     self							:    str					,  UpperCamelCase__							:    Optional[int])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      AddedToken(UpperCamelCase__					,  lstrip=UpperCamelCase__					,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__					,  UpperCamelCase__) else value
						__lowerCAmelCase:	Any   				=      value

				def 							lowercase_					(     self							:    int					,  *UpperCamelCase__							:    Optional[Any]					,  **UpperCamelCase__							:    Any)->       BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[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()._batch_encode_plus(*UpperCamelCase__					,  **UpperCamelCase__)

				def 							lowercase_					(     self							:    Any					,  *UpperCamelCase__							:    str					,  **UpperCamelCase__							:    str)->       BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:	List[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 							lowercase_					(     self							:    int					,  UpperCamelCase__							:    str					,  UpperCamelCase__							:    Optional[str] = None)->       Tuple[str]:



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Optional[Any]					,  UpperCamelCase__							:    Optional[int]					,  UpperCamelCase__							:    str=None)->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	List[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 							lowercase_					(     self							:    Optional[int]					,  UpperCamelCase__							:    List[int]					,  UpperCamelCase__							:    Optional[List[int]] = None)->       List[int]:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      [self.sep_token_id]
						__lowerCAmelCase:	int   				=      [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_torch_available


__A						       =      {
    "configuration_swinv2": ["SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Swinv2Config"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A						       =      [
			    "SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Swinv2ForImageClassification",
			    "Swinv2ForMaskedImageModeling",
			    "Swinv2Model",
			    "Swinv2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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


