





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase				:   List[Any]						       =						logging.get_logger(__name__)

_lowerCAmelCase				:   int						       =						{
    '''microsoft/swinv2-tiny-patch4-window8-256''': (
        '''https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'''
    ),
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''swinv2'''

     __UpperCamelCase            =  {
         '''num_attention_heads''': '''num_heads''',
         '''num_hidden_layers''': '''num_layers''',
     }



     def __init__(				self							:Union[str, Any]      ,   snake_case							:Optional[int]=224      ,   snake_case							:Optional[Any]=4      ,   snake_case							:List[Any]=3      ,   snake_case							:List[Any]=96      ,   snake_case							:int=[2, 2, 6, 2]      ,   snake_case							:List[str]=[3, 6, 12, 24]      ,   snake_case							:Dict=7      ,   snake_case							:Optional[int]=4.0      ,   snake_case							:Optional[Any]=True      ,   snake_case							:str=0.0      ,   snake_case							:int=0.0      ,   snake_case							:Any=0.1      ,   snake_case							:List[str]="gelu"      ,   snake_case							:int=False      ,   snake_case							:Any=0.02      ,   snake_case							:str=1e-5      ,   snake_case							:Dict=32      ,   **snake_case							:int      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					Dict        =      image_size
        A_       :					Optional[int]        =      patch_size
        A_       :					Union[str, Any]        =      num_channels
        A_       :					List[Any]        =      embed_dim
        A_       :					List[Any]        =      depths
        A_       :					Optional[int]        =      len(snake_case	)
        A_       :					Optional[Any]        =      num_heads
        A_       :					Dict        =      window_size
        A_       :					Optional[int]        =      mlp_ratio
        A_       :					Optional[int]        =      qkv_bias
        A_       :					Union[str, Any]        =      hidden_dropout_prob
        A_       :					Dict        =      attention_probs_dropout_prob
        A_       :					Union[str, Any]        =      drop_path_rate
        A_       :					int        =      hidden_act
        A_       :					Optional[Any]        =      use_absolute_embeddings
        A_       :					Tuple        =      layer_norm_eps
        A_       :					Dict        =      initializer_range
        A_       :					Tuple        =      encoder_stride
        # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        A_       :					List[str]        =      int(embed_dim * 2 ** (len(snake_case	) - 1)	)
        A_       :					int        =      (0, 0, 0, 0)







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_bart import BartTokenizer


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


_lowerCAmelCase				:   List[str]						       =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase				:   int						       =						{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

_lowerCAmelCase				:   Optional[int]						       =						{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']
     __UpperCamelCase            =  BartTokenizer



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





        '''simple docstring'''




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

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

        A_       :					Any        =      add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A_       :					Tuple        =      "post_processor"
        A_       :					Union[str, Any]        =      getattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)
        if tokenizer_component_instance:
           A_       :					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:
              A_       :					List[Any]        =      tuple(state["sep"]	)
           if "cls" in state:
              A_       :					str        =      tuple(state["cls"]	)

           A_       :					int        =      False

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

           if state.get("trim_offsets"      ,   snake_case	) != trim_offsets:
              A_       :					int        =      trim_offsets
              A_       :					str        =      True

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



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''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     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Optional[int]        =      AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else value
        A_       :					List[Any]        =      value



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   *snake_case							:str      ,   **snake_case							:str	):





        '''simple docstring'''




        A_       :					Tuple        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._batch_encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   *snake_case							:Any      ,   **snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




        A_       :					Any        =      self._tokenizer.model.save(snake_case      ,   name=snake_case	)
        return tuple(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:List[str]      ,   snake_case							:Optional[int]=None	):





        '''simple docstring'''




        A_       :					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     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Dict        =      [self.sep_token_id]
        A_       :					List[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]
