

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   :      Optional[int]     =     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase   :      Any     =     {
    "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   :      Any     =     {
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}
class        _A						(     A__):
  SCREAMING_SNAKE_CASE			:					Any				=     VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE			:					List[Any]				=     PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE			:					Union[str, Any]				=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE			:					str				=     ['''input_ids''', '''attention_mask''']
  SCREAMING_SNAKE_CASE			:					List[str]				=     BartTokenizer

  def __init__( self       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE=None       ,    _SCREAMING_SNAKE_CASE="replace"       ,    _SCREAMING_SNAKE_CASE="<s>"       ,    _SCREAMING_SNAKE_CASE="</s>"       ,    _SCREAMING_SNAKE_CASE="</s>"       ,    _SCREAMING_SNAKE_CASE="<s>"       ,    _SCREAMING_SNAKE_CASE="<unk>"       ,    _SCREAMING_SNAKE_CASE="<pad>"       ,    _SCREAMING_SNAKE_CASE="<mask>"       ,    _SCREAMING_SNAKE_CASE=False       ,    _SCREAMING_SNAKE_CASE=True       ,    **_SCREAMING_SNAKE_CASE       ,    ):





        """simple docstring"""







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

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

        SCREAMING_SNAKE_CASE_ :     Tuple							=   add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        SCREAMING_SNAKE_CASE_ :     int							=   'post_processor'
        SCREAMING_SNAKE_CASE_ :     int							=   getattr(self.backend_tokenizer       ,    _a       ,    _a    )
        if tokenizer_component_instance:
              SCREAMING_SNAKE_CASE_ :     int							=   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_ :     Dict							=   tuple(state['sep']    )
              if "cls" in state:
                    SCREAMING_SNAKE_CASE_ :     List[Any]							=   tuple(state['cls']    )

              SCREAMING_SNAKE_CASE_ :     int							=   False

              if state.get('add_prefix_space'       ,    _a    ) != add_prefix_space:
                    SCREAMING_SNAKE_CASE_ :     Dict							=   add_prefix_space
                    SCREAMING_SNAKE_CASE_ :     List[Any]							=   True

              if state.get('trim_offsets'       ,    _a    ) != trim_offsets:
                    SCREAMING_SNAKE_CASE_ :     Any							=   trim_offsets
                    SCREAMING_SNAKE_CASE_ :     Optional[Any]							=   True

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

  @property
  def        UpperCAmelCase			( self    ):





        """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        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     List[Any]							=   AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a    ) if isinstance(_a       ,    _a    ) else value
        SCREAMING_SNAKE_CASE_ :     int							=   value

  def        UpperCAmelCase			( self       ,    *_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     int							=   kwargs.get('is_split_into_words'       ,    _a    )

        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(*_a       ,    **_a    )

  def        UpperCAmelCase			( self       ,    *_SCREAMING_SNAKE_CASE       ,    **_SCREAMING_SNAKE_CASE    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Tuple							=   kwargs.get('is_split_into_words'       ,    _a    )

        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(*_a       ,    **_a    )

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE = None    ):





        """simple docstring"""







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

  def        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE=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        UpperCAmelCase			( self       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE = None    ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_ :     Union[str, Any]							=   [self.sep_token_id]
        SCREAMING_SNAKE_CASE_ :     Tuple							=   [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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase :      List[str]					 =     logging.get_logger(__name__)

lowerCamelCase :      List[Any]					 =     {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}







class 							A__    (       A__     ,   A__  ):
  A__		 =				'convnextv2'





  def __init__(		self      :	Tuple							,					_a      :	Optional[int]=3							,					_a      :	Any=4							,					_a      :	int=4							,					_a      :	Union[str, Any]=None							,					_a      :	List[str]=None							,					_a      :	Optional[Any]="gelu"							,					_a      :	Any=0.02							,					_a      :	Any=1e-12							,					_a      :	Tuple=0.0							,					_a      :	int=224							,					_a      :	Any=None							,					_a      :	Optional[int]=None							,					**_a      :	List[str]							,					)	->    Optional[Any]:





       '''simple docstring'''






       super().__init__(**_a )

       _SCREAMING_SNAKE_CASE							    =num_channels
       _SCREAMING_SNAKE_CASE							    =patch_size
       _SCREAMING_SNAKE_CASE							    =num_stages
       _SCREAMING_SNAKE_CASE							    =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
       _SCREAMING_SNAKE_CASE							    =[3, 3, 9, 3] if depths is None else depths
       _SCREAMING_SNAKE_CASE							    =hidden_act
       _SCREAMING_SNAKE_CASE							    =initializer_range
       _SCREAMING_SNAKE_CASE							    =layer_norm_eps
       _SCREAMING_SNAKE_CASE							    =drop_path_rate
       _SCREAMING_SNAKE_CASE							    =image_size
       _SCREAMING_SNAKE_CASE							    =['stem'] + [f"stage{idx}" for idx in range(1							,					len(self.depths ) + 1 )]
       _SCREAMING_SNAKE_CASE    ,						_SCREAMING_SNAKE_CASE							    =get_aligned_output_features_output_indices(
           out_features=_a							,					out_indices=_a							,					stage_names=self.stage_names )
