






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


A_	:	Any				=				logging.get_logger(__name__)




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	Tuple								='''vision-text-dual-encoder'''
       a       :	Optional[Any]								=True





       def __init__(      self						,    _lowerCamelCase=5_1_2						,    _lowerCamelCase=2.6_5_9_2						,    **_lowerCamelCase							):
         super().__init__(**_lowerCamelCase							)

         if "vision_config" not in kwargs:
           raise ValueError('`vision_config` can not be `None`.'							)

         if "text_config" not in kwargs:
           raise ValueError('`text_config` can not be `None`.'							)

         UpperCamelCase_:      Dict					   =       kwargs.pop('vision_config'							)
         UpperCamelCase_:      str					   =       kwargs.pop('text_config'							)

         UpperCamelCase_:      Optional[Any]					   =       vision_config.pop('model_type'							)
         UpperCamelCase_:      Union[str, Any]					   =       text_config.pop('model_type'							)

         if vision_model_type == "clip":
           UpperCamelCase_:      Optional[Any]					   =       AutoConfig.for_model(_lowerCamelCase						,    **_lowerCamelCase							).vision_config
         elif vision_model_type == "clip_vision_model":
           UpperCamelCase_:      Optional[int]					   =       CLIPVisionConfig(**_lowerCamelCase							)
         else:
           UpperCamelCase_:      str					   =       AutoConfig.for_model(_lowerCamelCase						,    **_lowerCamelCase							)

         UpperCamelCase_:      int					   =       AutoConfig.for_model(_lowerCamelCase						,    **_lowerCamelCase							)

         UpperCamelCase_:      Any					   =       projection_dim
         UpperCamelCase_:      List[str]					   =       logit_scale_init_value





       @classmethod
       def     _a					(      cls						,    _lowerCamelCase						,    _lowerCamelCase						,    **_lowerCamelCase							):
         return cls(vision_config=vision_config.to_dict()						,    text_config=text_config.to_dict()						,    **_lowerCamelCase							)





       def     _a					(      self							):
         UpperCamelCase_:      Optional[int]					   =       copy.deepcopy(self.__dict__							)
         UpperCamelCase_:      Tuple					   =       self.vision_config.to_dict()
         UpperCamelCase_:      Dict					   =       self.text_config.to_dict()
         UpperCamelCase_:      Optional[Any]					   =       self.__class__.model_type
         return output







import os
from shutil import copyfile
from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_reformer import ReformerTokenizer
else:
   A_	:	Union[str, Any]				=				None


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


A_	:	List[str]				=				'▁'

A_	:	int				=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

A_	:	Optional[Any]				=				{
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

A_	:	List[Any]				=				{
    'google/reformer-crime-and-punishment': 524288,
}




class        _lowerCAmelCase( UpperCAmelCase_      ):





       """simple docstring"""







       a       :	int								=VOCAB_FILES_NAMES
       a       :	Optional[Any]								=PRETRAINED_VOCAB_FILES_MAP
       a       :	List[Any]								=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       a       :	Tuple								=['''input_ids''', '''attention_mask''']
       a       :	Optional[Any]								=ReformerTokenizer





       def __init__(      self						,    _lowerCamelCase=None						,    _lowerCamelCase=None						,    _lowerCamelCase="</s>"						,    _lowerCamelCase="<unk>"						,    _lowerCamelCase=[]						,    **_lowerCamelCase						,    ):
         super().__init__(
             _lowerCamelCase						,    tokenizer_file=_lowerCamelCase						,    eos_token=_lowerCamelCase						,    unk_token=_lowerCamelCase						,    additional_special_tokens=_lowerCamelCase						,    **_lowerCamelCase						,    )

         UpperCamelCase_:      Any					   =       vocab_file
         UpperCamelCase_:      str					   =       False if not self.vocab_file else True





       def     _a					(      self						,    _lowerCamelCase						,    _lowerCamelCase = None							):
         if not self.can_save_slow_tokenizer:
           raise ValueError(
               'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
               'tokenizer.'							)

         if not os.path.isdir(_lowerCamelCase							):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
         UpperCamelCase_:      str					   =       os.path.join(
             _lowerCamelCase						,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']							)

         if os.path.abspath(self.vocab_file							) != os.path.abspath(_lowerCamelCase							):
           copyfile(self.vocab_file						,    _lowerCamelCase							)

         return (out_vocab_file,)