




from dataclasses import dataclass

UpperCAmelCase__						:       Optional[int]							=    '0.1'
UpperCAmelCase__						:       Union[str, Any]							=    'Lucia Harcekova'

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							int
   __SCREAMING_SNAKE_CASE		:							int
   __SCREAMING_SNAKE_CASE		:							str






   def __repr__(	self   :       Optional[int]     )->			str:
     return f'({self.offset}, {self.length}, {self.indicator})'

class      UpperCAmelCase						:






   def __init__(	self   :       List[str]						,					UpperCamelCase_   :       int = 13						,					UpperCamelCase_   :       int = 6     )->			None:
     _lowercase: str						     =    window_size
     _lowercase: Union[str, Any]						     =    lookahead_buffer_size
     _lowercase: Any						     =    self.window_size - self.lookahead_buffer_size






   def  _a						(	self   :       Union[str, Any]						,					UpperCamelCase_   :       str     )->			list[Token]:
     _lowercase: Union[str, Any]						     =    []
     _lowercase: Optional[int]						     =    ''

     # while there are still characters in text to compress
     while text:
       # find the next encoding phrase
       # - triplet with offset, length, indicator (the next encoding character)
       _lowercase: int						     =    self._find_encoding_token(UpperCamelCase_						,					UpperCamelCase_     )

       # update the search buffer:
       # - add new characters from text into it
       # - check if size exceed the max search buffer size, if so, drop the
       #   oldest elements
       search_buffer += text[: token.length + 1]
       if len(UpperCamelCase_     ) > self.search_buffer_size:
         _lowercase: Optional[Any]						     =    search_buffer[-self.search_buffer_size :]

       # update the text
       _lowercase: int						     =    text[token.length + 1 :]

       # append the token to output
       output.append(UpperCamelCase_     )

     return output






   def  _a						(	self   :       List[Any]						,					UpperCamelCase_   :       list[Token]     )->			str:
     _lowercase: List[Any]						     =    ''

     for token in tokens:
       for _ in range(token.length     ):
         output += output[-token.offset]
       output += token.indicator

     return output






   def  _a						(	self   :       Any						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       str     )->			Token:

     if not text:
       raise ValueError('We need some text to work with.'     )

     # Initialise result parameters to default values
     _lowercase     ,					_lowercase: str						     =    0, 0

     if not search_buffer:
       return Token(UpperCamelCase_						,					UpperCamelCase_						,					text[length]     )

     for i, character in enumerate(UpperCamelCase_     ):
       _lowercase: Any						     =    len(UpperCamelCase_     ) - i
       if character == text[0]:
         _lowercase: Optional[int]						     =    self._match_length_from_index(UpperCamelCase_						,					UpperCamelCase_						,					0						,					UpperCamelCase_     )
         # if the found length is bigger than the current or if it's equal,
         # which means it's offset is smaller: update offset and length
         if found_length >= length:
           _lowercase     ,					_lowercase: List[Any]						     =    found_offset, found_length

     return Token(UpperCamelCase_						,					UpperCamelCase_						,					text[length]     )






   def  _a						(	self   :       Tuple						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       str						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       int     )->			int:
     if not text or text[text_index] != window[window_index]:
       return 0
     return 1 + self._match_length_from_index(
         UpperCamelCase_						,					window + text[text_index]						,					text_index + 1						,					window_index + 1     )


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   # Initialize compressor class
   UpperCAmelCase__						:       Tuple							=    LZaaCompressor(window_size=13, lookahead_buffer_size=6)

   # Example
   UpperCAmelCase__						:       Any							=    'cabracadabrarrarrad'
   UpperCAmelCase__						:       Optional[Any]							=    lzaa_compressor.compress(TEXT)
   print(lzaa_compressor.compress('ababcbababaa'))
   UpperCAmelCase__						:       Union[str, Any]							=    lzaa_compressor.decompress(compressed_text)
   assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output
