





from dataclasses import dataclass

_UpperCamelCase    :							int      =       """0.1"""
_UpperCamelCase    :							List[Any]      =       """Lucia Harcekova"""







@dataclass
class 						_lowerCAmelCase:

  """simple docstring"""







  lowerCamelCase__			     =    42
  lowerCamelCase__			     =    42
  lowerCamelCase__			     =    42

  def __repr__(					self	)->					str:
     return f"({self.offset}, {self.length}, {self.indicator})"







class 						_lowerCAmelCase:

  """simple docstring"""







  def __init__(					self  ,				UpperCAmelCase = 13  ,				UpperCAmelCase = 6	)->					None:
     __A      					=   window_size
     __A      					=   lookahead_buffer_size
     __A      					=   self.window_size - self.lookahead_buffer_size

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase	)->					list[Token]:
     __A      					=   []
     __A      					=   ''''''

     # 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)
        __A      					=   self._find_encoding_token(lowercase_  ,				lowercase_	)

        # 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(lowercase_	) > self.search_buffer_size:
           __A      					=   search_buffer[-self.search_buffer_size :]

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

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

     return output

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase	)->					str:
     __A      					=   ''''''

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

     return output

  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase	)->					Token:







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

     # Initialise result parameters to default values
     __A					,						__A      					=   0, 0

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

     for i, character in enumerate(lowercase_	):
        __A      					=   len(lowercase_	) - i
        if character == text[0]:
           __A      					=   self._match_length_from_index(lowercase_  ,				lowercase_  ,				0  ,				lowercase_	)
           # 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:
              __A					,						__A      					=   found_offset, found_length

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





  def 		SCREAMING_SNAKE_CASE__       (					self  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase  ,				UpperCAmelCase	)->					int:







     if not text or text[text_index] != window[window_index]:
        return 0
     return 1 + self._match_length_from_index(
         lowercase_  ,				window + text[text_index]  ,				text_index + 1  ,				window_index + 1	)


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

    testmod()
    # Initialize compressor class
    _UpperCamelCase    :							Optional[int]      =       LZaaCompressor(window_size=1_3, lookahead_buffer_size=6)

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


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__   = logging.get_logger(__name__)

lowerCamelCase__   = {
    '''nghuyong/ernie-1.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-1.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-base-en''': '''https://huggingface.co/nghuyong/ernie-2.0-base-en/resolve/main/config.json''',
    '''nghuyong/ernie-2.0-large-en''': '''https://huggingface.co/nghuyong/ernie-2.0-large-en/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-base-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-base-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-medium-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-medium-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-mini-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-mini-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-micro-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-micro-zh/resolve/main/config.json''',
    '''nghuyong/ernie-3.0-nano-zh''': '''https://huggingface.co/nghuyong/ernie-3.0-nano-zh/resolve/main/config.json''',
    '''nghuyong/ernie-gram-zh''': '''https://huggingface.co/nghuyong/ernie-gram-zh/resolve/main/config.json''',
    '''nghuyong/ernie-health-zh''': '''https://huggingface.co/nghuyong/ernie-health-zh/resolve/main/config.json''',
}




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 __A           = '''ernie'''







 def __init__(						self							:					Dict				,      lowercase_							:					List[Any]=30522				,      lowercase_							:					Optional[int]=768				,      lowercase_							:					Tuple=12				,      lowercase_							:					Optional[int]=12				,      lowercase_							:					Any=3072				,      lowercase_							:					int="gelu"				,      lowercase_							:					List[Any]=0.1				,      lowercase_							:					Any=0.1				,      lowercase_							:					str=512				,      lowercase_							:					Any=2				,      lowercase_							:					Any=3				,      lowercase_							:					List[Any]=False				,      lowercase_							:					List[Any]=0.02				,      lowercase_							:					str=1e-1_2				,      lowercase_							:					int=0				,      lowercase_							:					str="absolute"				,      lowercase_							:					Dict=True				,      lowercase_							:					List[str]=None				,      **lowercase_							:					Dict				,      )		->     Union[str, Any]:


   """simple docstring"""







   super().__init__(pad_token_id=lowercase_				,      **lowercase_)

   _UpperCamelCase				=  vocab_size
   _UpperCamelCase				=  hidden_size
   _UpperCamelCase				=  num_hidden_layers
   _UpperCamelCase				=  num_attention_heads
   _UpperCamelCase				=  hidden_act
   _UpperCamelCase				=  intermediate_size
   _UpperCamelCase				=  hidden_dropout_prob
   _UpperCamelCase				=  attention_probs_dropout_prob
   _UpperCamelCase				=  max_position_embeddings
   _UpperCamelCase				=  type_vocab_size
   _UpperCamelCase				=  task_type_vocab_size
   _UpperCamelCase				=  use_task_id
   _UpperCamelCase				=  initializer_range
   _UpperCamelCase				=  layer_norm_eps
   _UpperCamelCase				=  position_embedding_type
   _UpperCamelCase				=  use_cache
   _UpperCamelCase				=  classifier_dropout




class  _UpperCAmelCase       (			lowerCAmelCase   ):



 '''simple docstring'''




 @property
 def 						__UpperCAmelCase							(						self							:					Optional[int])		->     Mapping[str, Mapping[int, str]]:


   """simple docstring"""







   if self.task == "multiple-choice":
     _UpperCamelCase				=  {0: "batch", 1: "choice", 2: "sequence"}
   else:
     _UpperCamelCase				=  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("token_type_ids", dynamic_axis),
           ("task_type_ids", dynamic_axis),
       ])




