




'''simple docstring'''






from dataclasses import dataclass

lowercase	:      Any	=       '''0.1'''
lowercase	:      int	=       '''Lucia Harcekova'''

@dataclass
class   SCREAMING_SNAKE_CASE_		:
 __snake_case	:     int
 __snake_case	:     int
 __snake_case	:     str



 def __repr__(  self ):






        '''simple docstring'''




        return f"""({self.offset}, {self.length}, {self.indicator})"""






class   SCREAMING_SNAKE_CASE_		:



 def __init__(  self   ,		A = 13   ,		A = 6 ):






        '''simple docstring'''




        lowercase  :		str						=      window_size
        lowercase  :		Tuple						=      lookahead_buffer_size
        lowercase  :		Union[str, Any]						=      self.window_size - self.lookahead_buffer_size



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		Optional[int]						=      []
        lowercase  :		Union[str, Any]						=      ''''''

        # 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(A   ,		A )

               # 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(A ) > self.search_buffer_size:
                      lowercase  :		List[str]						=      search_buffer[-self.search_buffer_size :]

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

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

        return output



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		Dict						=      ''''''

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

        return output



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A ):






        '''simple docstring'''




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

        # Initialise result parameters to default values
        lowercase  :		Any						=      0, 0

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

        for i, character in enumerate(A ):
               lowercase  :		str						=      len(A ) - i
               if character == text[0]:
                      lowercase  :		Dict						=      self._match_length_from_index(A   ,		A   ,		0   ,		A )
                      # 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  :		List[Any]						=      found_offset, found_length

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





 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A   ,		A   ,		A ):






        '''simple docstring'''




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


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

      testmod()
      # Initialize compressor class
      lowercase	:      Union[str, Any]	=       LZaaCompressor(window_size=13, lookahead_buffer_size=6)

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










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase	:      int				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




