code: 
  
  
  from dataclasses import dataclass
  
  UpperCamelCase__				       =			'0.1'
  UpperCamelCase__				       =			'Lucia Harcekova'
  
  @dataclass
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
       UpperCAmelCase_         =  4_2
       UpperCAmelCase_         =  4_2
       UpperCAmelCase_         =  4_2
  
  
  
  
       def __repr__( self       :       Union[str, Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              return F'({self.offset}, {self.length}, {self.indicator})'
  
  
  
  
  class 						UpperCAmelCase__   :
  
  
  
       '''simple docstring'''
  
  
  
  
       def __init__( self       :       Optional[Any]	,      UpperCamelCase       :       Union[str, Any] = 13	,      UpperCamelCase       :       str = 6		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Optional[int]           =			window_size
              _lowercase					:				Optional[Any]           =			lookahead_buffer_size
              _lowercase					:				Tuple           =			self.window_size - self.lookahead_buffer_size
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       Optional[int]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				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					:				Union[str, Any]           =			self._find_encoding_token(_lowerCamelCase	,      _lowerCamelCase		)
  
                     # 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(_lowerCamelCase		) > self.search_buffer_size:
                            _lowercase					:				Union[str, Any]           =			search_buffer[-self.search_buffer_size :]
  
                     # update the text
                     _lowercase					:				Union[str, Any]           =			text[token.length + 1 :]
  
                     # append the token to output
                     output.append(_lowerCamelCase		)
  
              return output
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       List[Any]		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              _lowercase					:				Tuple           =			""""""
  
              for token in tokens:
                     for _ in range(token.length		):
                            output += output[-token.offset]
                     output += token.indicator
  
              return output
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Any	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if not text:
                     raise ValueError('''We need some text to work with.'''		)
  
              # Initialise result parameters to default values
              _lowercase					:				str           =			0, 0
  
              if not search_buffer:
                     return Token(_lowerCamelCase	,      _lowerCamelCase	,      text[length]		)
  
              for i, character in enumerate(_lowerCamelCase		):
                     _lowercase					:				List[Any]           =			len(_lowerCamelCase		) - i
                     if character == text[0]:
                            _lowercase					:				Tuple           =			self._match_length_from_index(_lowerCamelCase	,      _lowerCamelCase	,      0	,      _lowerCamelCase		)
                            # 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					:				Union[str, Any]           =			found_offset, found_length
  
              return Token(_lowerCamelCase	,      _lowerCamelCase	,      text[length]		)
  
  
  
  
       def   lowerCAmelCase_   ( self       :       Dict	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       List[str]	,      UpperCamelCase       :       Dict		):
  
  
  
  
  
              """simple docstring"""
  
  
  
              if not text or text[text_index] != window[window_index]:
                     return 0
              return 1 + self._match_length_from_index(
                  _lowerCamelCase	,      window + text[text_index]	,      text_index + 1	,      window_index + 1		)
  
  
  if __name__ == "__main__":
     from doctest import testmod
  
     testmod()
     # Initialize compressor class
     UpperCamelCase__				       =			LZaaCompressor(window_size=13, lookahead_buffer_size=6)
  
     # Example
     UpperCamelCase__				       =			'cabracadabrarrarrad'
     UpperCamelCase__				       =			lzaa_compressor.compress(TEXT)
     print(lzaa_compressor.compress('ababcbababaa'))
     UpperCamelCase__				       =			lzaa_compressor.decompress(compressed_text)
     assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."
style_context: 
  
  
  import os
  from pathlib import Path
  from typing import Dict, List
  
  import fire
  import torch
  
  from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
  from transformers.utils.logging import get_logger
  
  
  UpperCamelCase__				       =			get_logger(__name__)
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_			)						->							List[str]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         if text.startswith(UpperCAmelCase_			):
                return text[len(UpperCAmelCase_			) :]
         return text  # or whatever
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Optional[int]:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         return {remove_prefix(UpperCAmelCase_    ,     '''model.'''			): v for k, v in sd.items()}
  
  def   UpperCamelCase__   (    UpperCAmelCase_			)						->							Any:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				Optional[int]           =			{}
         for k in state_dicts[0].keys():
                _lowercase					:				Tuple           =			[sd[k] for sd in state_dicts]
                _lowercase					:				Union[str, Any]           =			sum(UpperCAmelCase_			) / len(UpperCAmelCase_			)
                assert isinstance(UpperCAmelCase_    ,     torch.Tensor			)
                _lowercase					:				Union[str, Any]           =			new_t
         return new_sd
  
  def   UpperCamelCase__   (    UpperCAmelCase_    ,     UpperCAmelCase_    ,     UpperCAmelCase_			)						->							None:
  
  
  
  
         '''simple docstring'''
  
  
  
  
  
  
         _lowercase					:				List[str]           =			AutoModelForSeqaSeqLM.from_pretrained(UpperCAmelCase_			)
         if os.path.isfile(UpperCAmelCase_			):
                _lowercase					:				Optional[Any]           =			[pl_ckpt_path]
         else:
                assert os.path.isdir(UpperCAmelCase_			)
                _lowercase					:				List[str]           =			list(Path(UpperCAmelCase_			).glob('''*.ckpt'''			)			)
                assert ckpt_files, F'could not find any ckpt files inside the {pl_ckpt_path} directory'
  
         if len(UpperCAmelCase_			) > 1:
                logger.info(F'averaging the weights of {ckpt_files}'			)
  
         _lowercase					:				List[Any]           =			[sanitize(torch.load(UpperCAmelCase_    ,     map_location='''cpu'''			)['''state_dict''']			) for x in ckpt_files]
         _lowercase					:				Optional[Any]           =			average_state_dicts(UpperCAmelCase_			)
  
         _lowercase       ,	_lowercase					:				str           =			hf_model.load_state_dict(UpperCAmelCase_    ,     strict=UpperCAmelCase_			)
         assert not missing, F'missing keys: {missing}'
         hf_model.save_pretrained(UpperCAmelCase_			)
         try:
                _lowercase					:				Union[str, Any]           =			AutoTokenizer.from_pretrained(UpperCAmelCase_			)
                tok.save_pretrained(UpperCAmelCase_			)
         except Exception:
                pass
                # dont copy tokenizer if cant
  
  
  if __name__ == "__main__":
     fire.Fire(convert_pl_to_hf)