


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."



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)