




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)














"""simple docstring"""





from dataclasses import dataclass

UpperCamelCase					=     """0.1"""
UpperCamelCase					=     """Lucia Harcekova"""







@dataclass
class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42





   def __repr__(       self)			->   str:




          '''simple docstring'''




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







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_ = 13					, UpperCAmelCase_ = 6)			->   None:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     window_size
          lowercase__:							Any         =     lookahead_buffer_size
          lowercase__:							Optional[Any]         =     self.window_size - self.lookahead_buffer_size





   def __lowercase							(       self					, UpperCAmelCase_)			->   list[Token]:




          '''simple docstring'''




          lowercase__:							Any         =     []
          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__:							Optional[Any]         =     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__:							List[Any]         =     search_buffer[-self.search_buffer_size :]

                 # update the text
                 lowercase__:							List[str]         =     text[token.length + 1 :]

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

          return output





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							int         =     ""

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

          return output





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Token:




          '''simple docstring'''




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

          # Initialise result parameters to default values
          lowercase__    ,      lowercase__:							Optional[int]         =     0, 0

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

          for i, character in enumerate(UpperCAmelCase_):
                 lowercase__:							Union[str, Any]         =     len(UpperCAmelCase_) - i
                 if character == text[0]:
                        lowercase__:							Optional[Any]         =     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__:							Any         =     found_offset, found_length

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





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




          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					=     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."






