'''simple docstring'''
import math
import sys







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Any		    =  ''
    try:
        with open(_a				,     'rb'			) as binary_file:
            __UpperCamelCase	:	Tuple		    =  binary_file.read()
        for dat in data:
            __UpperCamelCase	:	Optional[Any]		    =  f"""{dat:08b}"""
            result += curr_byte
        return result
    except OSError:
        print('File not accessible'			)
        sys.exit()







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  {'0': '0', '1': '1'}
    __UpperCamelCase    ,					__UpperCamelCase	:	Any		    =  '', ''
    __UpperCamelCase	:	List[Any]		    =  len(_a			)

    for i in range(len(_a			)			):
        curr_string += data_bits[i]
        if curr_string not in lexicon:
            continue

        __UpperCamelCase	:	Union[str, Any]		    =  lexicon[curr_string]
        result += last_match_id
        __UpperCamelCase	:	Tuple		    =  last_match_id + '0'

        if math.loga(_a			).is_integer():
            __UpperCamelCase	:	List[str]		    =  {}
            for curr_key in list(_a			):
                __UpperCamelCase	:	Union[str, Any]		    =  lexicon.pop(_a			)
            __UpperCamelCase	:	Union[str, Any]		    =  new_lex

        __UpperCamelCase	:	Dict		    =  last_match_id + '1'
        index += 1
        __UpperCamelCase	:	int		    =  ''
    return result







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  8
    try:
        with open(_a				,     'wb'			) as opened_file:
            __UpperCamelCase	:	List[Any]		    =  [
                to_write[i : i + byte_length]
                for i in range(0				,     len(_a			)				,     _a			)
            ]

            if len(result_byte_array[-1]			) % byte_length == 0:
                result_byte_array.append('10000000'			)
            else:
                result_byte_array[-1] += "1" + "0" * (
                    byte_length - len(result_byte_array[-1]			) - 1
                )

            for elem in result_byte_array[:-1]:
                opened_file.write(int(_a				,     2			).to_bytes(1				,     byteorder='big'			)			)
    except OSError:
        print('File not accessible'			)
        sys.exit()







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Tuple		    =  0
    for letter in data_bits:
        if letter == "1":
            break
        counter += 1

    __UpperCamelCase	:	List[str]		    =  data_bits[counter:]
    __UpperCamelCase	:	Dict		    =  data_bits[counter + 1 :]
    return data_bits







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  read_file_binary(_a			)
    __UpperCamelCase	:	Union[str, Any]		    =  remove_prefix(_a			)
    __UpperCamelCase	:	List[str]		    =  decompress_data(_a			)
    write_file_binary(_a				,     _a			)


if __name__ == "__main__":
  compress(sys.argv[1], sys.argv[2])




'''simple docstring'''
import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a=  logging.get_logger(__name__)

a=  {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						'''xlnet'''
     SCREAMING_SNAKE_CASE__		   =						['''mems''']
     SCREAMING_SNAKE_CASE__		   =						{
         '''n_token''': '''vocab_size''',  # Backward compatibility
         '''hidden_size''': '''d_model''',
         '''num_attention_heads''': '''n_head''',
         '''num_hidden_layers''': '''n_layer''',
     }







     def __init__(		self     ,			_lowerCamelCase=3_2_0_0_0     ,			_lowerCamelCase=1_0_2_4     ,			_lowerCamelCase=2_4     ,			_lowerCamelCase=1_6     ,			_lowerCamelCase=4_0_9_6     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="bi"     ,			_lowerCamelCase=0.0_2     ,			_lowerCamelCase=1E-1_2     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=None     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=-1     ,			_lowerCamelCase=False     ,			_lowerCamelCase="last"     ,			_lowerCamelCase=True     ,			_lowerCamelCase="tanh"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=5     ,			_lowerCamelCase=1     ,			_lowerCamelCase=2     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[str]		    =  vocab_size
         __UpperCamelCase	:	List[Any]		    =  d_model
         __UpperCamelCase	:	List[str]		    =  n_layer
         __UpperCamelCase	:	Optional[Any]		    =  n_head
         if d_model % n_head != 0:
             raise ValueError(f"""'d_model % n_head' ({d_model % n_head}) should be equal to 0"""    )
         if "d_head" in kwargs:
             if kwargs["d_head"] != d_model // n_head:
                 raise ValueError(
                     f"""`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"""    )
         __UpperCamelCase	:	str		    =  d_model // n_head
         __UpperCamelCase	:	Union[str, Any]		    =  ff_activation
         __UpperCamelCase	:	str		    =  d_inner
         __UpperCamelCase	:	Any		    =  untie_r
         __UpperCamelCase	:	List[Any]		    =  attn_type

         __UpperCamelCase	:	str		    =  initializer_range
         __UpperCamelCase	:	Optional[int]		    =  layer_norm_eps

         __UpperCamelCase	:	Union[str, Any]		    =  dropout
         __UpperCamelCase	:	List[str]		    =  mem_len
         __UpperCamelCase	:	Optional[int]		    =  reuse_len
         __UpperCamelCase	:	str		    =  bi_data
         __UpperCamelCase	:	Tuple		    =  clamp_len
         __UpperCamelCase	:	List[Any]		    =  same_length

         __UpperCamelCase	:	Optional[Any]		    =  summary_type
         __UpperCamelCase	:	List[str]		    =  summary_use_proj
         __UpperCamelCase	:	Union[str, Any]		    =  summary_activation
         __UpperCamelCase	:	str		    =  summary_last_dropout
         __UpperCamelCase	:	Union[str, Any]		    =  start_n_top
         __UpperCamelCase	:	str		    =  end_n_top

         __UpperCamelCase	:	Optional[int]		    =  bos_token_id
         __UpperCamelCase	:	Optional[Any]		    =  pad_token_id
         __UpperCamelCase	:	List[Any]		    =  eos_token_id

         if "use_cache" in kwargs:
             warnings.warn(
                 'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
                 ' instead.'     ,			_lowerCamelCase     ,			)
             __UpperCamelCase	:	Optional[int]		    =  kwargs['use_cache']

         __UpperCamelCase	:	Optional[int]		    =  use_mems_eval
         __UpperCamelCase	:	Tuple		    =  use_mems_train
         super().__init__(pad_token_id=_lowerCamelCase     ,			bos_token_id=_lowerCamelCase     ,			eos_token_id=_lowerCamelCase     ,			**_lowerCamelCase    )







     @property
     def lowerCAmelCase     (		self    ):
         logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit."""    )
         return -1







     @max_position_embeddings.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         # Message copied from Transformer-XL documentation
         raise NotImplementedError(
             f"""The model {self.model_type} is one of the few models that has no sequence length limit."""    )



