


'''simple docstring'''

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


lowerCAmelCase :Optional[int]             =     logging.get_logger(__name__)

lowerCAmelCase :Any             =     {
    """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class _lowerCamelCase			(		lowercase_				):



		'''simple docstring'''







		A_      :     Any        =							'''xglm'''
		A_      :     List[str]        =							['''past_key_values''']

		A_      :     Optional[Any]        =							{
		    '''num_attention_heads''': '''attention_heads''',
		    '''hidden_size''': '''d_model''',
		    '''num_hidden_layers''': '''num_layers''',
		}







		def __init__(   self						:		Tuple    ,	_A						:		int=256008    ,	_A						:		Optional[Any]=2048    ,	_A						:		Dict=1024    ,	_A						:		int=4096    ,	_A						:		List[str]=24    ,	_A						:		Optional[int]=16    ,	_A						:		List[Any]="gelu"    ,	_A						:		Optional[Any]=0.1    ,	_A						:		List[str]=0.1    ,	_A						:		List[str]=0.0    ,	_A						:		str=0.0    ,	_A						:		List[Any]=0.02    ,	_A						:		Union[str, Any]=True    ,	_A						:		Union[str, Any]=True    ,	_A						:		Union[str, Any]=2    ,	_A						:		Optional[Any]=1    ,	_A						:		Any=0    ,	_A						:		str=2    ,	**_A						:		Any    ,	)    ->					Tuple:
			__magic_name__			:       List[Any]	      =	vocab_size
			__magic_name__			:       Dict	      =	max_position_embeddings
			__magic_name__			:       Optional[int]	      =	d_model
			__magic_name__			:       Tuple	      =	ffn_dim
			__magic_name__			:       List[Any]	      =	num_layers
			__magic_name__			:       Union[str, Any]	      =	attention_heads
			__magic_name__			:       Tuple	      =	activation_function
			__magic_name__			:       Union[str, Any]	      =	dropout
			__magic_name__			:       List[Any]	      =	attention_dropout
			__magic_name__			:       Optional[Any]	      =	activation_dropout
			__magic_name__			:       Dict	      =	layerdrop
			__magic_name__			:       Dict	      =	init_std
			__magic_name__			:       Optional[int]	      =	scale_embedding  # scale factor will be sqrt(d_model) if True
			__magic_name__			:       Optional[int]	      =	use_cache

			super().__init__(
			    pad_token_id=_A    ,	bos_token_id=_A    ,	eos_token_id=_A    ,	decoder_start_token_id=_A    ,	**_A    ,	)


_UpperCAmelCase						:  Any									=			6_5521





def    __lowerCamelCase      (					UpperCamelCase__ ):







							'''simple docstring'''






							snake_case_     							=       1
							snake_case_     							=       0
							for plain_chr in plain_text:
														snake_case_     							=       (a + ord(UpperCamelCase__ )) % MOD_ADLER
														snake_case_     							=       (b + a) % MOD_ADLER
							return (b << 16) | a




