


'''simple docstring'''







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


lowerCAmelCase    :     List[str]					=						logging.get_logger(__name__)

lowerCAmelCase    :     Optional[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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    "xglm"
     __magic_name__    			=    ["past_key_values"]

     __magic_name__    			=    {
         "num_attention_heads": "attention_heads",
         "hidden_size": "d_model",
         "num_hidden_layers": "num_layers",
     }







     def __init__( self      ,	snake_case__=25_6008      ,	snake_case__=2048      ,	snake_case__=1024      ,	snake_case__=4096      ,	snake_case__=24      ,	snake_case__=16      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=0.0      ,	snake_case__=0.0      ,	snake_case__=0.02      ,	snake_case__=True      ,	snake_case__=True      ,	snake_case__=2      ,	snake_case__=1      ,	snake_case__=0      ,	snake_case__=2      ,	**snake_case__      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       vocab_size
            _lowerCAmelCase    :							Any   			=       max_position_embeddings
            _lowerCAmelCase    :							str   			=       d_model
            _lowerCAmelCase    :							Any   			=       ffn_dim
            _lowerCAmelCase    :							Any   			=       num_layers
            _lowerCAmelCase    :							List[Any]   			=       attention_heads
            _lowerCAmelCase    :							Optional[Any]   			=       activation_function
            _lowerCAmelCase    :							Dict   			=       dropout
            _lowerCAmelCase    :							Tuple   			=       attention_dropout
            _lowerCAmelCase    :							int   			=       activation_dropout
            _lowerCAmelCase    :							Any   			=       layerdrop
            _lowerCAmelCase    :							List[str]   			=       init_std
            _lowerCAmelCase    :							Any   			=       scale_embedding  # scale factor will be sqrt(d_model) if True
            _lowerCAmelCase    :							Optional[int]   			=       use_cache

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





'''simple docstring'''







from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *

