






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


__UpperCamelCase					:		Tuple	  =						logging.get_logger(__name__)

__UpperCamelCase					:		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   __SCREAMING_SNAKE_CASE(	a_	):
							_UpperCAmelCase     =       "xglm"
							_UpperCAmelCase     =       ["past_key_values"]

							_UpperCAmelCase     =       {
							    "num_attention_heads": "attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "num_layers",
							}
							def __init__(     self:      Any       ,	UpperCamelCase:      List[str]=25_60_08       ,	UpperCamelCase:      Tuple=20_48       ,	UpperCamelCase:      List[str]=10_24       ,	UpperCamelCase:      Optional[int]=40_96       ,	UpperCamelCase:      Dict=24       ,	UpperCamelCase:      Tuple=16       ,	UpperCamelCase:      List[Any]="gelu"       ,	UpperCamelCase:      List[Any]=0.1       ,	UpperCamelCase:      Tuple=0.1       ,	UpperCamelCase:      Union[str, Any]=0.0       ,	UpperCamelCase:      List[str]=0.0       ,	UpperCamelCase:      int=0.02       ,	UpperCamelCase:      str=True       ,	UpperCamelCase:      str=True       ,	UpperCamelCase:      Optional[int]=2       ,	UpperCamelCase:      Any=1       ,	UpperCamelCase:      Optional[int]=0       ,	UpperCamelCase:      Optional[Any]=2       ,	**UpperCamelCase:      Dict       ,	)		->		Tuple:
											snake_case__									=     vocab_size
											snake_case__									=     max_position_embeddings
											snake_case__									=     d_model
											snake_case__									=     ffn_dim
											snake_case__									=     num_layers
											snake_case__									=     attention_heads
											snake_case__									=     activation_function
											snake_case__									=     dropout
											snake_case__									=     attention_dropout
											snake_case__									=     activation_dropout
											snake_case__									=     layerdrop
											snake_case__									=     init_std
											snake_case__									=     scale_embedding  # scale factor will be sqrt(d_model) if True
											snake_case__									=     use_cache

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












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__UpperCamelCase					:		str	  =						{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""BartphoTokenizer"""]

if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bartpho import BartphoTokenizer

else:
							import sys

							__UpperCamelCase					:		Any	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




