





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


_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=						{
    """microsoft/biogpt""": """https://huggingface.co/microsoft/biogpt/resolve/main/config.json""",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}
class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]	 =    "biogpt"






						def __init__( self : Tuple						,				_A : Optional[int]=42384						,				_A : List[str]=1024						,				_A : Dict=24						,				_A : str=16						,				_A : Optional[Any]=4096						,				_A : int="gelu"						,				_A : Optional[Any]=0.1						,				_A : Union[str, Any]=0.1						,				_A : Union[str, Any]=1024						,				_A : List[str]=0.0_2						,				_A : Optional[Any]=1E-12						,				_A : Any=True						,				_A : Union[str, Any]=True						,				_A : Tuple=0.0						,				_A : Optional[int]=0.0						,				_A : str=1						,				_A : Any=0						,				_A : Dict=2						,				**_A : List[str]						,				)							->      Any:







							"""simple docstring"""


							snake_case_						:     str     =		vocab_size
							snake_case_						:     str     =		max_position_embeddings
							snake_case_						:     int     =		hidden_size
							snake_case_						:     Tuple     =		num_hidden_layers
							snake_case_						:     Optional[Any]     =		num_attention_heads
							snake_case_						:     Optional[int]     =		intermediate_size
							snake_case_						:     Tuple     =		hidden_act
							snake_case_						:     Dict     =		hidden_dropout_prob
							snake_case_						:     int     =		attention_probs_dropout_prob
							snake_case_						:     List[Any]     =		initializer_range
							snake_case_						:     str     =		layer_norm_eps
							snake_case_						:     Dict     =		scale_embedding
							snake_case_						:     Dict     =		use_cache
							snake_case_						:     Optional[Any]     =		layerdrop
							snake_case_						:     List[Any]     =		activation_dropout
							super().__init__(pad_token_id=_A						,				bos_token_id=_A						,				eos_token_id=_A						,				**_A		)








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE 		=						{
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE 		=						[
						    """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InformerForPrediction""",
						    """InformerModel""",
						    """InformerPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_informer import (
												    INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InformerForPrediction,
												    InformerModel,
												    InformerPreTrainedModel,
												)

else:
						import sys

						_SCREAMING_SNAKE_CASE 		=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

