





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__:			Union[str, Any]       =       "biogpt"






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







							"""simple docstring"""


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_SCREAMING_SNAKE_CASE      	=		{"""configuration_swin""": ["""SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SwinConfig""", """SwinOnnxConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """SwinForImageClassification""",
						    """SwinForMaskedImageModeling""",
						    """SwinModel""",
						    """SwinPreTrainedModel""",
						    """SwinBackbone""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_SCREAMING_SNAKE_CASE      	=		[
						    """TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFSwinForImageClassification""",
						    """TFSwinForMaskedImageModeling""",
						    """TFSwinModel""",
						    """TFSwinPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_swin import (
												    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SwinBackbone,
												    SwinForImageClassification,
												    SwinForMaskedImageModeling,
												    SwinModel,
												    SwinPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_swin import (
												    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFSwinForImageClassification,
												    TFSwinForMaskedImageModeling,
												    TFSwinModel,
												    TFSwinPreTrainedModel,
												)

else:
						import sys

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

