






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A      :     Union[str, Any]       	={'''configuration_regnet''': ['''REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RegNetConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     int       	=[
			    '''REGNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''RegNetForImageClassification''',
			    '''RegNetModel''',
			    '''RegNetPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Optional[int]       	=[
			    '''TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TFRegNetForImageClassification''',
			    '''TFRegNetModel''',
			    '''TFRegNetPreTrainedModel''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A      :     Union[str, Any]       	=[
			    '''FlaxRegNetForImageClassification''',
			    '''FlaxRegNetModel''',
			    '''FlaxRegNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_regnet import (
						    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RegNetForImageClassification,
						    RegNetModel,
						    RegNetPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_regnet import (
						    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRegNetForImageClassification,
						    TFRegNetModel,
						    TFRegNetPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_regnet import (
						    FlaxRegNetForImageClassification,
						    FlaxRegNetModel,
						    FlaxRegNetPreTrainedModel,
						)


else:
			import sys

			_A      :     List[Any]       	=_LazyModule(__name__, globals()['''__file__'''], _import_structure)









'''simple docstring'''







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


_A      :     Union[str, Any]       	=logging.get_logger(__name__)

_A      :     Tuple       	={
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}

class _lowercase      (							_lowercase						):
							a							 =			"""splinter"""




							def __init__(				self:							List[str]     ,				UpperCamelCase__:							List[Any]=30_522     ,				UpperCamelCase__:							Union[str, Any]=768     ,				UpperCamelCase__:							str=12     ,				UpperCamelCase__:							Dict=12     ,				UpperCamelCase__:							Tuple=3_072     ,				UpperCamelCase__:							Optional[int]="gelu"     ,				UpperCamelCase__:							Optional[int]=0.1     ,				UpperCamelCase__:							str=0.1     ,				UpperCamelCase__:							List[Any]=512     ,				UpperCamelCase__:							List[str]=2     ,				UpperCamelCase__:							Any=0.02     ,				UpperCamelCase__:							Optional[Any]=1e-12     ,				UpperCamelCase__:							Dict=True     ,				UpperCamelCase__:							str=0     ,				UpperCamelCase__:							int=104     ,				**UpperCamelCase__:							Any     ,				):
														super().__init__(pad_token_id=UpperCamelCase__     ,				**UpperCamelCase__	)

														lowerCamelCase__   :      Union[str, Any]			    =			vocab_size
														lowerCamelCase__   :      str			    =			max_position_embeddings
														lowerCamelCase__   :      Tuple			    =			hidden_size
														lowerCamelCase__   :      Dict			    =			num_hidden_layers
														lowerCamelCase__   :      Tuple			    =			num_attention_heads
														lowerCamelCase__   :      Tuple			    =			intermediate_size
														lowerCamelCase__   :      Any			    =			hidden_act
														lowerCamelCase__   :      int			    =			hidden_dropout_prob
														lowerCamelCase__   :      List[str]			    =			attention_probs_dropout_prob
														lowerCamelCase__   :      Dict			    =			initializer_range
														lowerCamelCase__   :      List[Any]			    =			type_vocab_size
														lowerCamelCase__   :      Tuple			    =			layer_norm_eps
														lowerCamelCase__   :      Optional[Any]			    =			use_cache
														lowerCamelCase__   :      Optional[Any]			    =			question_token_id

