




'''simple docstring'''


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


_snake_case   :   str     							=	logging.get_logger(__name__)

# TODO: upload to AWS
_snake_case   :   Tuple     							=	{
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}

class     A					(	_a					):
							lowercase_           =       'retribert'







							def __init__(						self :						Union[str, Any]							,    lowerCAmelCase_ :						Optional[int]=3_05_22							,    lowerCAmelCase_ :						List[str]=7_68							,    lowerCAmelCase_ :						Optional[Any]=8							,    lowerCAmelCase_ :						List[str]=12							,    lowerCAmelCase_ :						int=30_72							,    lowerCAmelCase_ :						str="gelu"							,    lowerCAmelCase_ :						Tuple=0.1							,    lowerCAmelCase_ :						List[str]=0.1							,    lowerCAmelCase_ :						Any=5_12							,    lowerCAmelCase_ :						Dict=2							,    lowerCAmelCase_ :						int=0.0_2							,    lowerCAmelCase_ :						str=1e-12							,    lowerCAmelCase_ :						Optional[int]=True							,    lowerCAmelCase_ :						str=1_28							,    lowerCAmelCase_ :						Dict=0							,    **lowerCAmelCase_ :						Optional[Any]							,    )    ->							Dict:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_							,    **lowerCAmelCase_      )

														_a			  =       vocab_size
														_a			  =       hidden_size
														_a			  =       num_hidden_layers
														_a			  =       num_attention_heads
														_a			  =       hidden_act
														_a			  =       intermediate_size
														_a			  =       hidden_dropout_prob
														_a			  =       attention_probs_dropout_prob
														_a			  =       max_position_embeddings
														_a			  =       type_vocab_size
														_a			  =       initializer_range
														_a			  =       layer_norm_eps
														_a			  =       share_encoders
														_a			  =       projection_dim







'''simple docstring'''


from typing import TYPE_CHECKING

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


_snake_case   :   List[Any]     							=	{'configuration_beit': ['BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BeitConfig', 'BeitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   Any     							=	['BeitFeatureExtractor']
				_snake_case   :   Any     							=	['BeitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'BEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BeitForImageClassification',
				    'BeitForMaskedImageModeling',
				    'BeitForSemanticSegmentation',
				    'BeitModel',
				    'BeitPreTrainedModel',
				]


try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_snake_case   :   List[str]     							=	[
				    'FlaxBeitForImageClassification',
				    'FlaxBeitForMaskedImageModeling',
				    'FlaxBeitModel',
				    'FlaxBeitPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_beit import BeitFeatureExtractor
								from .image_processing_beit import BeitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_beit import (
								    BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BeitForImageClassification,
								    BeitForMaskedImageModeling,
								    BeitForSemanticSegmentation,
								    BeitModel,
								    BeitPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_beit import (
								    FlaxBeitForImageClassification,
								    FlaxBeitForMaskedImageModeling,
								    FlaxBeitModel,
								    FlaxBeitPreTrainedModel,
								)


else:
				import sys

				_snake_case   :   Union[str, Any]     							=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

