



'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}

class 	__magic_name__						(    _UpperCamelCase   ):
							lowerCAmelCase       :    int   				=					'roberta-prelayernorm'







							def __init__(							self					:       int	,_UpperCAmelCase					:       Any=50265	,_UpperCAmelCase					:       int=768	,_UpperCAmelCase					:       Any=12	,_UpperCAmelCase					:       Union[str, Any]=12	,_UpperCAmelCase					:       Tuple=3072	,_UpperCAmelCase					:       List[str]="gelu"	,_UpperCAmelCase					:       Tuple=0.1	,_UpperCAmelCase					:       str=0.1	,_UpperCAmelCase					:       Tuple=512	,_UpperCAmelCase					:       Tuple=2	,_UpperCAmelCase					:       List[Any]=0.02	,_UpperCAmelCase					:       Any=1E-12	,_UpperCAmelCase					:       List[str]=1	,_UpperCAmelCase					:       List[Any]=0	,_UpperCAmelCase					:       Optional[int]=2	,_UpperCAmelCase					:       int="absolute"	,_UpperCAmelCase					:       List[Any]=True	,_UpperCAmelCase					:       Tuple=None	,**_UpperCAmelCase					:       int	,):
									super().__init__(pad_token_id=_UpperCAmelCase	,bos_token_id=_UpperCAmelCase	,eos_token_id=_UpperCAmelCase	,**_UpperCAmelCase		)

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

class 	__magic_name__						(    _UpperCamelCase   ):







							@property
							def 							__lowercase			(							self					:       Union[str, Any]		):
									if self.task == "multiple-choice":
											_a     :      List[str]        =							{0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
											_a     :      List[Any]        =							{0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('input_ids', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)









'''simple docstring'''

from typing import TYPE_CHECKING

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


__lowerCAmelCase									=   {'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


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

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase									=   [
							    '''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

							__lowerCAmelCase									=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




