

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


A_			:					Optional[int]    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}



class        _a		(_SCREAMING_SNAKE_CASE							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''data2vec-vision'''






					def __init__(					self       ,			A__=768       ,			A__=12       ,			A__=12       ,			A__=3072       ,			A__="gelu"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0_2       ,			A__=1e-12       ,			A__=224       ,			A__=16       ,			A__=3       ,			A__=False       ,			A__=False       ,			A__=False       ,			A__=False       ,			A__=0.1       ,			A__=0.1       ,			A__=True       ,			A__=[3, 5, 7, 11]       ,			A__=[1, 2, 3, 6]       ,			A__=True       ,			A__=0.4       ,			A__=256       ,			A__=1       ,			A__=False       ,			A__=255       ,			**A__       ,			):
						super().__init__(**_lowercase     )

						A__				:       List[Any]	  =						hidden_size
						A__				:       str	  =						num_hidden_layers
						A__				:       List[Any]	  =						num_attention_heads
						A__				:       str	  =						intermediate_size
						A__				:       int	  =						hidden_act
						A__				:       str	  =						hidden_dropout_prob
						A__				:       Any	  =						attention_probs_dropout_prob
						A__				:       int	  =						initializer_range
						A__				:       List[Any]	  =						layer_norm_eps

						A__				:       List[str]	  =						image_size
						A__				:       Optional[Any]	  =						patch_size
						A__				:       str	  =						num_channels
						A__				:       Tuple	  =						use_mask_token
						A__				:       List[Any]	  =						use_absolute_position_embeddings
						A__				:       Optional[Any]	  =						use_relative_position_bias
						A__				:       int	  =						use_shared_relative_position_bias
						A__				:       Any	  =						layer_scale_init_value
						A__				:       List[Any]	  =						drop_path_rate
						A__				:       Any	  =						use_mean_pooling
						# decode head attributes (semantic segmentation)
						A__				:       str	  =						out_indices
						A__				:       Dict	  =						pool_scales
						# auxiliary head attributes (semantic segmentation)
						A__				:       int	  =						use_auxiliary_head
						A__				:       int	  =						auxiliary_loss_weight
						A__				:       int	  =						auxiliary_channels
						A__				:       Dict	  =						auxiliary_num_convs
						A__				:       int	  =						auxiliary_concat_input
						A__				:       Tuple	  =						semantic_loss_ignore_index



class        _a		(_SCREAMING_SNAKE_CASE							):


					'''simple docstring'''






					UpperCAmelCase__:  Union[str, Any]    				=			version.parse('''1.11'''							)






					@property
					def     __A     (					self     ):
						return OrderedDict(
						    [
						        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
						    ]     )






					@property
					def     __A     (					self     ):
						return 1e-4





'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase          =						{"""configuration_vit_mae""": ["""VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTMAEConfig"""]}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """ViTMAEForPreTraining""",
						    """ViTMAELayer""",
						    """ViTMAEModel""",
						    """ViTMAEPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase          =						[
						    """TFViTMAEForPreTraining""",
						    """TFViTMAEModel""",
						    """TFViTMAEPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_vit_mae import (
												    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ViTMAEForPreTraining,
												    ViTMAELayer,
												    ViTMAEModel,
												    ViTMAEPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
						import sys

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