





'''simple docstring'''

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


a   	=    logging.get_logger(__name__)

a   	=    {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class __a			( _snake_case      ):
   __UpperCamelCase :			Tuple						   =						'glpn'





   def __init__(       self					:      Dict					,lowerCamelCase					:      Tuple=3					,lowerCamelCase					:      Optional[Any]=4					,lowerCamelCase					:      Dict=[2, 2, 2, 2]					,lowerCamelCase					:      Union[str, Any]=[8, 4, 2, 1]					,lowerCamelCase					:      Any=[32, 64, 160, 256]					,lowerCamelCase					:      Tuple=[7, 3, 3, 3]					,lowerCamelCase					:      List[str]=[4, 2, 2, 2]					,lowerCamelCase					:      str=[1, 2, 5, 8]					,lowerCamelCase					:      Union[str, Any]=[4, 4, 4, 4]					,lowerCamelCase					:      Any="gelu"					,lowerCamelCase					:      int=0.0					,lowerCamelCase					:      str=0.0					,lowerCamelCase					:      Dict=0.02					,lowerCamelCase					:      Dict=0.1					,lowerCamelCase					:      Union[str, Any]=1E-6					,lowerCamelCase					:      Dict=64					,lowerCamelCase					:      Optional[int]=10					,lowerCamelCase					:      int=-1					,**lowerCamelCase					:      Optional[Any]					,):

      '''simple docstring'''

      super().__init__(**lowerCamelCase     )

      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							num_encoder_blocks
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							sr_ratios
      __SCREAMING_SNAKE_CASE   				=							hidden_sizes
      __SCREAMING_SNAKE_CASE   				=							patch_sizes
      __SCREAMING_SNAKE_CASE   				=							strides
      __SCREAMING_SNAKE_CASE   				=							mlp_ratios
      __SCREAMING_SNAKE_CASE   				=							num_attention_heads
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							drop_path_rate
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							decoder_hidden_size
      __SCREAMING_SNAKE_CASE   				=							max_depth
      __SCREAMING_SNAKE_CASE   				=							head_in_index







'''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__)
