
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "shi-labs/nat-mini-in1k-224": "https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json",
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""nat"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			List[Any]=4  ,	UpperCamelCase__				:			Optional[Any]=3  ,	UpperCamelCase__				:			Tuple=64  ,	UpperCamelCase__				:			List[Any]=[3, 4, 6, 5]  ,	UpperCamelCase__				:			Dict=[2, 4, 8, 16]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Union[str, Any]=3.0  ,	UpperCamelCase__				:			Optional[int]=True  ,	UpperCamelCase__				:			Tuple=0.0  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			List[Any]=0.1  ,	UpperCamelCase__				:			Union[str, Any]="gelu"  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			Optional[Any]=1e-5  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			Optional[int]=None  ,	**UpperCamelCase__				:			Optional[int]  ,	)						->			Tuple:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       Optional[Any]     			=						num_channels
				snake_case  :       Tuple     			=						embed_dim
				snake_case  :       int     			=						depths
				snake_case  :       List[str]     			=						len(UpperCamelCase__      )
				snake_case  :       Any     			=						num_heads
				snake_case  :       str     			=						kernel_size
				snake_case  :       Any     			=						mlp_ratio
				snake_case  :       Tuple     			=						qkv_bias
				snake_case  :       Any     			=						hidden_dropout_prob
				snake_case  :       str     			=						attention_probs_dropout_prob
				snake_case  :       int     			=						drop_path_rate
				snake_case  :       Tuple     			=						hidden_act
				snake_case  :       Union[str, Any]     			=						layer_norm_eps
				snake_case  :       Dict     			=						initializer_range
				# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       Tuple     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						layer_scale_init_value
				snake_case  :       Dict     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )




'''simple docstring'''
import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowercase__						       =      logging.get_logger(__name__)






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			Optional[Any]  ,	*UpperCamelCase__				:			Optional[Any]  ,	**UpperCamelCase__				:			str      )						->			None:






				"""simple docstring"""







				warnings.warn(
				    '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use GLPNImageProcessor instead.'''  ,	UpperCamelCase__  ,	)
				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )


