from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{
    'shi-labs/dinat-mini-in1k-224': 'https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}
class     a_			(						SCREAMING_SNAKE_CASE	,					SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'dinat'

 UpperCAmelCase_     			= {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }




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







        '''simple docstring'''



        super().__init__(**lowercase__)

        lowerCAmelCase__					     = patch_size
        lowerCAmelCase__					     = num_channels
        lowerCAmelCase__					     = embed_dim
        lowerCAmelCase__					     = depths
        lowerCAmelCase__					     = len(lowercase__)
        lowerCAmelCase__					     = num_heads
        lowerCAmelCase__					     = kernel_size
        lowerCAmelCase__					     = dilations
        lowerCAmelCase__					     = mlp_ratio
        lowerCAmelCase__					     = qkv_bias
        lowerCAmelCase__					     = hidden_dropout_prob
        lowerCAmelCase__					     = attention_probs_dropout_prob
        lowerCAmelCase__					     = drop_path_rate
        lowerCAmelCase__					     = hidden_act
        lowerCAmelCase__					     = layer_norm_eps
        lowerCAmelCase__					     = initializer_range
        # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        lowerCAmelCase__					     = int(embed_dim * 2 ** (len(lowercase__) - 1))
        lowerCAmelCase__					     = layer_scale_init_value
        lowerCAmelCase__					     = ['stem'] + [F"""stage{idx}""" for idx in range(1      ,   len(lowercase__) + 1)]
        lowerCAmelCase__     , lowerCAmelCase__					     = get_aligned_output_features_output_indices(
            out_features=lowercase__      ,   out_indices=lowercase__      ,   stage_names=self.stage_names)

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= 1
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return self.__class__(**{k: copy.deepcopy(lowercase__) for k, v in self.__dict__.items()})
