"""simple docstring"""


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


__UpperCamelCase									=			logging.get_logger(__name__)

__UpperCamelCase									=			{
    '''facebook/levit-128S''': '''https://huggingface.co/facebook/levit-128S/resolve/main/config.json''',
    # See all LeViT models at https://huggingface.co/models?filter=levit
}




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      "levit"







       def __init__( self,							lowerCAmelCase__=224,							lowerCAmelCase__=3,							lowerCAmelCase__=3,							lowerCAmelCase__=2,							lowerCAmelCase__=1,							lowerCAmelCase__=16,							lowerCAmelCase__=[128, 256, 384],							lowerCAmelCase__=[4, 8, 12],							lowerCAmelCase__=[4, 4, 4],							lowerCAmelCase__=[16, 16, 16],							lowerCAmelCase__=0,							lowerCAmelCase__=[2, 2, 2],							lowerCAmelCase__=[2, 2, 2],							lowerCAmelCase__=0.02,							**lowerCAmelCase__,							)      ->      Optional[Any]:
            super().__init__(**lowerCAmelCase__)
            snake_case_					       =					image_size
            snake_case_					       =					num_channels
            snake_case_					       =					kernel_size
            snake_case_					       =					stride
            snake_case_					       =					padding
            snake_case_					       =					hidden_sizes
            snake_case_					       =					num_attention_heads
            snake_case_					       =					depths
            snake_case_					       =					key_dim
            snake_case_					       =					drop_path_rate
            snake_case_					       =					patch_size
            snake_case_					       =					attention_ratio
            snake_case_					       =					mlp_ratio
            snake_case_					       =					initializer_range
            snake_case_					       =					[
                ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
                ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
            ]




class  UpperCamelCase					(				lowerCAmelCase__   ):
       SCREAMING_SNAKE_CASE_			=      version.parse("1.11"   )







       @property
       def 		a_						( self)      ->      Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
                ])







       @property
       def 		a_						( self)      ->      float:
            return 1e-4


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


A   :     str       =				logging.get_logger(__name__)

# TODO: upload to AWS
A   :     Dict       =				{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''retribert'''







  def __init__(							self       :				Optional[int]     ,   __magic_name__       :				Optional[Any]=30_522     ,   __magic_name__       :				int=768     ,   __magic_name__       :				Dict=8     ,   __magic_name__       :				List[Any]=12     ,   __magic_name__       :				Tuple=3_072     ,   __magic_name__       :				List[Any]="gelu"     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Any=0.1     ,   __magic_name__       :				Tuple=512     ,   __magic_name__       :				Dict=2     ,   __magic_name__       :				int=0.02     ,   __magic_name__       :				List[Any]=1e-12     ,   __magic_name__       :				List[str]=True     ,   __magic_name__       :				Dict=128     ,   __magic_name__       :				Union[str, Any]=0     ,   **__magic_name__       :				List[Any]     ,   )     ->		Dict:
        super().__init__(pad_token_id=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     vocab_size
        SCREAMING_SNAKE_CASE_      =     hidden_size
        SCREAMING_SNAKE_CASE_      =     num_hidden_layers
        SCREAMING_SNAKE_CASE_      =     num_attention_heads
        SCREAMING_SNAKE_CASE_      =     hidden_act
        SCREAMING_SNAKE_CASE_      =     intermediate_size
        SCREAMING_SNAKE_CASE_      =     hidden_dropout_prob
        SCREAMING_SNAKE_CASE_      =     attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_      =     max_position_embeddings
        SCREAMING_SNAKE_CASE_      =     type_vocab_size
        SCREAMING_SNAKE_CASE_      =     initializer_range
        SCREAMING_SNAKE_CASE_      =     layer_norm_eps
        SCREAMING_SNAKE_CASE_      =     share_encoders
        SCREAMING_SNAKE_CASE_      =     projection_dim


