





"""simple docstring"""
import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowerCamelCase_								=		logging.get_logger(__name__)






class 					_SCREAMING_SNAKE_CASE(   A  ):




       def __init__(						self		,*SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)			->      None:





             """simple docstring"""






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






"""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


lowerCamelCase_								=		logging.get_logger(__name__)

lowerCamelCase_								=		{
    "microsoft/beit-base-patch16-224-pt22k": (
        "https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      str							=  '''beit'''




       def __init__(						self		,SCREAMING_SNAKE_CASE__=81_92		,SCREAMING_SNAKE_CASE__=7_68		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=12		,SCREAMING_SNAKE_CASE__=30_72		,SCREAMING_SNAKE_CASE__="gelu"		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0		,SCREAMING_SNAKE_CASE__=0.0_2		,SCREAMING_SNAKE_CASE__=1E-12		,SCREAMING_SNAKE_CASE__=2_24		,SCREAMING_SNAKE_CASE__=16		,SCREAMING_SNAKE_CASE__=3		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=0.1		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=[3, 5, 7, 11]		,SCREAMING_SNAKE_CASE__=[1, 2, 3, 6]		,SCREAMING_SNAKE_CASE__=True		,SCREAMING_SNAKE_CASE__=0.4		,SCREAMING_SNAKE_CASE__=2_56		,SCREAMING_SNAKE_CASE__=1		,SCREAMING_SNAKE_CASE__=False		,SCREAMING_SNAKE_CASE__=2_55		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(**SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE   :Any						=  vocab_size
             __SCREAMING_SNAKE_CASE   :Dict						=  hidden_size
             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  num_hidden_layers
             __SCREAMING_SNAKE_CASE   :List[str]						=  num_attention_heads
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  intermediate_size
             __SCREAMING_SNAKE_CASE   :Optional[int]						=  hidden_act
             __SCREAMING_SNAKE_CASE   :Tuple						=  hidden_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  initializer_range
             __SCREAMING_SNAKE_CASE   :str						=  layer_norm_eps

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






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      List[Any]							=  version.parse('''1.11'''  )




       @property
       def     _UpperCamelCase     (						self							)			->      Mapping[str, Mapping[int, str]]:





             """simple docstring"""






             return OrderedDict(
                 [
                     ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ]							)




       @property
       def     _UpperCamelCase     (						self							)			->      float:





             """simple docstring"""






             return 1E-4