




def 			lowerCamelCase_		(       UpperCamelCase__				:      Optional[Any] ,     UpperCamelCase__				:      List[Any] ,     UpperCamelCase__				:      Dict      )     ->       Dict:





				"""simple docstring"""


				if n == 0:
								return 1

				elif n % 2 == 1:
								return (binary_exponentiation(_lowerCamelCase ,     n - 1 ,     _lowerCamelCase      ) * a) % mod

				else:
								__lowerCamelCase		       =    binary_exponentiation(_lowerCamelCase ,     n / 2 ,     _lowerCamelCase      )
								return (b * b) % mod


# a prime number
__A        =    7_01

__A        =    10_00_00_00_00
__A        =    10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)


print((a / b) % p == (a * b ** (p - 2)) % p)







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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

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







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

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



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
