



"""simple docstring"""





__lowerCAmelCase       :					int             =					"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"
def 	__lowerCAmelCase						(		__UpperCamelCase      :			bytes    ):


						'''simple docstring'''






						if not isinstance(UpperCAmelCase__					, UpperCAmelCase__    ):
												snake_case_   : Union[str, Any]						=			F'a bytes-like object is required, not \'{data.__class__.__name__}\''
												raise TypeError(UpperCAmelCase__    )

						snake_case_   : Dict						=			"""""".join(bin(UpperCAmelCase__    )[2:].zfill(8    ) for byte in data    )

						snake_case_   : Union[str, Any]						=			len(UpperCAmelCase__    ) % 6 != 0

						if padding_needed:
												# The padding that will be added later
												snake_case_   : List[Any]						=			b"""=""" * ((6 - len(UpperCAmelCase__    ) % 6) // 2)

												# Append binary_stream with arbitrary binary digits (0's by default) to make its
												# length a multiple of 6.
												binary_stream += "0" * (6 - len(UpperCAmelCase__    ) % 6)
						else:
												snake_case_   : Union[str, Any]						=			b""""""

						# Encode every 6 binary digits to their corresponding Base64 character
						return (
						    "".join(
						        B64_CHARSET[int(binary_stream[index : index + 6]					, 2    )]
						        for index in range(0					, len(UpperCAmelCase__    )					, 6    )    ).encode()
						    + padding
						)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str    ):


						'''simple docstring'''






						if not isinstance(UpperCAmelCase__					, UpperCAmelCase__    ) and not isinstance(UpperCAmelCase__					, UpperCAmelCase__    ):
												snake_case_   : List[str]						=			(
												    """argument should be a bytes-like object or ASCII string, """
												    F'not \'{encoded_data.__class__.__name__}\''
												)
												raise TypeError(UpperCAmelCase__    )

						# In case encoded_data is a bytes-like object, make sure it contains only
						# ASCII characters so we convert it to a string object
						if isinstance(UpperCAmelCase__					, UpperCAmelCase__    ):
												try:
																		snake_case_   : Optional[int]						=			encoded_data.decode("""utf-8"""    )
												except UnicodeDecodeError:
																		raise ValueError("""base64 encoded data should only contain ASCII characters"""    )

						snake_case_   : Any						=			encoded_data.count("""="""    )

						# Check if the encoded string contains non base64 characters
						if padding:
												assert all(
												    char in B64_CHARSET for char in encoded_data[:-padding]    ), "Invalid base64 character(s) found."
						else:
												assert all(
												    char in B64_CHARSET for char in encoded_data    ), "Invalid base64 character(s) found."

						# Check the padding
						assert len(UpperCAmelCase__    ) % 4 == 0 and padding < 3, "Incorrect padding"

						if padding:
												# Remove padding if there is one
												snake_case_   : Optional[int]						=			encoded_data[:-padding]

												snake_case_   : Any						=			"""""".join(
												    bin(B64_CHARSET.index(UpperCAmelCase__    )    )[2:].zfill(6    ) for char in encoded_data    )[: -padding * 2]
						else:
												snake_case_   : int						=			"""""".join(
												    bin(B64_CHARSET.index(UpperCAmelCase__    )    )[2:].zfill(6    ) for char in encoded_data    )

						snake_case_   : Optional[int]						=			[
						    int(binary_stream[index : index + 8]					, 2    )
						    for index in range(0					, len(UpperCAmelCase__    )					, 8    )
						]

						return bytes(UpperCAmelCase__    )


if __name__ == "__main__":
					import doctest

					doctest.testmod()






"""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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCAmelCase       :					Tuple      					=					logging.get_logger(__name__)

__lowerCAmelCase       :					Dict      					=					{
    '''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    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__  ,				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       '''swin'''

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





		def __init__(					self							,    _lowercase=2_2_4							,    _lowercase=4							,    _lowercase=3							,    _lowercase=9_6							,    _lowercase=[2, 2, 6, 2]							,    _lowercase=[3, 6, 1_2, 2_4]							,    _lowercase=7							,    _lowercase=4.0							,    _lowercase=True							,    _lowercase=0.0							,    _lowercase=0.0							,    _lowercase=0.1							,    _lowercase="gelu"							,    _lowercase=False							,    _lowercase=0.02							,    _lowercase=1E-5							,    _lowercase=3_2							,    _lowercase=None							,    _lowercase=None							,    **_lowercase							,    ) ->						Union[str, Any]:







								'''simple docstring'''

								super().__init__(**_lowercase   )

								snake_case_   : str						=			image_size
								snake_case_   : int						=			patch_size
								snake_case_   : Optional[int]						=			num_channels
								snake_case_   : Union[str, Any]						=			embed_dim
								snake_case_   : Optional[int]						=			depths
								snake_case_   : Optional[int]						=			len(_lowercase   )
								snake_case_   : Optional[Any]						=			num_heads
								snake_case_   : Optional[Any]						=			window_size
								snake_case_   : Optional[Any]						=			mlp_ratio
								snake_case_   : Optional[Any]						=			qkv_bias
								snake_case_   : Optional[Any]						=			hidden_dropout_prob
								snake_case_   : Tuple						=			attention_probs_dropout_prob
								snake_case_   : Union[str, Any]						=			drop_path_rate
								snake_case_   : List[Any]						=			hidden_act
								snake_case_   : str						=			use_absolute_embeddings
								snake_case_   : str						=			layer_norm_eps
								snake_case_   : Optional[Any]						=			initializer_range
								snake_case_   : Any						=			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
								snake_case_   : Tuple						=			int(embed_dim * 2 ** (len(_lowercase   ) - 1)   )
								snake_case_   : Tuple						=			["""stem"""] + [f'stage{idx}' for idx in range(1							,    len(_lowercase   ) + 1   )]
								snake_case_			,		snake_case_   : Any						=			get_aligned_output_features_output_indices(
								    out_features=_lowercase							,    out_indices=_lowercase							,    stage_names=self.stage_names   )




class    _lowerCAmelCase    (				SCREAMING_SNAKE_CASE__		):





		"""simple docstring"""






		_lowerCamelCase			  =       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

