



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    :		Optional[Any]  =logging.get_logger(__name__)

__snake_case    :		List[str]  ={
    '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__      (							lowerCamelCase__				,    lowerCamelCase__):




		'''simple docstring'''



		snake_case_     ="""swin"""

		snake_case_     ={
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(self		,__lowerCamelCase=2_24		,__lowerCamelCase=4		,__lowerCamelCase=3		,__lowerCamelCase=96		,__lowerCamelCase=[2, 2, 6, 2]		,__lowerCamelCase=[3, 6, 12, 24]		,__lowerCamelCase=7		,__lowerCamelCase=4.0		,__lowerCamelCase=True		,__lowerCamelCase=0.0		,__lowerCamelCase=0.0		,__lowerCamelCase=0.1		,__lowerCamelCase="gelu"		,__lowerCamelCase=False		,__lowerCamelCase=0.02		,__lowerCamelCase=1e-5		,__lowerCamelCase=32		,__lowerCamelCase=None		,__lowerCamelCase=None		,**__lowerCamelCase		,)					->      List[str]:

						"""simple docstring"""






						super().__init__(**__lowerCamelCase			)

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



class 	lowerCamelCase__      (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_     =version.parse("""1.11""")



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

						"""simple docstring"""






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



		@property
		def 		lowerCAmelCase__				(self			)					->      float:

						"""simple docstring"""






						return 1e-4






def      lowerCAmelCase__		(   lowerCamelCase_		:		int = 1000000):





				'''simple docstring'''

				lowerCAmelCase__					:    int												=					set(range(3	,lowerCamelCase_	,2))
				primes.add(2)
				for p in range(3	,lowerCamelCase_	,2):
								if p not in primes:
												continue
								primes.difference_update(set(range(p * p	,lowerCamelCase_	,lowerCamelCase_)))

				lowerCAmelCase__					:    int												=					[float(lowerCamelCase_) for n in range(limit + 1)]

				for p in primes:
								for n in range(lowerCamelCase_	,limit + 1	,lowerCamelCase_):
												phi[n] *= 1 - 1 / p

				return int(sum(phi[2:]))


if __name__ == "__main__":
							print(f"""{solution() = }""")

