
'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowercase							:   List[Any]					=     logging.get_logger(__name__)

class 	__lowercase				(  _lowercase      ,     _lowercase    ):
			lowerCamelCase			:		List[str]	      =			"maskformer-swin"

			lowerCamelCase			:		Optional[Any]	      =			{
			    "num_attention_heads": "num_heads",
			    "num_hidden_layers": "num_layers",
			}

			def __init__(self			,   A=2_2_4			,   A=4			,   A=3			,   A=9_6			,   A=[2, 2, 6, 2]			,   A=[3, 6, 1_2, 2_4]			,   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=None			,   A=None			,   **A			,   ):
								super().__init__(**A      )

								lowerCamelCase_					:      List[Any]     =		image_size
								lowerCamelCase_					:      Optional[int]     =		patch_size
								lowerCamelCase_					:      str     =		num_channels
								lowerCamelCase_					:      int     =		embed_dim
								lowerCamelCase_					:      Any     =		depths
								lowerCamelCase_					:      Tuple     =		len(A      )
								lowerCamelCase_					:      int     =		num_heads
								lowerCamelCase_					:      Optional[int]     =		window_size
								lowerCamelCase_					:      Optional[int]     =		mlp_ratio
								lowerCamelCase_					:      Union[str, Any]     =		qkv_bias
								lowerCamelCase_					:      List[Any]     =		hidden_dropout_prob
								lowerCamelCase_					:      str     =		attention_probs_dropout_prob
								lowerCamelCase_					:      str     =		drop_path_rate
								lowerCamelCase_					:      Optional[int]     =		hidden_act
								lowerCamelCase_					:      Dict     =		use_absolute_embeddings
								lowerCamelCase_					:      Dict     =		layer_norm_eps
								lowerCamelCase_					:      Optional[Any]     =		initializer_range
								# 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_					:      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      )




'''simple docstring'''


import os
from math import logaa




def lowercase_	(   _lowercase = "base_exp.txt"				)     ->							int:
					'''simple docstring'''

					lowerCamelCase_					:      float     =		0
					lowerCamelCase_					:      Dict     =		0
					for i, line in enumerate(open(os.path.join(os.path.dirname(_lowercase				)			,       _lowercase				)				)				):
										lowerCamelCase_,      lowerCamelCase_					:      Dict     =		list(map(_lowercase			,       line.split(''','''				)				)				)
										if x * logaa(_lowercase				) > largest:
															lowerCamelCase_					:      List[str]     =		x * logaa(_lowercase				)
															lowerCamelCase_					:      Union[str, Any]     =		i + 1
					return result


if __name__ == "__main__":
					print(solution())


