


'''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


__A	=logging.get_logger(__name__)

__A	={
    '''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 		_snake_case					(     UpperCAmelCase__       ,   UpperCAmelCase__	):
						lowerCAmelCase      :Optional[Any]   =					'''swin'''

						lowerCAmelCase      :Any   =					{
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						}




						def __init__(      self							,							_lowerCamelCase=224							,							_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							,							):
									super().__init__(**__lowerCAmelCase)

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


class 		_snake_case					(     UpperCAmelCase__	):
						lowerCAmelCase      :int   =					version.parse('''1.11'''	)




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




						@property
						def 				snake_case__						(      self):
									return 1e-4



'''simple docstring'''



from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
__A	=numpy.array([0, 0])
__A	=numpy.array([0.5, 0.8_6_6_0_2_5_4])
__A	=numpy.array([1, 0])
__A	=[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Union[str, Any]        =     initial_vectors
			for _ in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Dict        =     iteration_step(UpperCamelCase__					)
			return vectors







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Optional[Any]        =     []
			for i, start_vector in enumerate(vectors[:-1]					):
						UpperCAmelCase__  :					Tuple        =     vectors[i + 1]
						new_vectors.append(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     end_vector - start_vector
						new_vectors.append(start_vector + difference_vector / 3					)
						new_vectors.append(
						    start_vector + difference_vector / 3 + rotate(difference_vector / 3 ,      6_0					)					)
						new_vectors.append(start_vector + difference_vector * 2 / 3					)
			new_vectors.append(vectors[-1]					)
			return new_vectors







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     numpy.radians(UpperCamelCase__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     numpy.cos(UpperCamelCase__					), numpy.sin(UpperCamelCase__					)
			UpperCAmelCase__  :					Dict        =     numpy.array(((c, -s), (s, c))					)
			return numpy.dot(UpperCamelCase__ ,      UpperCamelCase__					)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					Any        =     plt.gca()
			axes.set_aspect("""equal"""					)

			# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
			# y-coordinates as inputs, which are constructed from the vector-list using
			# zip()
			UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     zip(*UpperCamelCase__					)
			plt.plot(UpperCamelCase__ ,      UpperCamelCase__					)
			plt.show()


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		__A	=iterate(INITIAL_VECTORS, 5)
		plot(processed_vectors)